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Question 121
A company needs to provide near real-time analytics dashboards that update every few minutes. What architecture pattern should be used?
A) Streaming data through Kinesis to QuickSight with SPICE refresh
B) Batch processing once per day
C) Manual dashboard updates
D) No dashboard updates
Answer: A
Explanation:
Streaming data through Amazon Kinesis Data Streams or Kinesis Data Firehose enables continuous data ingestion with low latency. Data flows from sources through Kinesis to storage like S3 or Redshift. Amazon QuickSight can connect to these destinations with SPICE datasets configured for automatic refresh every few minutes.
SPICE is QuickSight’s in-memory calculation engine that caches data for fast dashboard rendering. Scheduled SPICE refreshes pull updated data from sources at defined intervals. For near real-time use cases, configuring refresh intervals of 1-5 minutes provides current data without continuous querying of source systems.
This architecture balances freshness with performance and cost. Streaming ingestion ensures data is available within seconds of generation. Periodic SPICE refreshes provide dashboard performance while limiting query load on sources. Users see near real-time data with sub-second dashboard response times.
Batch processing once per day introduces 24-hour data latency that does not meet near real-time requirements. Daily batch updates are appropriate for historical reporting but cannot support operational dashboards that inform real-time decision-making.
Manual dashboard updates require human intervention and introduce delays. Manual processes cannot provide consistent few-minute update frequencies. Automated refresh schedules ensure reliable, timely updates without operational overhead.
Never updating dashboards leaves them showing stale, outdated data. Dashboards must reflect current reality to support business decisions. Static dashboards are useful only for historical snapshots, not operational monitoring.
Question 122
A data engineer needs to ensure that AWS Glue jobs can recover from failures without reprocessing successfully completed portions. What feature enables this?
A) Job bookmarks to track processing progress
B) Always reprocess everything from start
C) No failure recovery mechanism
D) Manual restart tracking
Answer: A
Explanation:
AWS Glue job bookmarks track which data has been processed successfully, enabling jobs to resume from the last checkpoint after failures. Bookmarks record information about processed files, partitions, or records depending on the data source. When jobs restart, they skip already-processed data and continue from where they stopped.
For S3 sources, bookmarks track which files or objects have been processed based on modification timestamps. For JDBC sources, bookmarks can track maximum values of key columns. This state persistence enables efficient incremental processing and failure recovery without reprocessing.
Job bookmarks significantly reduce recovery time and costs after failures. Without bookmarks, failed jobs must reprocess all data, potentially duplicating work and causing data inconsistencies. Bookmarks enable exactly-once processing semantics despite at-least-once execution attempts.
Always reprocessing from the start wastes resources and time on data that was successfully processed before failure. For large datasets, reprocessing can take hours. Bookmark-based resumption completes jobs in minutes by skipping completed work.
Operating without failure recovery mechanisms means jobs completely restart after any failure, multiplying processing costs and time. Production pipelines experience occasional failures from transient issues. Recovery capabilities are essential for operational efficiency.
Manual restart tracking requires custom code to persist and retrieve processing state. This reimplements functionality that Glue provides natively through bookmarks. Manual implementations are error-prone and require ongoing maintenance.
Question 123
A company wants to enable data scientists to experiment with datasets without affecting production data or pipelines. What environment setup provides this safely?
A) Create separate development sandbox with copies of production data
B) Experiment directly in production environment
C) No separate environment needed
D) Share production credentials with everyone
Answer: A
Explanation:
Development sandboxes provide isolated environments where data scientists can experiment freely without risk to production systems. Sandbox environments include copies or samples of production data, separate compute resources, and dedicated S3 buckets. Failed experiments or errors in sandbox have no impact on production pipelines or users.
Data copies in sandbox can be full production copies for realistic testing or subsets for faster iteration. Sensitive data should be masked in sandbox to protect privacy while maintaining analytical utility. Regular refreshes keep sandbox data reasonably current with production.
