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Question 81
Your organization runs analytics workloads on BigQuery with unpredictable query patterns. Some days have heavy usage while others are light. You want to optimize costs while ensuring adequate capacity. What pricing model should you use?
A) Flat-rate pricing with autoscaling slots
B) On-demand pricing with no reservations
C) Flex slots with baseline reservation
D) Flat-rate pricing with maximum slot commitment
Answer: C
Explanation:
This question tests understanding of BigQuery pricing models and choosing appropriate capacity models for workloads with variable demand patterns.
BigQuery offers multiple pricing models balancing cost predictability with flexibility. Workloads with unpredictable patterns need pricing that provides baseline capacity while accommodating spikes without overprovisioning for peak capacity constantly.
Flex slots with baseline reservation provide optimal cost-performance balance for variable workloads.
A) is incorrect because traditional flat-rate pricing doesn’t offer true autoscaling in the sense of paying only for capacity used. Flat-rate requires committing to a specific slot count with annual or monthly commitments. While you can purchase additional capacity when needed, you pay for committed slots whether used or not. For unpredictable workloads with significant variability, flat-rate pricing risks either overprovisioning (wasting money during light periods) or underprovisioning (insufficient capacity during peaks).
B) is incorrect because pure on-demand pricing with no reservations can be expensive for workloads with any consistent baseline usage. On-demand charges per query based on bytes processed, which provides ultimate flexibility but at premium pricing. For organizations with regular analytics workloads, even unpredictable ones, on-demand becomes expensive compared to commitment-based pricing. On-demand works well for sporadic, ad-hoc usage but isn’t cost-optimal when you have ongoing analytics needs.
C) is correct because flex slots with baseline reservation provide the ideal balance for variable workloads. Flex slots offer short-term slot commitments (60-second minimum) that you can scale up and down dynamically. You maintain a baseline reservation covering typical usage at discounted rates compared to on-demand, then temporarily add flex slot capacity during usage spikes, paying only for the duration needed. This model provides cost savings from baseline commitments while accommodating unpredictable peaks without overprovisioning. You pay for baseline capacity and spike capacity only when used.
D) is incorrect because flat-rate pricing with maximum slot commitment optimizes for predictable, consistently heavy workloads, not unpredictable patterns. Committing to maximum capacity needed for peak usage means paying for that capacity 24/7, even during light usage periods. For workloads with significant variability between light and heavy days, this approach wastes substantial money on unused capacity. Maximum commitments make sense when utilization is consistently high, not for variable patterns.
Question 82
You need to migrate a Teradata data warehouse to BigQuery. The warehouse contains 200 TB of data, complex stored procedures, and proprietary SQL extensions. What migration approach should you follow?
A) Use BigQuery Data Transfer Service for automated migration
B) Use BigQuery Migration Service for schema and SQL translation with batch data transfer
C) Manually rewrite all SQL and use gsutil to transfer data
D) Export to CSV files and load directly into BigQuery
Answer: B
Explanation:
This question evaluates understanding of complex data warehouse migration strategies, particularly handling proprietary SQL dialects and large data volumes.
Migrating from legacy data warehouses involves more than data transfer – SQL dialects differ, stored procedures need translation, and schema patterns may not map directly. Migration tools that handle both data and code translation accelerate migrations while reducing errors.
BigQuery Migration Service provides comprehensive migration capabilities including SQL translation and data transfer orchestration.
A) is incorrect because BigQuery Data Transfer Service is designed for importing data from SaaS applications and some data warehouses, but doesn’t provide the comprehensive migration capabilities needed for Teradata. Data Transfer Service focuses on ongoing scheduled data imports, not one-time migrations with SQL translation. It doesn’t handle translating Teradata’s proprietary SQL extensions, stored procedures, or complex schema patterns to BigQuery equivalents. Migration from Teradata requires more comprehensive tooling.
B) is correct because BigQuery Migration Service provides end-to-end migration capabilities specifically for data warehouse migrations like Teradata to BigQuery. The service includes SQL translation that converts Teradata SQL dialects and proprietary extensions to BigQuery standard SQL, schema migration that maps Teradata table structures to appropriate BigQuery designs, stored procedure conversion, and orchestration of bulk data transfer for the 200 TB dataset. This comprehensive approach handles both code and data migration efficiently, reducing manual effort and migration errors compared to manual approaches.
C) is incorrect because manually rewriting SQL and transferring data creates massive effort and risk for a 200 TB data warehouse. Complex stored procedures with proprietary SQL extensions require significant expertise to translate correctly. Manual translation is time-consuming, error-prone, and difficult to validate comprehensively. While gsutil can transfer data, the combination of manual code translation and basic data transfer tools lacks the automation and validation that migration services provide. Manual migration approaches should be last resorts when tooling doesn’t support the source system.
D) is overly simplistic and ignores the SQL and stored procedure translation needs. While exporting to CSV and loading into BigQuery handles data transfer, it doesn’t address translating complex SQL, stored procedures, or adapting schema patterns. CSV export also loses data type precision for some Teradata types, requires significant storage for 200 TB of CSV files, and is slower than binary transfer methods. This approach addresses only the data transfer portion of migration, ignoring the substantial code migration effort.
Question 83
Your streaming pipeline needs to maintain state for each user tracking their activity over rolling 7-day windows. The state must persist across pipeline restarts and handle millions of active users. What Dataflow feature should you use?
