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Question 81
A company wants to optimize costs by using Amazon S3 Intelligent-Tiering. What type of data workload benefits most from this storage class?
A) Data with unpredictable or changing access patterns
B) Data accessed every day
C) Data never accessed
D) Write-once data only
Answer: A
Explanation:
S3 Intelligent-Tiering automatically moves objects between frequent and infrequent access tiers based on access patterns. This is ideal for data where access patterns are unpredictable or change over time. The service monitors access and optimizes storage costs without performance impact or retrieval fees.
Intelligent-Tiering includes additional archive tiers for data not accessed in 90 or 180 days. These tiers provide deeper cost savings while automatically retrieving data when accessed. The service adapts to changing patterns, ensuring optimal cost without manual intervention or prior knowledge of access patterns.
There is a small monthly monitoring fee per object, but for objects larger than 128 KB with varying access patterns, the storage savings typically exceed monitoring costs. The service provides cost optimization without the operational overhead of analyzing access patterns and manually moving objects.
Data accessed every day should remain in Standard storage since it is frequently accessed. Intelligent-Tiering adds monitoring costs without benefit for consistently accessed data. Standard storage is optimized and cost-effective for frequent access patterns.
Data that is never accessed should be stored in Glacier or Glacier Deep Archive from the beginning rather than using Intelligent-Tiering. Since the access pattern is known, explicit placement in archive storage saves the monitoring fees charged by Intelligent-Tiering.
While write-once data can use Intelligent-Tiering, this characteristic does not determine suitability. The key factor is whether access patterns are unpredictable. Write-once data with known access patterns should use appropriate fixed storage classes to avoid monitoring fees.
Question 82
A data engineer needs to process data that arrives throughout the day but wants to minimize costs by running batch processing jobs only once per day. What scheduling approach balances cost and freshness?
A) Schedule Glue jobs to run during off-peak hours
B) Run jobs continuously 24/7
C) Process data every minute
D) Never process accumulated data
Answer: A
Explanation:
Scheduling Glue jobs to run during off-peak hours processes accumulated data in a single batch, minimizing compute costs. A daily job can process all data that arrived since the last run, providing next-day freshness while avoiding the overhead of continuous or frequent processing.
Using time-based triggers with Glue workflows, you can schedule jobs to run at optimal times like early morning when data volumes are complete and resource costs may be lower. This approach consolidates processing, enabling better resource utilization and cost predictability compared to frequent small jobs.
Batch processing can leverage larger DPU allocations for faster completion while only paying for actual job duration. Processing once daily with 20 DPUs for 1 hour costs the same as processing hourly with 1 DPU for 20 hours, but the daily approach is simpler to manage.
Running jobs continuously wastes resources during periods of low data arrival. Glue charges by DPU-hour, so continuous execution incurs maximum costs. Unless real-time processing is required, continuous running provides no benefit and dramatically increases expenses.
Processing every minute incurs significant overhead from job startup, metadata operations, and small file creation. Frequent small jobs are less efficient than batching data into larger jobs. This approach maximizes costs while providing marginal freshness improvement over hourly or daily processing.
Never processing accumulated data defeats the purpose of data pipelines. Data must be processed to provide value for analytics and decision-making. The question is finding the optimal frequency that balances freshness requirements against costs.
Question 83
A data pipeline needs to handle occasional large spikes in data volume without manual intervention. What AWS feature provides automatic scaling?
A) Auto Scaling for EMR or Glue auto-scaling DPUs
B) Fixed cluster size regardless of load
C) Manual cluster resizing
D) Ignore load variations
Answer: A
Explanation:
Amazon EMR supports Auto Scaling that automatically adds or removes instances based on CloudWatch metrics like YARN memory or CPU utilization. During load spikes, the cluster scales out to maintain performance. When load decreases, it scales in to reduce costs. This provides elastic capacity without manual intervention.
AWS Glue can automatically allocate additional DPUs when jobs require more resources. You configure maximum DPU limits, and Glue scales within those bounds based on workload. This ensures jobs complete successfully during volume spikes while controlling maximum costs through DPU limits.
Auto Scaling responds to actual workload demands rather than requiring prediction of peak loads. You avoid overprovisioning for occasional spikes while ensuring capacity exists when needed. Scaling policies can be tuned based on performance metrics and cost objectives.
Fixed cluster sizes either overprovision for normal workloads, wasting money, or underprovision for peaks, causing failures. Without elasticity, clusters must be sized for maximum expected load, paying for excess capacity during normal operations. This approach does not adapt to changing requirements.
Manual cluster resizing requires human intervention and cannot respond quickly to unexpected load changes. By the time manual scaling occurs, the spike may have passed or jobs may have already failed. Manual processes also require 24/7 monitoring to detect when scaling is needed.