Sandboxes also enable parallel development where multiple teams work on different features simultaneously without conflicts. Each team operates in their own sandbox, merging successful experiments into production through controlled promotion processes. This development workflow improves innovation while maintaining production stability.
Experimenting directly in production risks data corruption, pipeline failures, and service disruptions. Production environments serve business users who depend on reliable data. Experiments introduce uncertainty that should not affect operational systems.
Claiming no separate environment is needed ignores the incompatibility between stable operations and experimental work. Production requires stability and predictability, while experimentation requires freedom to try and fail. Separate environments accommodate both needs.
Sharing production credentials with everyone eliminates access controls and audit trails. Credentials should be environment-specific and role-based. Sandbox credentials should not provide production access, maintaining security boundaries between environments.
Question 124
A data pipeline must ensure that aggregated metrics match source transactional data exactly. What validation approach confirms this?
A) Reconciliation checks comparing source counts with aggregated results
B) No validation needed
C) Trust all calculations implicitly
D) Random sampling verification
Answer: A
Explanation:
Reconciliation checks compare control totals between source and destination to verify data completeness and accuracy. Count source records and compare with the sum of aggregated results. Check that revenue totals in aggregated summaries match source transaction totals. Discrepancies indicate data loss or calculation errors requiring investigation.
Implement reconciliation as automated checks in ETL pipelines that fail jobs when discrepancies exceed thresholds. Log reconciliation results for audit trails. Reconciliation should verify row counts, sum of numeric fields, and distinct value counts on key dimensions. These multiple checks provide confidence in data integrity.
For daily batch loads, reconciliation ensures each day’s data is complete and accurate before making it available to users. Financial reconciliation is especially critical, where even small discrepancies can indicate serious issues. Regular reconciliation builds trust in data products.
Operating without validation means errors go undetected until users notice problems in reports. By that time, incorrect data may have influenced business decisions. Proactive validation catches issues during pipeline execution before impacting users.
Trusting calculations implicitly without verification assumes bug-free code and perfect systems, which never exist. Software contains bugs, requirements are misunderstood, and systems have edge cases. Validation provides confidence that implementations match requirements.
Random sampling can detect major issues but may miss subtle errors affecting small data subsets. Complete reconciliation provides stronger guarantees than sampling. For critical financial or operational data, comprehensive validation is required.
Question 125
A data engineer needs to optimize query performance in Amazon Athena for queries that filter on date ranges. How should the data be organized?
A) Partition data by date in S3 folder structure
B) Store all data in single file without organization
C) Random folder naming
D) Alphabetical ordering only
Answer: A
Explanation:
Partitioning data by date creates S3 folder structures like year=2024/month=01/day=15 that Athena uses for partition pruning. When queries filter on dates, Athena scans only relevant partitions rather than all data. For queries selecting single days from years of data, partitioning reduces data scanned by 99% or more.
Date partitioning aligns with common query patterns for time-series data where analysis focuses on specific time periods. Athena’s query optimizer understands partition structures and automatically applies filters to limit scanning. This optimization reduces both query time and costs since Athena charges by data scanned.
Hierarchical date partitioning supports queries at various granularities. Year-level partitions support annual queries, month-level supports monthly analysis, and day-level supports daily reports. A single partition structure accommodates diverse query patterns efficiently.
Storing all data in a single file prevents any partition-based optimization and requires scanning the entire dataset for every query. As data accumulates, single-file approach becomes increasingly inefficient. Queries slow down and costs increase linearly with data volume.
Random folder naming provides no semantic meaning that query optimizers can exploit. Without predictable structure, Athena cannot determine which folders to scan and must check all data. Random naming eliminates partition pruning benefits.
Alphabetical ordering does not align with date-based query patterns and provides no optimization for time-range filters. While alphabetical ordering might help name-based searches, it does not benefit temporal queries that dominate time-series analysis.