A) Side inputs for state management
B) Stateful processing with ValueState or BagState
C) Global windows with persistent triggers
D) External state storage in Cloud Bigtable
Answer: B
Explanation:
This question tests understanding of stateful stream processing in Dataflow, particularly managing per-key state that persists across pipeline lifecycle events.
Streaming applications often need to maintain state associated with entities (users, sessions, devices) across extended time periods. The state management solution must handle large state sizes, persist across failures and restarts, and scale to millions of entities.
Dataflow’s stateful processing with managed state provides durable, scalable per-key state management.
A) is incorrect because side inputs are designed for broadcasting reference data to all workers, not maintaining per-key state. Side inputs distribute the same data (like lookup tables) to all workers for enrichment purposes. They don’t provide mechanisms for maintaining separate state per user that updates as events are processed. Side inputs are reloaded periodically with fresh reference data but don’t track evolving per-key state over time.
B) is correct because Dataflow’s stateful processing with ValueState or BagState provides exactly the capabilities needed. Stateful DoFns can declare state variables (ValueState for single values, BagState for collections) associated with each key (user ID). As events arrive for a user, the DoFn reads current state, updates it based on the event, and writes back updated state. Dataflow automatically manages state persistence to durable storage, ensuring state survives pipeline restarts and worker failures. State is automatically partitioned across workers for scalability, handling millions of users efficiently. This is the standard Apache Beam pattern for per-key state management.
C) is incorrect because global windows don’t partition data by key and don’t provide per-user state management. Global windows treat all data as one unbounded collection without temporal partitioning. While you could theoretically maintain state through complex custom triggers and aggregations in global windows, this would require manually implementing what stateful processing provides built-in. Triggers control when window results are emitted but don’t provide the per-key state management primitives needed for this use case.
D) is incorrect because while external state in Bigtable is technically viable, it adds unnecessary complexity compared to Dataflow’s managed state. Storing state in Bigtable requires Dataflow to make network calls for every state read and write, adding latency and operational complexity. You’d need to manage Bigtable schemas, handle consistency, and coordinate state management manually. Dataflow’s managed state provides better performance through local state access with asynchronous persistence, automatic state lifecycle management, and simpler programming model. External state stores make sense for state sharing across different systems, not for state within a single Dataflow pipeline.
Question 84
You are building a data lake that stores raw data from various sources. Different teams need to access data with different transformations applied. You want to avoid creating multiple copies of data. What architecture pattern should you implement?
A) Store raw data and create multiple transformed copies for each team
B) Store raw data and use BigQuery views to provide transformed access
C) Use Dataflow to transform data differently for each team in real-time
D) Store data in multiple formats optimized for each team
Answer: B
Explanation:
This question assesses understanding of data lake architecture patterns that balance storage efficiency with access flexibility.
Data lakes serving multiple teams often face the challenge that different teams need different views or transformations of the same data. Creating physical copies for each team wastes storage and creates synchronization problems. Virtual transformation layers provide customized access without duplication.
BigQuery views over raw data provide transformation virtualization without data duplication.
A) is incorrect because creating multiple transformed copies violates the principle of single source of truth and creates significant operational overhead. Multiple copies consume substantial storage for large datasets, require synchronization mechanisms to keep copies updated when source data changes, create consistency risks where copies drift out of sync, and complicate governance with unclear data lineage. Physical copies should be avoided when virtual transformation layers can provide the same functionality.
B) is correct because BigQuery views provide SQL-based transformations over raw data without creating physical copies. Each team can have customized views applying their required transformations – filtering rows, computing derived columns, aggregating data, or joining with other datasets. Views query the underlying raw data on-demand, ensuring teams always see current data without synchronization delays. This approach maintains a single copy of raw data, provides unlimited customized access patterns through views, and simplifies governance with clear lineage from raw to transformed views.
C) is incorrect because real-time Dataflow transformations for each team creates substantial operational complexity and cost. You would need separate Dataflow pipelines for each team applying their transformations, consuming compute resources continuously, duplicating processing logic, and creating operational overhead managing multiple pipelines. Dataflow is excellent for ETL pipelines that create transformed datasets, but using it for on-demand transformation serving is inefficient compared to view-based virtual transformation in BigQuery.
D) is incorrect because storing data in multiple formats creates the same problems as multiple transformed copies – storage waste, synchronization complexity, and governance challenges. Different formats (Parquet, Avro, CSV) serve different access patterns, but the question asks about different transformations, not formats. Even for format optimization, modern query engines like BigQuery handle various formats efficiently, making format-specific copies unnecessary. Multiple formats multiply storage costs and operational complexity without significant benefits.
Question 85
Your machine learning pipeline trains models on sensitive customer data. You need to ensure training data cannot be used to infer individual customer information while maintaining model accuracy. What technique should you apply?
A) Remove customer identifiers before training
B) Use differential privacy during model training
C) Encrypt training data with customer-managed keys
D) Aggregate data to prevent individual-level analysis
Answer: B
Explanation:
This question tests understanding of privacy-preserving machine learning techniques that protect individual privacy while enabling useful model training.
Training ML models on sensitive data creates privacy risks where model parameters or predictions might leak information about training data. Simply removing identifiers isn’t sufficient as models can still memorize and reveal sensitive patterns. Privacy-preserving techniques provide mathematical guarantees about information leakage.