Ignoring load variations causes job failures during spikes when fixed capacity is exceeded. Failed jobs must be retried, potentially during subsequent spikes, creating a backlog. Capacity management is essential for reliable pipelines with variable workloads.
Question 84
A company needs to grant external partners temporary access to specific S3 data without creating AWS accounts for them. What mechanism should be used?
A) Pre-signed URLs with expiration times
B) Make buckets public permanently
C) Share root account credentials
D) Create long-lived access keys
Answer: A
Explanation:
Pre-signed URLs provide temporary access to specific S3 objects without requiring AWS credentials. You generate URLs that include authentication information and expire after a specified time period. Partners can download objects using these URLs through standard HTTP requests without AWS accounts.
Pre-signed URLs can be generated for specific objects and operations like GET or PUT. The URL includes an expiration timestamp, after which it becomes invalid. This time-limited access is ideal for temporary partner access, file sharing, or allowing uploads to S3 from external systems.
Generating pre-signed URLs requires programmatic access with appropriate IAM permissions. You can create URLs on-demand through scripts or applications, providing just-in-time access that minimizes security exposure. The URLs can be revoked by rotating the credentials used to generate them.
Making buckets public permanently exposes all data to the internet without access controls. This violates security best practices and data protection regulations. Public buckets are vulnerable to unauthorized access, data breaches, and compliance violations.
Sharing root account credentials provides unrestricted access to all AWS resources and is extremely dangerous. Root credentials should never be shared and should only be used for essential account management tasks. Sharing root credentials could result in account compromise and massive financial liability.
Creating long-lived access keys for external partners creates persistent security risks. If keys are compromised, they remain valid until manually rotated. Long-lived credentials also complicate access auditing and revocation. Temporary credentials with automatic expiration are far more secure.
Question 85
A data engineer needs to join streaming clickstream data with slowly changing dimension tables. What architecture pattern should be used?
A) Stream-to-table join with cached dimension data
B) Store all dimensions in stream
C) Ignore dimension data
D) Batch process everything monthly
Answer: A
Explanation:
Stream-to-table joins allow enriching streaming events with reference data from dimension tables. Services like Kinesis Data Analytics can load dimension data from S3 or databases into memory and join it with streaming records. The dimension data is refreshed periodically to capture slow changes.
For clickstream events containing product IDs, you load a product dimension table with names, categories, and prices. As click events flow through, each is joined with corresponding product details. This enrichment happens in real-time, producing complete events for downstream processing.
Caching dimension data in memory provides fast lookup performance during stream processing. For slowly changing dimensions, periodic refresh intervals balance data freshness with cache efficiency. Most dimension changes occur infrequently relative to stream event rates, making caching effective.
Storing all dimension data in every stream event creates massive data duplication and wastes bandwidth. Dimension tables might contain hundreds of attributes that rarely change. Including all this data in every event is inefficient compared to lightweight IDs with lookups.
Ignoring dimension data produces incomplete event records lacking business context. Analysts need product names and categories, not just numeric IDs. Enrichment during stream processing provides complete data for real-time analytics without requiring joins during queries.
Batch processing everything monthly eliminates real-time capabilities and introduces unacceptable delays. Modern businesses require real-time insights for operational decisions. Monthly processing cannot support use cases like fraud detection or personalization that need immediate responses.
Question 86
A data pipeline processes customer orders and must prevent processing the same order twice even if the source system sends duplicates. What pattern ensures this?
A) Idempotent processing with order ID deduplication
B) Process every message received
C) Random filtering
D) No duplicate checking
Answer: A
Explanation:
Idempotent processing ensures that processing the same order multiple times produces the same result as processing it once. By tracking order IDs that have been processed and checking each incoming order against this list, duplicates are detected and skipped. This prevents double-charging customers or double-counting revenue.
Implementation typically uses a database or caching layer like DynamoDB or ElastiCache to store processed order IDs. Before processing an order, check if its ID exists in the store. If found, skip processing. If not, process the order and add the ID to the store. This simple pattern prevents duplicates reliably.
The deduplication window should match business requirements. For real-time processing, storing IDs for 24-48 hours typically suffices. For batch processing, the window should cover the batch period plus potential reprocessing scenarios. Expired IDs can be removed to limit storage growth.
Processing every message received without deduplication guarantees that source system duplicates corrupt data. Duplicate orders create financial discrepancies, inventory errors, and customer dissatisfaction. Systems must defend against duplicates regardless of source system reliability.
Random filtering provides no systematic duplicate detection and may skip legitimate orders while processing duplicates. Randomness is inappropriate for critical business logic like order processing. Duplicate detection must be deterministic and reliable.