Question 126
A company needs to ensure that deleted S3 objects can be recovered within 30 days of deletion. What S3 feature provides this capability?
A) S3 Versioning with lifecycle policies
B) No recovery mechanism
C) Manual backups only
D) Hope objects are never deleted
Answer: A
Explanation:
S3 Versioning maintains multiple versions of each object, creating a version when objects are modified or deleted. Deleting a versioned object creates a delete marker rather than permanently removing it. The object can be recovered by removing the delete marker or restoring a previous version within the retention period.
Lifecycle policies can automatically permanently delete old versions after specified periods like 30 days. This combination provides recovery windows while eventually reclaiming storage from old versions. Policies transition old versions to cheaper storage classes before deletion, optimizing costs.
Versioning protects against accidental deletions and allows point-in-time recovery. If an object is deleted today, you can recover yesterday’s version. For compliance scenarios requiring retention, versioning ensures data remains recoverable throughout required periods.
Operating without recovery mechanisms means deleted data is permanently lost immediately. Accidental deletions from human error, application bugs, or malicious actions cannot be undone. Versioning provides essential protection against data loss.
Manual backups require operational overhead and may not capture all objects at time of deletion. Backups typically run on schedules, creating recovery point objectives of hours or days. Versioning provides continuous protection without gaps.
Hoping objects are never deleted is not a strategy. Deletions occur accidentally and intentionally in normal operations. Recovery capabilities must exist to handle mistakes and changing requirements that make previously deleted data valuable.
Question 127
A data pipeline must process late-arriving data that arrives after initial aggregations are complete. What architecture pattern handles this?
A) Event-time processing with windowing and late data handling
B) Ignore all late data
C) Only process data as it arrives
D) Reject out-of-order events
Answer: A
Explanation:
Event-time processing uses timestamps embedded in data to determine correct time windows rather than processing time when data arrives. Windowing with allowed lateness parameters defines how long windows remain open to accept late data. Late events are incorporated into appropriate windows based on their event timestamps.
Services like Kinesis Data Analytics and Apache Spark Structured Streaming support event-time semantics with configurable lateness tolerance. Windows remain open for defined periods after their nominal end time, accepting late events. When lateness tolerance expires, windows close and emit final results.
This approach ensures correctness despite network delays, system downtime, or source delays that cause data to arrive out of order. Event timestamps reflect when events actually occurred, not when they were processed. Using event time produces accurate aggregations that reflect reality.
Ignoring late data introduces errors in aggregations and metrics. Late-arriving sales transactions would not be counted, understating revenue. Network or processing delays should not cause data loss. Systems must handle eventual delivery of all data.
Only processing data as it arrives using processing time creates aggregations that reflect system behavior rather than business reality. Events assigned to windows based on arrival time rather than occurrence time produce meaningless aggregations when delays vary.
Rejecting out-of-order events treats inevitable delays as errors. Distributed systems naturally experience variable latency. Robust systems accommodate this reality rather than failing when timing is imperfect. Event-time processing with lateness tolerance provides both correctness and robustness.
Question 128
A data engineer needs to implement automated testing for AWS Glue ETL jobs. What approach enables continuous integration for data pipelines?
A) Unit tests for transformation logic with test data fixtures
B) No testing of ETL code
C) Only manual testing before deployment
D) Test in production with real data
Answer: A
Explanation:
Unit tests verify transformation logic correctness using small test datasets with known expected outputs. Test fixtures include sample input data representing normal cases, edge cases, and error conditions. Automated tests run during CI/CD builds, catching regressions before deployment to production.
For Glue jobs written in Python or Scala, standard testing frameworks like pytest or ScalaTest work well. Tests create small DataFrames with test data, apply transformation logic, and assert that outputs match expectations. Mock external dependencies like database connections to enable isolated testing.
Automated testing provides confidence that code changes do not break existing functionality. Test suites run in minutes, providing rapid feedback during development. Comprehensive tests reduce bugs reaching production and enable refactoring with safety nets.