Differential privacy provides provable privacy guarantees while allowing useful model training on sensitive data.
A) is insufficient because removing identifiers (like names, IDs) doesn’t prevent privacy leakage through model predictions. Even with identifiers removed, models trained on sensitive features can memorize training examples and reveal information through predictions or membership inference attacks. Someone could query the model with various inputs to infer whether specific individuals were in the training data. De-identification is a good practice but doesn’t provide the strong privacy guarantees needed for sensitive data.
B) is correct because differential privacy provides mathematical guarantees that individual training examples don’t significantly influence model outputs, preventing inference of individual information. Differential privacy adds carefully calibrated noise during training, ensuring that including or excluding any individual’s data doesn’t meaningfully change the model. This provides provable privacy bounds – attackers can’t determine whether specific individuals were in the training data, even with full model access. Modern differentially private ML techniques maintain good model accuracy while providing strong privacy guarantees, making them the gold standard for privacy-preserving ML.
C) is incorrect because encryption protects data at rest and in transit but doesn’t prevent privacy leakage through model outputs. Encrypting training data with CMEK ensures unauthorized parties can’t read stored data, which is important for compliance. However, during training, data must be decrypted for computation. The trained model itself can leak information about training data through predictions, regardless of whether the original data was encrypted. Encryption is essential for data protection but doesn’t address the model-based privacy inference risks.
D) is partially helpful but may sacrifice too much model utility. Aggregating data to group level (e.g., city instead of address) reduces individual privacy risk but loses granular patterns that models need. Heavy aggregation prevents individual-level inference but may degrade model accuracy significantly. The right aggregation level is difficult to determine – too little leaves privacy risks, too much hurts utility. Differential privacy provides better privacy-utility tradeoffs with mathematical guarantees, making it preferable to ad-hoc aggregation approaches.
Question 86
You need to implement a data quality monitoring system that tracks data quality metrics over time, alerts on degradation, and integrates with your existing BigQuery data warehouse. What Google Cloud service provides these capabilities?
A) Cloud Monitoring with custom metrics
B) Dataplex data quality with automated monitoring
C) BigQuery scheduled queries computing quality metrics
D) Custom dashboards in Looker Studio
Answer: B
Explanation:
This question evaluates understanding of data quality monitoring solutions on Google Cloud, particularly managed services designed specifically for data quality use cases.
Data quality monitoring requires defining quality rules, executing them regularly against datasets, tracking quality metrics over time, detecting anomalies or degradation, and alerting stakeholders. Purpose-built data quality platforms provide these capabilities integrated with data systems.
Dataplex data quality provides comprehensive, managed data quality monitoring integrated with BigQuery.
A) is incorrect because Cloud Monitoring focuses on infrastructure and application monitoring, not data quality. While you could create custom metrics for data quality and send them to Cloud Monitoring, this requires building the quality rule engine, execution framework, metric computation, and integration logic yourself. Cloud Monitoring would handle alerting and visualization, but you’d need to implement all data quality checking logic custom. Purpose-built data quality services provide these capabilities integrated.
B) is correct because Dataplex data quality offers comprehensive data quality monitoring designed for this use case. Dataplex allows defining quality rules declaratively (completeness checks, uniqueness constraints, value range validation, custom SQL rules) that execute automatically against BigQuery tables. It tracks quality scores over time, detects quality degradation through anomaly detection, integrates with Cloud Monitoring for alerting, and provides built-in dashboards visualizing quality trends. This managed service eliminates the need to build custom quality monitoring infrastructure.
C) is incorrect because while scheduled queries can compute quality metrics, they don’t provide the complete quality monitoring framework. You would need to write SQL computing quality checks, schedule query execution, store results in tables, build alerting logic detecting degradation, and create dashboards visualizing trends. This requires substantial custom development to replicate what data quality platforms provide. Scheduled queries are a building block but not a complete quality monitoring solution.
D) is incorrect because Looker Studio provides visualization but not data quality rule execution or monitoring. Looker Studio can display quality metrics through dashboards, but you still need infrastructure computing those metrics, executing quality rules, detecting anomalies, and storing historical trends. Looker Studio is the presentation layer, not the quality monitoring engine. A complete solution needs both execution/monitoring capabilities and visualization.
Question 87
Your organization processes payment transactions and must ensure exactly-once processing with no duplicates or lost transactions. The pipeline reads from Pub/Sub and writes to Cloud Spanner. What configuration ensures these guarantees?
A) Use Pub/Sub with at-least-once delivery and application-level deduplication
B) Use Dataflow exactly-once mode with Pub/Sub and transactional Spanner writes
C) Use Pub/Sub with message ordering and idempotent Spanner operations
D) Implement distributed locks to prevent duplicate processing
Answer: B
Explanation:
This question tests understanding of end-to-end exactly-once processing guarantees in distributed streaming systems, particularly for financial transactions requiring strict consistency.
Payment processing requires the strongest consistency guarantees – each transaction must be processed exactly once with no duplicates or losses. Achieving exactly-once semantics requires coordination between message queuing, stream processing, and database systems.
Dataflow’s exactly-once mode with transactional sinks provides end-to-end exactly-once guarantees.
A) is incorrect because relying on application-level deduplication doesn’t provide true exactly-once guarantees. At-least-once delivery means Pub/Sub may deliver messages multiple times, requiring applications to detect and skip duplicates. Application-level deduplication requires maintaining state about previously processed messages, handling state consistency across failures, and managing state expiration. This approach is complex, error-prone, and doesn’t provide the transactional guarantees that financial systems require. It’s weaker than platform-level exactly-once guarantees.