Operating without duplicate checking assumes perfect source systems and network reliability, which never exists in practice. Message delivery systems commonly provide at-least-once guarantees, meaning duplicates are expected. Application logic must handle duplicates through idempotent design.
Question 87
A company stores data in multiple S3 buckets across regions. They need to run analytics that spans all regions. What approach enables this efficiently?
A) Use Athena with federated queries or S3 cross-region replication
B) Manually download and combine data
C) Query each region separately without aggregation
D) Store data in only one region
Answer: A
Explanation:
Amazon Athena can query data across multiple regions by defining external tables that reference S3 URIs in different regions. A single Athena query can read from buckets in multiple regions and combine results. Cross-region queries incur data transfer costs but provide unified analytics without data movement.
Alternatively, S3 Cross-Region Replication can consolidate data into a single region for querying. Replication happens automatically and continuously, keeping a centralized copy synchronized with regional sources. This approach optimizes query performance and costs by avoiding cross-region reads during analysis.
The choice depends on data volume, query frequency, and update patterns. For infrequent analysis of large datasets, cross-region queries may be cost-effective. For frequent queries, consolidating data through replication reduces ongoing query costs despite initial replication expense.
Manually downloading and combining data is operationally intensive and does not scale. Large datasets cannot be efficiently moved through manual processes. This approach also requires local storage and compute resources to combine data before analysis.
Querying each region separately and mentally aggregating results is error-prone and time-consuming. Analysts cannot efficiently combine results across regions without automated tools. This approach prevents comprehensive analysis and increases the likelihood of mistakes.
Storing data in only one region may violate data residency requirements or create performance issues for regional applications. Multi-region storage often serves business needs like disaster recovery or low-latency local access. Analytics architecture must work with distributed data rather than forcing centralization.
Question 88
A data engineer needs to implement data cataloging that automatically discovers new datasets as they arrive in S3. What AWS service provides this capability?
A) AWS Glue Crawlers with scheduled runs
B) Manual catalog entry
C) No cataloging needed
D) Email notifications only
Answer: A
Explanation:
AWS Glue Crawlers automatically scan data sources like S3, infer schemas, detect partitions, and populate the Data Catalog. Scheduled crawlers run periodically to discover new data and update catalog metadata. This automation eliminates manual cataloging effort and ensures the catalog remains current as data evolves.
Crawlers use classifiers to identify file formats, extract schemas, and detect table structures. For partitioned data, crawlers automatically identify partition columns and values. You configure crawlers with S3 paths to scan and schedules for execution, then Glue handles discovery and cataloging automatically.
Automatic discovery accelerates data-to-insights time by making new datasets immediately queryable through Athena or Redshift Spectrum. Analysts do not need to wait for manual catalog updates or schema definitions. Crawlers can also detect schema changes and update table definitions accordingly.
Manual catalog entry is time-consuming, error-prone, and creates delays between data arrival and availability for analysis. As data sources proliferate, manual cataloging becomes unsustainable. Manual processes also fail to detect schema changes, causing query errors when schemas evolve.
Operating without cataloging makes data discovery impossible and queries difficult. Users must know S3 paths and schemas from external documentation. Without centralized metadata, data governance and access controls cannot be effectively implemented.
Email notifications about new data do not catalog it or make it queryable. Notifications inform users that action is needed but do not automate the cataloging process. Automated crawlers both detect and catalog data without requiring human intervention.
Question 89
A data pipeline must ensure that sensitive columns are encrypted at rest in Amazon S3. What approach provides column-level encryption?
A) Client-side encryption before upload with envelope encryption
B) No encryption
C) Transport encryption only
D) Encrypt only file names
Answer: A
Explanation:
Client-side encryption encrypts data before uploading to S3, ensuring sensitive columns are protected with separate keys. Envelope encryption uses data encryption keys for each column and a master key to encrypt the data keys. This hierarchy enables fine-grained key management where different columns can use different keys.
AWS Encryption SDK or similar libraries can implement column-level encryption in ETL jobs. Before writing Parquet or CSV files to S3, encrypt sensitive columns using column-specific keys. Metadata about which columns are encrypted and which keys to use can be stored securely in AWS Secrets Manager.
This approach ensures sensitive data is encrypted throughout its lifecycle and only accessible to applications with appropriate key permissions. Even if S3 data is accessed without authorization, encrypted columns remain protected. Decryption requires both S3 access and key access.
Operating without encryption violates regulatory requirements for protecting sensitive data. Unencrypted sensitive information in S3 exposes organizations to data breaches, compliance violations, and regulatory penalties. Encryption at rest is a baseline security control for sensitive data.