Operating without ETL testing means bugs are discovered in production when they corrupt data or break pipelines. Production failures impact business users and damage trust in data products. Testing is essential for quality assurance.
Manual testing does not scale and provides inconsistent coverage. As codebases grow, manual testing becomes impractical. Automated tests execute consistently and completely every time, catching issues manual testing might miss.
Testing in production with real data risks corrupting actual business data and disrupting operational pipelines. Production should only run validated code. Testing must occur in separate environments using test data.
Question 129
A company wants to provide SQL query access to data in Amazon DynamoDB without ETL into another database. What AWS capability enables this?
A) Athena Federated Query with DynamoDB connector
B) Direct SQL queries to DynamoDB tables
C) Manual data export to CSV files
D) DynamoDB does not support querying
Answer: A
Explanation:
Athena Federated Query uses Lambda-based connectors to query data sources beyond S3. The DynamoDB connector translates SQL queries into DynamoDB API calls, enabling standard SQL queries against DynamoDB tables. Results are returned through Athena’s standard interface alongside queries against S3 data.
Federated queries enable joining DynamoDB data with S3 data lakes in single SQL statements. For example, join real-time user sessions in DynamoDB with historical clickstream data in S3. This unified query capability eliminates the need to extract DynamoDB data into relational databases for analysis.
The DynamoDB connector handles the impedance mismatch between SQL and DynamoDB’s key-value model. SQL queries are translated into efficient DynamoDB scans or queries. While not optimized for large-scale analytics, federated queries enable ad-hoc analysis and occasional reporting without ETL pipelines.
DynamoDB’s native API does not support SQL queries. Its query and scan operations use DynamoDB’s specific syntax. While sufficient for application development, these APIs are unfamiliar to SQL-oriented analysts. Athena provides a SQL interface to DynamoDB data.
Manual data export to CSV files for analysis introduces delays and operational overhead. Exports create point-in-time snapshots that become stale immediately. Federated queries provide current data without separate export processes.
Claiming DynamoDB does not support querying is incorrect. DynamoDB has robust query capabilities through its native API and now through Athena Federated Query for SQL access.
Question 130
A data pipeline experiences variable latency from source systems, sometimes causing SLA violations. What monitoring approach provides early warning of issues?
A) CloudWatch alarms on pipeline duration with anomaly detection
B) No monitoring of pipeline performance
C) Check manually once per month
D) Wait for users to complain
Answer: A
Explanation:
CloudWatch alarms monitor metrics like Glue job duration, Lambda execution time, or custom metrics tracking end-to-end pipeline latency. Alarms trigger when metrics exceed static thresholds or deviate from historical patterns using anomaly detection. Early warnings enable proactive intervention before SLA violations impact users.
Anomaly detection identifies unusual patterns without requiring manual threshold configuration. CloudWatch learns normal variability and triggers alarms when current values fall outside expected ranges. This adaptive approach catches performance degradation that static thresholds might miss.
Comprehensive monitoring includes metrics at each pipeline stage to identify bottlenecks. If overall latency increases, stage-specific metrics indicate whether delays occur during extraction, transformation, or loading. Granular monitoring speeds root cause analysis and resolution.
Operating without performance monitoring means SLA violations are discovered reactively when users complain. By that time, business operations may be impacted. Proactive monitoring enables intervention before problems affect users.
Monthly manual checks provide very coarse visibility with 30-day blind spots. Issues occurring between checks go unnoticed until monthly reviews. Real-time or near-real-time monitoring is essential for production pipelines with SLAs.
Relying on user complaints as the monitoring mechanism damages user trust and often indicates significant business impact has already occurred. Monitoring should detect issues before users experience problems, not after.
Question 131
A data engineer needs to implement data masking for development environments while maintaining referential integrity. What approach preserves relationships while protecting sensitive data?