B) is correct because this combination provides end-to-end exactly-once processing with transactional guarantees. Dataflow’s exactly-once mode ensures each Pub/Sub message is processed exactly once, even with worker failures or restarts, through checkpointing and transactional state management. Cloud Spanner supports ACID transactions that Dataflow integrates with, ensuring writes are atomic and occur exactly once. Dataflow coordinates with both Pub/Sub (acknowledging messages only after successful processing) and Spanner (using transactions for writes) to provide complete exactly-once guarantees from ingestion through storage.
C) is partially correct but doesn’t provide complete exactly-once guarantees. Message ordering in Pub/Sub ensures ordered delivery but doesn’t prevent duplicate delivery. Idempotent Spanner operations (operations that produce the same result when executed multiple times) help handle duplicates gracefully but don’t prevent them. This approach mitigates duplicate effects but doesn’t provide the strong consistency guarantees that exactly-once processing delivers. For financial transactions, prevention is preferable to mitigation.
D) is incorrect because distributed locks are a low-level primitive that doesn’t solve the complete exactly-once problem. While locks can prevent concurrent processing of the same message, implementing correct distributed locking is complex and error-prone. Locks also create performance bottlenecks and don’t address message acknowledgment coordination with Pub/Sub or transactional writes to Spanner. Using platform-level exactly-once features is far simpler and more reliable than building custom coordination with distributed locks.
Question 88
You need to analyze clickstream data to build user behavior models. The analysis requires joining 30 days of clickstream data (5 TB) with user profile data (100 GB) multiple times with different transformations. What optimization strategy should you use?
A) Load all data into BigQuery and run joins repeatedly
B) Use BigQuery with clustering on user_id and results caching
C) Export data to Dataproc for Spark-based analysis
D) Create denormalized tables combining clickstream and profiles
Answer: B
Explanation:
This question assesses understanding of BigQuery optimization strategies for iterative analytics involving repeated queries with variations.
Iterative data analysis involves running similar queries repeatedly with different filters, aggregations, or transformations. Optimization should minimize redundant computation while supporting query variation flexibility.
BigQuery’s combination of clustering and results caching provides optimal performance for iterative analytics.
A) is correct functionally but doesn’t apply optimizations. Loading data into BigQuery enables SQL-based analysis which is appropriate for joins and transformations. However, running joins repeatedly without optimization wastes computation scanning the same data. Each query would scan both clickstream and profile data completely. While BigQuery is fast, repeated full scans of 5 TB are expensive and slower than leveraging caching and clustering optimizations.
B) is correct because this combination optimizes repeated analysis effectively. Clustering by user_id organizes both clickstream and profile data so user records are collocated, dramatically improving join performance by reducing data scanning. Results caching automatically reuses query results when queries are identical or similar, eliminating redundant computation. For iterative analysis where you run similar queries with minor variations, results caching serves cached results for unchanged portions while recomputing only changed logic. This combination provides maximum performance for exploratory data analysis patterns.
C) is incorrect unless you have specific requirements BigQuery doesn’t meet. Dataproc with Spark can perform these analyses but requires cluster management, exporting data from BigQuery, and Spark expertise. For SQL-style analytics on structured data, BigQuery’s managed service is simpler and likely performs comparably or better, especially with clustering and caching. Dataproc makes sense for complex ML pipelines, graph processing, or Spark ecosystem tools, not general iterative SQL analytics.
D) is incorrect because denormalization creates significant storage overhead and maintenance complexity. Combining 5 TB of clickstream data with profile information would create a massive denormalized table with profile data duplicated across millions of clickstream records. Updates to profiles would require updating millions of rows. Denormalization optimizes specific query patterns but loses flexibility for varied analysis. With BigQuery’s efficient joins, especially on clustered tables, denormalization’s downsides outweigh benefits for iterative analysis requiring flexibility.
Question 89
Your data pipeline must process files in strict order based on filename sequences. Files must be processed completely before the next file begins. Processing takes several minutes per file. What architecture ensures sequential processing?
A) Use Cloud Functions triggered by Cloud Storage with concurrency limit of 1
B) Use Pub/Sub with ordered delivery and Dataflow sequential processing
C) Use Cloud Composer with sequential task dependencies in DAG
D) Use Cloud Scheduler triggering Cloud Run jobs sequentially
Answer: C
Explanation:
This question tests understanding of orchestrating sequential data processing workflows where order and completion guarantees are critical.
Some processing scenarios require strict sequential execution where each step must complete fully before the next begins. The orchestration solution must enforce ordering, wait for completion confirmation, and handle failures appropriately.
Cloud Composer with DAG task dependencies provides explicit sequential workflow orchestration.
A) is partially correct but has limitations. Setting Cloud Functions concurrency to 1 prevents concurrent execution, ensuring only one file processes at a time. However, this approach doesn’t guarantee processing order based on filename sequences – if files arrive out of order to Cloud Storage, they trigger functions in arrival order, not filename order. Cloud Functions also has execution time limits (9 minutes for 2nd gen) that may not accommodate processing taking “several minutes” as files grow. Sequential processing through concurrency limits is crude compared to explicit orchestration.
B) is incorrect because Pub/Sub ordered delivery with Dataflow doesn’t naturally map to file processing workflows. Pub/Sub ordered delivery ensures messages with the same ordering key are delivered in order, but that’s for streaming messages, not file processing orchestration. Dataflow processes streams continuously and in parallel; enforcing strict sequential file processing where each file completes before starting the next contradicts Dataflow’s parallel processing model. This combination doesn’t provide the sequential file processing workflow needed.
C) is correct because Cloud Composer with Apache Airflow DAGs explicitly models sequential workflows. You can create a DAG where tasks represent processing each file, with task dependencies ensuring files process in sequence – task 2 depends on task 1 completing, task 3 on task 2, etc. Composer waits for each task to complete successfully before triggering dependent tasks, providing the sequential execution guarantee needed. Airflow also handles failure scenarios with retries, monitoring, and alerting. This explicit workflow orchestration is ideal for sequential processing requirements.
D) is incorrect because Cloud Scheduler triggers on time schedules, not file completion events. Scheduler can trigger Cloud Run jobs sequentially by scheduling them at intervals, but this requires estimating processing duration and scheduling jobs far enough apart. If processing takes longer than expected, jobs overlap breaking sequentiality. If you schedule conservatively, gaps waste time waiting unnecessarily. Event-driven orchestration triggered by task completion (like Composer) is superior to time-based scheduling for sequential workflows.
Question 90
You are implementing a feature store for ML that needs to serve features with sub-10ms latency for online prediction while also supporting batch feature retrieval for training. What architecture should you use?
A) Store features in BigQuery for both online and offline access
B) Use Vertex AI Feature Store with online and offline serving
C) Store features in Cloud Bigtable for online serving and BigQuery for offline
D) Use Memorystore for online serving and Cloud Storage for offline
Answer: B
Explanation:
This question evaluates understanding of feature store architecture requirements for ML systems with both online and offline serving needs.
ML feature stores must support two distinct access patterns: online serving requiring sub-10ms latency for individual entity lookups during prediction, and offline serving requiring efficient batch access to millions of feature values for training. The solution should optimize for both patterns while maintaining feature consistency.
Vertex AI Feature Store is purpose-built for exactly this dual serving requirement.
A) is incorrect because BigQuery is not optimized for sub-10ms online serving latency. BigQuery excels at analytical queries returning results in seconds but isn’t designed for operational lookups with millisecond latency. While BigQuery handles offline batch feature retrieval excellently, it can’t meet online serving latency requirements. Using BigQuery for both patterns creates a compromise where online serving performance is inadequate.
B) is correct because Vertex AI Feature Store provides purpose-built online and offline serving optimized for each use case. Online serving uses a low-latency distributed key-value store providing sub-10ms feature lookups for individual entities during real-time prediction. Offline serving exports features in batch to BigQuery or Cloud Storage for training data preparation, optimizing for throughput over millions of entities. Feature Store maintains consistency between serving paths, provides versioning, and integrates with Vertex AI training and prediction. This unified feature store with dual serving modes addresses all requirements.
C) is a technically viable architecture but adds operational complexity compared to managed Feature Store. Bigtable provides sub-10ms latency for online serving, and BigQuery handles batch offline access well. However, this approach requires managing two separate systems, implementing dual-write logic to keep features synchronized between Bigtable and BigQuery, handling consistency challenges, and building feature versioning and management yourself. While functional, this DIY approach requires more effort than using Feature Store’s integrated online/offline capabilities.
D) is incorrect because Memorystore (managed Redis/Memcached) and Cloud Storage aren’t optimized for feature store use cases. While Memorystore provides sub-millisecond latency, it’s a cache, not a primary data store, requiring additional backing storage and cache management logic. Cloud Storage provides object storage, not efficient batch feature retrieval with schema support. This combination lacks the feature-specific capabilities like versioning, point-in-time retrieval, and feature metadata that feature stores provide.
Question 91
Your streaming pipeline processes events from IoT devices. Some devices send bursts of events followed by long idle periods. You want to compute metrics over each device’s active periods. What windowing strategy should you use?
A) Fixed windows of 5 minutes
B) Sliding windows of 10 minutes
C) Session windows with appropriate gap duration
D) Global windows with periodic triggers
Answer: C
Explanation:
This question tests understanding of window types in stream processing for activity-based grouping with variable duration activity patterns.
IoT devices with bursty activity patterns create variable-length active periods separated by idle gaps. The windowing strategy should adapt to actual activity patterns rather than imposing fixed time boundaries, grouping events within active periods while separating distinct activity bursts.
Session windows group events based on activity gaps, perfect for bursty patterns with idle periods.
A) is incorrect because fixed windows impose arbitrary time boundaries that don’t align with device activity patterns. A device active from 10:03 to 10:12 would have activity split across three 5-minute windows (10:00-10:05, 10:05-10:10, 10:10-10:15) instead of being grouped as one activity period. Fixed windows also group unrelated events that happen to fall in the same window. Fixed windows work well for periodic aggregations like “events per hour” but not for activity-based metrics.
B) is incorrect because sliding windows create overlapping time-based windows that don’t represent distinct activity periods. Sliding windows would group events within rolling time ranges regardless of idle gaps, potentially combining multiple separate activity bursts into overlapping windows. A single activity burst would also appear in multiple overlapping windows, creating duplicated metrics. Sliding windows serve different use cases like moving averages, not activity period identification.