A) Deterministic masking with consistent token mapping
B) Random masking for each occurrence
C) No masking in development
D) Complete data randomization
Answer: A
Explanation:
Deterministic masking applies the same transformation to a value every time it appears, preserving relationships between tables. If customer ID “12345” masks to “98765”, every occurrence of “12345” maps to “98765” consistently. This maintains foreign key relationships while protecting actual values.
Token mapping tables store relationships between original and masked values, though the mapping itself must be securely protected. For email addresses, deterministic masking might preserve domain structure while anonymizing local parts. For names, phonetically similar fake names maintain test realism while protecting real identities.
This approach enables realistic development and testing with protected data. Developers can join tables, test workflows, and debug issues using masked data that behaves like production. Referential integrity ensures foreign keys match primary keys after masking.
Random masking generates different tokens for each occurrence of a value, breaking referential integrity. If a customer ID appears in multiple tables, random masking produces different masked values in each location. This breaks joins and makes the data unusable for testing.
Using unmasked production data in development exposes sensitive information to developers who do not need access to real values. This violates data protection principles and regulations. Development should use masked data that protects privacy while enabling work.
Complete randomization destroys all structure and relationships, making data useless for development. While randomization protects sensitive values, it produces nonsensical data that cannot support realistic testing. Deterministic masking balances protection with utilityAmazon SNS is a pub-sub messaging service without Kafka’s streaming capabilities like offset management, replay, or long-term message retention. SNS is designed for different use cases than Kafka and would not support direct migration of Kafka applications.
AWS Lambda can consume from MSK but is not a replacement for Kafka itself. Lambda is a compute service that can process streaming data but does not provide the message broker functionality that Kafka provides. MSK is the appropriate managed Kafka service.
Question 132
A data pipeline processes financial transactions and must prevent duplicate processing even if the same transaction is received multiple times. What pattern ensures deduplication?
A) Track processed transaction IDs in DynamoDB with conditional writes
B) Process every transaction without checking
C) Use random filtering
D) Ignore transaction IDs completely
Answer: A
Explanation:
DynamoDB provides conditional writes that enable atomic deduplication checks. Before processing a transaction, attempt to write its ID to a DynamoDB table with a condition that the ID does not already exist. If the write succeeds, process the transaction. If it fails due to existing ID, skip processing as it’s a duplicate.
This pattern provides distributed deduplication across multiple processing instances. DynamoDB’s strong consistency ensures that concurrent processes checking the same transaction ID receive consistent results. The first process to write an ID succeeds; all others receive conditional check failures indicating duplicate.
Using DynamoDB for deduplication is scalable and performant with microsecond read/write latency. Time-to-live (TTL) can automatically remove old transaction IDs after a retention window, preventing unlimited table growth. This approach is more reliable than in-memory deduplication that loses state on failures.
Processing every transaction without checking guarantees duplicates corrupt financial data. Duplicate transactions cause incorrect balances, double charges, and audit failures. Financial systems must implement robust deduplication to ensure accuracy.
Random filtering provides no systematic duplicate detection and may process duplicates while filtering legitimate transactions. Deduplication must be deterministic based on transaction identifiers, not probabilistic.
Ignoring transaction IDs eliminates the ability to identify duplicates. Transaction IDs exist specifically to enable deduplication and traceability. Systems must use identifiers to implement correctness guarantees.
Question 133
A company stores machine learning training data in S3 and needs to version datasets to track which data was used to train each model. What approach manages dataset versions?
A) Use S3 object versioning with tags identifying dataset versions
B) Overwrite files without tracking versions
C) Use random file names
D) No version tracking needed
Answer: A
Explanation:
S3 object versioning automatically maintains multiple versions of each object with unique version IDs. Tags can label specific versions as dataset releases like “v1.0” or “training-2024-01”. Metadata links model artifacts to specific dataset version IDs, providing complete lineage from model back to training data.