C) is correct because session windows are designed specifically for grouping events into activity sessions separated by inactivity gaps. You configure a gap duration (e.g., 10 minutes) representing how long inactivity must last before considering an activity period complete. When a device sends events within the gap duration, they extend the session. After gap-duration inactivity, the session closes. The next event starts a new session. This creates variable-length windows matching actual device activity patterns – short bursts create short sessions, extended activity creates long sessions, perfectly matching the bursty IoT device behavior described.
D) is incorrect because global windows don’t partition data temporally, treating all events as one unbounded window. Global windows would group all events from a device across all time together, not separating distinct activity periods. While triggers can emit periodic results from global windows, they don’t create the activity-based segmentation needed to identify and analyze separate burst periods. Session windows provide the temporal partitioning required for this use case.
Question 92
You need to implement GDPR right-to-access requests where customers can request all personal data your organization stores about them across multiple systems. What architecture provides efficient customer data retrieval?
A) Query each system independently and manually aggregate results
B) Use Data Catalog to discover personal data locations and automated retrieval
C) Replicate all data to BigQuery for centralized querying
D) Implement custom APIs in each application for data export
Answer: B
Explanation:
This question assesses understanding of implementing data subject rights under GDPR, particularly efficient discovery and retrieval of personal data across distributed systems.
GDPR right-to-access requires organizations to provide individuals with all personal data held about them, potentially across many systems. The solution should efficiently discover where personal data exists, retrieve it from multiple sources, and compile comprehensive responses.
Data Catalog with metadata tagging enables discovery of personal data locations with automated retrieval workflows.
A) is incorrect because manual querying and aggregation doesn’t scale and is error-prone. For large organizations with personal data across dozens or hundreds of systems, manually identifying relevant systems, writing queries for each, and aggregating results is time-consuming and risks missing data locations. GDPR requires responses within one month, and manual processes struggle to meet this consistently. Manual approaches also lack auditability proving complete data retrieval.
B) is correct because Data Catalog enables systematic personal data discovery and retrieval. You tag tables and columns containing personal data with policy tags identifying them as containing PII. When right-to-access requests arrive, automated workflows query Data Catalog to discover all data assets containing personal data, retrieve customer data from each tagged location using customer identifiers, and compile comprehensive exports. This systematic approach ensures completeness, provides audit trails proving all known personal data locations were checked, and scales efficiently across many systems and requests.
C) is incorrect because replicating all data to BigQuery solely for access requests creates massive operational overhead and cost. Many systems’ data would need continuous replication to BigQuery, consuming significant resources. The replicated data requires storage and maintenance. Many compliance scenarios require retrieving data from systems of record, not replicas. While centralizing in BigQuery simplifies querying, the replication overhead and cost make this impractical as a general solution for access requests.
D) is partially correct but requires significant development effort across all applications. Implementing custom export APIs in each system provides programmatic data access but requires coordinating development across many teams, maintaining APIs as systems evolve, and building orchestration retrieving from all APIs for each request. This distributed API approach is viable but more complex than metadata-driven discovery approaches. Without Data Catalog-style discovery, you must manually maintain lists of systems containing personal data, risking missing new systems.
Question 93
You are designing a data pipeline on Google Cloud Platform that ingests streaming data from IoT devices. Which managed service is best suited to reliably ingest and buffer this streaming data before further processing?
A) BigQuery
B) Cloud Pub/Sub
C) Cloud SQL
D) Cloud Storage
Answer: B
Explanation:
Ingesting streaming data from IoT devices requires a service that can handle high-throughput, low-latency, and reliable message delivery. Cloud Pub/Sub is a fully managed messaging service that allows you to decouple data producers (IoT devices) from data consumers (processing systems). It supports at-least-once delivery and can buffer messages for subscribers that are temporarily offline. Unlike BigQuery, which is optimized for analytics queries, or Cloud SQL, which is relational and not designed for high-velocity streaming ingestion, Cloud Pub/Sub provides scalable event ingestion, topic and subscription management, and integration with downstream processing systems such as Dataflow. Using Cloud Storage for direct streaming ingestion is not ideal because it lacks message queuing capabilities, and it would require additional orchestration to manage incoming streams and retries. Leveraging Pub/Sub allows you to implement real-time analytics pipelines, event-driven architectures, and ensures reliable delivery and scalability.
Cloud Pub/Sub also integrates seamlessly with Dataflow for stream processing, BigQuery for analytics storage, and Cloud Functions for event-driven serverless computing. It ensures that even if the consumer service is down, messages are retained for a configurable period, ensuring data integrity and reliability. For IoT pipelines where latency, throughput, and reliability are critical, Pub/Sub provides the ideal solution, with automatic scaling and durable storage, eliminating the need for manual management of brokers or message queues. Choosing Cloud Pub/Sub aligns with best practices for cloud-native streaming architectures, allowing engineers to focus on processing logic instead of operational overhead.
Question 94
You need to store large volumes of structured data for analytics in Google Cloud and want to run SQL queries efficiently. Which storage option provides the best performance and scalability?
A) Cloud Bigtable
B) BigQuery
C) Cloud Spanner
D) Cloud Datastore
Answer: B
Explanation:
When dealing with large-scale structured data intended for analytics and querying with SQL, BigQuery is the most suitable option. It is a serverless, highly scalable data warehouse that allows you to store petabytes of data while executing fast, ad-hoc SQL queries. Unlike Cloud Bigtable, which is optimized for NoSQL key-value or wide-column workloads, BigQuery provides columnar storage and massively parallel query execution for analytical queries. Cloud Spanner is a globally distributed relational database designed for transactional workloads, not for large-scale analytics, and Cloud Datastore is optimized for NoSQL application storage, not analytical processing.
BigQuery abstracts infrastructure management, automatically scaling storage and compute resources to match query demands. Its separation of storage and compute ensures cost-efficiency, allowing storage to scale independently of query capacity. Additionally, BigQuery supports nested and repeated fields, enabling highly structured data to be stored without denormalization. For analytics, BigQuery provides advanced features such as partitioned tables, clustering, materialized views, and BI Engine integration for sub-second interactive queries. This makes it ideal for scenarios where engineers need fast, SQL-based insights over massive datasets without worrying about infrastructure provisioning or performance tuning.
Question 95
You are designing a real-time ETL pipeline using Dataflow. Which windowing strategy should you use to aggregate events arriving at slightly different times but belong to the same time interval?
A) Global Window
B) Fixed Windows
C) Sliding Windows
D) Session Windows
Answer: C
Explanation:
In streaming pipelines, windowing allows you to group events based on time intervals for aggregation. Sliding windows are ideal when you need to handle events that arrive at slightly different times but belong to overlapping intervals. Sliding windows create overlapping time slices that allow fine-grained aggregation, ensuring late-arriving events are included in relevant computations. Fixed windows divide the timeline into non-overlapping intervals, which can result in dropped data if events arrive late. Global windows aggregate all data into a single unbounded window, unsuitable for real-time aggregations. Session windows group events based on activity gaps, making them more suited for user activity tracking rather than structured time-based intervals.
Sliding windows are particularly useful when building real-time dashboards, monitoring metrics, or anomaly detection, where events may arrive slightly late due to network latency or IoT device batching. Dataflow handles watermarks and triggers, which determine when results for each window are emitted, accommodating late data arrivals without compromising accuracy. By combining sliding windows with triggers, engineers can balance low latency and accurate aggregation in streaming ETL pipelines, maintaining consistency and completeness of analytics. This approach is widely considered a best practice in cloud-native streaming data processing.
Question 96
You want to perform predictive analytics on a dataset stored in BigQuery using machine learning without exporting data to another environment. Which feature allows you to build and deploy ML models directly in BigQuery?
A) BigQuery ML
B) TensorFlow on AI Platform
C) AutoML Tables
D) Cloud Dataproc
Answer: A
Explanation:
BigQuery ML (BQML) allows data engineers and analysts to build and train machine learning models directly inside BigQuery using standard SQL syntax. This eliminates the need to export data to separate ML environments, reducing data movement, latency, and operational complexity. BQML supports linear regression, logistic regression, k-means clustering, deep neural networks, and time-series models, providing broad predictive capabilities for structured data stored in BigQuery. TensorFlow on AI Platform or AutoML Tables would require exporting data and setting up separate pipelines, adding complexity and potential errors in data handling. Cloud Dataproc is primarily a managed Spark/Hadoop environment for batch or streaming data processing, not specifically for in-database machine learning.
Using BQML, you can train models using SQL commands like CREATE MODEL, evaluate with ML.EVALUATE, and make predictions with ML.PREDICT. It integrates seamlessly with BI tools, dashboards, and analytics pipelines, allowing predictive analytics in real-time. Additionally, BQML supports hyperparameter tuning, model export, and versioning, making it suitable for iterative ML workflows. Storing models within BigQuery ensures security, access control, and auditability, leveraging existing IAM policies and minimizing operational overhead. Engineers can therefore quickly implement predictive analytics while maintaining scalability and governance, which aligns with cloud-native best practices for machine learning workflows on Google Cloud.
Question 97
You need to migrate an on-premises relational database to Cloud Spanner to achieve horizontal scaling and global consistency. Which factor is critical to consider during schema migration?
A) Using wide tables with nested dat
B) Identifying primary keys for all tables
C) Converting all data types to JSON
D) Migrating all stored procedures first
Answer: B
Explanation:
Cloud Spanner is a horizontally scalable, globally distributed relational database designed for transactional workloads with strong consistency. One critical requirement for Cloud Spanner schema design is that every table must have a primary key. This primary key determines how data is sharded across nodes, affecting performance, query efficiency, and scalability. Without well-defined primary keys, Spanner cannot distribute data effectively, potentially creating hotspots and limiting throughput. Using wide tables with nested data is not suitable, as Spanner is relational and does not natively support nested or document-style structures. Converting data to JSON is unnecessary unless specific semi-structured fields exist. Migrating stored procedures is not relevant for schema migration, as Spanner does not support traditional RDBMS stored procedures.
When planning migration, engineers should carefully map existing primary keys and possibly adjust them to fit Spanner’s architecture. Effective primary key design ensures uniform data distribution, optimal read/write performance, and supports global transactions with low latency. Additionally, understanding data access patterns and relationships helps prevent hotspots. Choosing the correct primary key is therefore a foundational step in ensuring successful migration and leveraging Spanner’s horizontal scalability and strong consistency guarantees. Proper schema design aligns with cloud-native principles, ensuring that the database can grow elastically while maintaining performance and reliability.