Version tracking enables model reproducibility where you can identify exactly which data trained a specific model. This is essential for debugging model behavior, understanding performance changes between versions, and regulatory compliance in industries requiring model transparency.
S3 versioning combined with lifecycle policies manages storage costs by transitioning old dataset versions to cheaper storage classes. Recent versions remain in Standard storage for active use, while historical versions move to Glacier for archival. This balances accessibility with cost optimization.
Overwriting files without versioning permanently loses historical datasets and breaks reproducibility. If model performance degrades after retraining, you cannot compare with the original training data. Versioning preserves dataset history indefinitely.
Random file names without systematic versioning create chaos where datasets cannot be reliably referenced or compared. Systematic versioning with semantic version numbers enables clear communication about dataset evolution and comparison between versions.
Operating without version tracking makes model lineage impossible and prevents understanding why different model versions behave differently. Version tracking is fundamental to responsible ML operations and reproducibility.
Question 134
A data engineer needs to optimize AWS Glue jobs that read small files from S3. The jobs spend most time listing and opening files. What optimization reduces this overhead?
A) Combine small files into larger files before processing
B) Process each file individually without batching
C) Increase file count further
D) Read files sequentially only
Answer: A
Explanation:
Combining small files into larger files dramatically reduces listing and opening overhead. AWS Glue and Spark perform best with files sized 128MB to 1GB. Each file requires API calls to list, metadata retrieval, and connection setup. Consolidating thousands of small files into hundreds of large files eliminates most overhead.
File combination can be implemented as a separate Glue job that reads small files and writes consolidated outputs. This preprocessing step runs periodically to compact newly arrived small files. The main processing jobs then read consolidated files, completing much faster.
Consolidated files also enable better compression ratios, reducing both storage costs and data transfer time. Larger files allow compression algorithms to find more patterns. For formats like Parquet, larger files enable better column encoding and metadata optimization.
Processing each file individually maximizes overhead and minimizes throughput. With thousands of small files, processing time is dominated by file operations rather than actual data transformation. This approach wastes resources and extends job duration unnecessarily.
Increasing file count exacerbates the problem rather than solving it. More files mean more overhead with no benefits. File count should be minimized for efficient processing while maintaining parallelization opportunities.
Sequential file reading prevents parallelization benefits that distributed processing frameworks provide. However, addressing the small file problem through consolidation is more impactful than parallelization optimizations, as parallelizing overhead-heavy operations still wastes resources.
Question 135
A company needs to implement data retention policies that vary by data classification. Personal data must be deleted after 2 years, while transaction data must be retained for 7 years. How should this be implemented?
A) Use S3 Lifecycle policies with object tags defining retention periods
B) Apply same retention to all data
C) Manual deletion when remembered
D) Retain all data forever
Answer: A
Explanation:
S3 Lifecycle policies can filter objects by tags when applying expiration rules. Tag objects with classifications like “personal-data” or “transaction-data” during ingestion. Create lifecycle rules that delete objects tagged “personal-data” after 730 days and objects tagged “transaction-data” after 2,555 days (7 years).
Tag-based lifecycle policies enable flexible, automated retention management that scales across diverse datasets. Different data types with different regulatory requirements can coexist in the same bucket with appropriate retention applied to each. Tagging during ingestion ensures correct classification from the start.
Automated expiration based on tags ensures compliance with data protection regulations like GDPR that mandate deleting personal data when no longer needed. Lifecycle policies execute automatically without human intervention, preventing retention violations from forgotten manual processes.
Applying the same retention to all data violates regulatory requirements where different data types have different mandated retention periods. Personal data must be deleted to protect privacy, while financial data must be retained for audit purposes. Differentiated policies are required.
Manual deletion does not scale and creates compliance risks from delayed or forgotten deletions. As data volumes grow, manual management becomes impractical. Automated policies ensure timely, consistent retention enforcement.
Retaining all data forever violates data minimization principles in privacy regulations. Organizations must delete data when retention purposes expire. Indefinite retention increases storage costs and privacy risks unnecessarily.