Question 98
You have a BigQuery dataset that is queried frequently by multiple users. To reduce query costs and improve performance, which feature should you use to store precomputed results efficiently?
A) Partitioned Tables
B) Materialized Views
C) External Tables
D) Streaming Inserts
Answer: B
Explanation:
When a BigQuery dataset is queried repeatedly, materialized views provide a powerful mechanism to precompute and store query results efficiently. Unlike standard views, which recompute results on every query execution, materialized views store the computed data physically, allowing queries to access it directly. This reduces compute resources, lowers query costs, and improves performance for repeated analytical workloads.
Partitioned tables can optimize query performance by dividing data based on a column (like a date), but they do not precompute results. They reduce the amount of scanned data but still require computation each time a query runs. External tables, which reference data stored outside BigQuery (like Cloud Storage or Google Sheets), allow queries without loading data into BigQuery, but queries are slower and do not improve repeated query performance. Streaming inserts are used for ingesting real-time data but have no impact on precomputing results.
Materialized views also support incremental updates, which means that only the changed portions of the dataset are recomputed, rather than recalculating the entire dataset. This is particularly important for high-volume datasets or real-time dashboards where latency and cost efficiency are critical. They integrate seamlessly with BI tools and scheduled queries, ensuring that data is always fresh and analytics are responsive. By using materialized views, engineers can implement optimized analytics pipelines and adhere to cost-efficient cloud best practices. This approach aligns with Google Cloud recommendations for large-scale, query-intensive environments, balancing performance, cost, and real-time access to insights.
Question 99
Your team is designing a machine learning pipeline on Google Cloud. Data preprocessing, training, and evaluation must happen in an automated, reproducible, and scalable manner. Which service is most suitable for orchestrating these steps?
A) Cloud Dataflow
B) AI Platform Pipelines
C) Cloud Composer
D) Cloud Functions
Answer: B
Explanation:
For building a reproducible and automated ML pipeline that handles preprocessing, training, and evaluation, AI Platform Pipelines (built on Kubeflow Pipelines) is the most appropriate service. It allows engineers to define end-to-end ML workflows as a series of steps with dependencies, ensuring that the process is repeatable, auditable, and scalable. Each step can leverage cloud resources for parallel execution and can be easily monitored, versioned, and retried if failures occur.
Cloud Dataflow is primarily designed for data transformation and stream or batch processing; it is not a full ML orchestration solution. Cloud Composer, based on Apache Airflow, is a general-purpose workflow orchestrator suitable for ETL pipelines, but it lacks native ML-focused capabilities like artifact tracking, model evaluation, and integration with AI Platform training services. Cloud Functions are serverless functions triggered by events, suitable for lightweight tasks but not complex ML pipelines.
AI Platform Pipelines integrates tightly with BigQuery, Cloud Storage, AI Platform Training, and Vertex AI endpoints, allowing engineers to automate data ingestion, feature engineering, model training, and deployment. The platform supports pipeline versioning, experiment tracking, and reproducibility, which are critical for enterprise ML operations. By using AI Platform Pipelines, teams can implement MLOps best practices, reduce human error, and ensure consistent model performance. It also enables scalable model training, as pipelines can dynamically provision GPU/TPU resources as needed. This service embodies the modern approach to machine learning orchestration on Google Cloud, streamlining workflows and ensuring that machine learning solutions are reliable, scalable, and maintainable over time.
Question 100
You are tasked with designing a real-time anomaly detection system for IoT sensor data. Which combination of Google Cloud services would provide scalable ingestion, stream processing, and low-latency alerting?
A) Cloud Storage + BigQuery + Cloud Functions
B) Cloud Pub/Sub + Dataflow + Cloud Functions
C) Cloud SQL + Dataflow + AI Platform
D) Bigtable + BigQuery + Cloud Composer
Answer: B
Explanation:
Real-time anomaly detection for IoT data requires a scalable pipeline capable of ingesting high-volume streaming data, processing it in real-time, and triggering alerts with minimal latency. The combination of Cloud Pub/Sub, Dataflow, and Cloud Functions is ideal for this scenario.
Cloud Pub/Sub acts as a durable, high-throughput messaging service, ingesting data from IoT devices in real time. It decouples producers (sensors) from consumers (processing systems), ensuring reliability and scalability even under fluctuating load. Dataflow is used for stream processing, allowing engineers to implement transformations, aggregations, and anomaly detection logic on-the-fly. It supports windowing, triggers, and late data handling, making it suitable for analyzing time-series sensor data in real time. Cloud Functions are serverless compute units that can trigger alerts, notifications, or downstream actions when anomalies are detected, providing low-latency responses.
The other options are less suitable. Cloud Storage + BigQuery introduces latency because data must first be written and then queried in batch. Cloud SQL + Dataflow + AI Platform is not ideal for real-time ingestion at IoT scale, as Cloud SQL is not designed for high-throughput streaming. Bigtable + BigQuery + Cloud Composer is more suited for historical analytics or batch processing rather than real-time anomaly detection.
This architecture is cloud-native, fully managed, and scales automatically with the volume of data. By combining Pub/Sub for ingestion, Dataflow for stream processing, and Cloud Functions for alerting, engineers can implement robust, responsive anomaly detection pipelines. It supports real-time monitoring, automated decision-making, and operational efficiency, which are crucial for IoT applications where timely detection of anomalies can prevent failures, reduce costs, and improve system reliability.