Question 136
A data pipeline must ensure that downstream systems receive complete datasets before beginning processing. What coordination pattern should be used?
A) Use event notifications when full dataset arrives with coordinating flag file
B) Process partial data immediately
C) No coordination between stages
D) Random processing start times
Answer: A
Explanation:
A coordination flag file (often called a “_SUCCESS” file) signals that all data for a batch has arrived. Upstream processes write data files followed by the flag file as the last operation. Downstream processes wait for the flag file before beginning processing, ensuring they see complete datasets.
S3 Event Notifications can trigger Lambda functions or Step Functions when the flag file appears. These orchestrators then trigger downstream processing jobs with confidence that all input data is present. This event-driven coordination eliminates polling and provides immediate triggering upon data completeness.
The flag file pattern is simple but effective for batch coordination. Flag files are tiny markers with no business data, just signaling completion. Atomic S3 PUT operations ensure the flag appears only after all data files are written, preventing race conditions.
Processing partial data immediately before the full dataset arrives produces incorrect results. Aggregations, counts, and analytics based on incomplete data mislead business users. Coordination ensures processing waits for complete input.
Operating without coordination between pipeline stages creates race conditions where downstream processing may start before upstream completes. This results in missing data, inconsistent results, and pipeline failures. Explicit coordination is essential for reliable pipelines.
Random processing start times provide no guarantees about data completeness and may process before data arrives or wait unnecessarily long after data is ready. Deterministic event-driven coordination provides optimal reliability and latency.
Question 137
A data engineer needs to query data across AWS accounts without copying data. What architecture enables this?
A) Cross-account IAM roles with S3 bucket policies granting access
B) Copy all data to single account
C) Make all buckets public
D) Email data between accounts
Answer: A
Explanation:
Cross-account IAM roles enable secure access to S3 buckets in different accounts. The data owner account creates a bucket policy granting read permissions to a role in the accessing account. Users in the accessing account assume the role to read data directly from the owner’s bucket without copying.
Services like Athena and Glue in the accessing account can query data across account boundaries using these roles. The accessing account’s IAM role includes permissions to assume the cross-account role and read from specific S3 buckets. All access is logged through CloudTrail for audit trails.
This architecture maintains data ownership and governance boundaries while enabling analytics across organizational boundaries. Data owners retain full control over their data and can revoke access at any time. Centralized querying provides unified analytics without centralizing data storage.
Copying all data to a single account duplicates storage costs and creates data synchronization challenges. Copies become stale as source data changes, requiring continuous replication. This approach also concentrates data ownership, potentially violating organizational boundaries.
Making buckets public exposes data to the internet, eliminating security controls. Public access is never appropriate for business data requiring access restrictions. Cross-account access maintains security while enabling controlled sharing.
Emailing data between accounts is insecure, manual, and does not scale. Email systems are not designed for large data transfers. This approach creates ungoverned data copies and cannot support analytical queries.
Question 138
A company wants to use Amazon Redshift to analyze both current operational data and historical archives. What architecture optimizes costs while maintaining query capability?
A) Store current data in Redshift, historical data in S3 with Redshift Spectrum
B) Store all data in Redshift regardless of age
C) Delete all historical data
D) Store everything in S3 only
Answer: A
Explanation:
Redshift Spectrum enables querying data in S3 using the same SQL interface as data in Redshift tables. Current, frequently accessed data remains in Redshift for optimal performance. Historical data older than a cutoff (e.g., 1-2 years) is unloaded to S3 and queried through Spectrum external tables.
This tiered approach dramatically reduces costs because S3 storage is significantly cheaper than Redshift storage. Frequently accessed current data benefits from Redshift’s performance, while historical data incurs lower storage costs in S3. Queries can seamlessly join Redshift and Spectrum data.
Unloading historical data to S3 in columnar Parquet format optimizes both storage costs and Spectrum query performance. Compressed Parquet reduces storage by 75%+ compared to row-based formats. Spectrum’s columnar scanning ensures efficient query execution on archived data.
Storing all data in Redshift regardless of age incurs unnecessary high storage costs for infrequently accessed archives. As historical data accumulates over years, storage costs grow linearly. Tiering by access patterns optimizes costs without sacrificing query capability.
Deleting historical data violates retention requirements and eliminates valuable analytical capabilities. Historical analysis reveals trends and patterns not visible in recent data alone. Archives must be retained, but can be stored cost-effectively.
Storing everything in S3 only eliminates Redshift’s performance benefits for current data. Frequent queries against current data require Redshift’s optimized storage and query engine. Pure S3 storage with Athena is slower for complex analytical workloads.
Question 139
A data pipeline processes streaming IoT sensor data and must detect anomalies in real-time. What AWS service combination supports this?
A) Kinesis Data Streams with Lambda invoking SageMaker endpoints
B) Batch processing daily
C) Manual inspection of all readings
D) No anomaly detection
Answer: A
Explanation:
Kinesis Data Streams ingests sensor data in real-time with sub-second latency. Lambda functions consume from Kinesis, invoking SageMaker endpoints hosting trained anomaly detection models. Models score each sensor reading, identifying anomalous values that trigger alerts or automated responses.
This architecture provides real-time anomaly detection with latency measured in milliseconds to seconds. Anomalies are identified as sensor data arrives, enabling immediate response for critical applications like equipment monitoring or fraud detection. Real-time processing prevents issues from going unnoticed.
SageMaker endpoints provide scalable, low-latency inference for production ML models. Models can be updated independently of stream processing code, enabling continuous improvement of anomaly detection accuracy. Lambda’s automatic scaling handles varying sensor data rates without manual capacity management.
Batch processing daily introduces 24-hour delays in anomaly detection. For operational use cases like equipment failure prediction, delays allow problems to worsen before detection. Real-time anomaly detection enables preventive action before failures occur.
Manual inspection of sensor readings is impossible at IoT scale with thousands of sensors generating readings every second. Humans cannot process data fast enough or detect subtle anomalies that ML models identify. Automation is essential for IoT analytics.
Operating without anomaly detection misses opportunities to prevent equipment failures, detect fraud, or identify operational issues. Anomaly detection provides actionable insights that improve safety, reduce costs, and enhance operations.
Question 140
A data engineer needs to ensure that AWS Glue crawlers detect new partitions in S3 without crawling existing partitions. What crawler configuration optimizes this?
A) Configure incremental crawls with exclusion patterns for existing partitions
B) Crawl entire dataset every time
C) Never update catalog
D) Manual partition addition only
Answer: A
Explanation:
Incremental crawler configuration crawls only new or changed data by comparing timestamps and paths against previous crawl results. Crawlers can be configured with paths or date patterns that focus on recent partitions while excluding historical partitions already cataloged.
For date-partitioned data, configure crawlers to scan only recent date ranges like the last week. As time advances, the crawler naturally focuses on new partitions while ignoring older ones. Exclusion patterns can explicitly skip historical partition paths if needed.
Incremental crawling reduces crawler runtime and costs by avoiding redundant scanning of unchanged data. Large datasets with years of historical partitions benefit significantly from incremental approaches. Crawlers complete in minutes rather than hours.
Crawling the entire dataset every time wastes time and money scanning partitions that have not changed. As datasets grow to petabytes across thousands of partitions, full crawls become impractically slow. Incremental crawling scales efficiently.
Never updating the catalog leaves it stale and incomplete as new data arrives. Users cannot query recent data if catalog partitions are not updated. Regular crawler runs or event-driven updates maintain catalog currency.
Manual partition addition does not scale and introduces delays between data arrival and availability for querying. As partition creation frequency increases, manual updates become bottlenecks. Automated crawler updates are essential for operational efficiency.