Transport encryption with HTTPS protects data during transfer but does not protect data at rest in S3. Once uploaded, data without at-rest encryption is vulnerable if storage is compromised. Both transport and at-rest encryption are necessary for comprehensive protection.
Encrypting only filenames provides no protection for actual data content. Sensitive information resides in file contents, not names. Filename encryption without content encryption leaves data completely exposed and provides false sense of security.
Question 90
A company runs Apache Kafka on-premises and wants to migrate to a managed service on AWS. What service should they use?
A) Amazon Managed Streaming for Apache Kafka (Amazon MSK)
B) Amazon SQS
C) Amazon SNS
D) AWS Lambda alone
Answer: A
Explanation:
Amazon MSK is a fully managed Apache Kafka service that maintains compatibility with existing Kafka applications. You can migrate on-premises Kafka workloads with minimal code changes. MSK handles cluster provisioning, configuration, patching, and monitoring, reducing operational overhead while preserving Kafka functionality.
MSK supports Kafka APIs and protocols, allowing existing producers and consumers to connect with configuration changes only. You can use familiar Kafka tools and libraries. MSK also integrates with AWS services for authentication, encryption, and logging while maintaining Kafka’s streaming semantics.
Managed service benefits include automatic broker replacement on failure, simplified multi-AZ deployment for high availability, and integrated monitoring through CloudWatch. MSK reduces the operational burden of running Kafka while providing enterprise-grade reliability and security.
Amazon SQS is a message queuing service with different semantics than Kafka. SQS does not provide Kafka’s partitioning, log-based storage, or consumer group mechanisms. Migrating from Kafka to SQS would require significant application refactoring and loss of Kafka-specific capabilities.
Amazon SNS is a pub-sub messaging service without Kafka’s streaming capabilities likeMessage queuing decouples data ingestion from processing, allowing devices to send data even when processing systems are temporarily unavailable. Dead letter queues capture messages that fail processing after multiple retry attempts, preserving them for investigation and reprocessing. This pattern ensures no data is lost due to transient failures.
Amazon SQS or Amazon Kinesis Data Streams can buffer incoming IoT messages, providing resilience against processing system failures. If processing components fail, messages remain in the queue or stream until they can be processed successfully. Dead letter queues collect messages that consistently fail, preventing them from blocking the queue while preserving data.
This architecture enables asynchronous processing where data ingestion rate can differ from processing rate. During traffic spikes, messages accumulate in queues and are processed as capacity allows. Failed messages are automatically retried with exponential backoff, and messages that still fail move to dead letter queues for manual investigation.
Direct database writes without buffering create tight coupling between devices and the database. If the database is unavailable or overloaded, device writes fail and data may be lost. This approach does not handle failures gracefully and cannot absorb traffic variations without dropping data.
Synchronous processing requires devices to wait for processing to complete before acknowledging data receipt. This increases latency and reduces system throughput. If processing fails, devices must implement retry logic, complicating device software and potentially overwhelming processing systems with retries.
Discarding failed messages immediately results in permanent data loss. For IoT applications where sensor data represents real-world measurements, losing data creates gaps in the record that cannot be recovered. Dead letter queues preserve failed messages for analysis and potential recovery.
Question 91
A data engineer needs to implement slowly changing dimension Type 2 logic to track historical changes in customer attributes. What approach preserves history?
A) Add effective date and expiration date columns with versioning
B) Update records in place without history
C) Delete old records completely
D) Ignore all changes
Answer: A
Explanation:
Type 2 slowly changing dimensions preserve history by creating new records for each change rather than updating existing records. Each record includes effective start and end dates defining when that version was valid. Current records have null or far-future end dates, while historical records have actual end dates.
When a customer attribute changes, the current record’s end date is set to the change timestamp, and a new record is inserted with the new values and a start date equal to the change timestamp. This approach maintains complete history of all changes, supporting temporal queries that reconstruct data as it existed at any point in time.
Implementation typically includes a surrogate key separate from the natural business key. Multiple records share the same business key but have different surrogate keys, effective dates, and attribute values. Queries can filter by effective date ranges to retrieve data as it existed during specific periods.
Updating records in place without history overwrites previous values, losing historical information permanently. This Type 1 approach is simpler but prevents historical analysis. For customer dimensions, losing history of address changes or status changes eliminates valuable analytical capabilities.
Deleting old records eliminates history and can break referential integrity with fact tables. Historical transactions reference dimension versions that existed at transaction time. Deleting those dimension records makes historical analysis impossible and corrupts data relationships.
Ignoring changes means dimension data becomes stale and inaccurate. Dimensions must reflect current reality for accurate analytics. The question is whether to preserve history of changes, not whether to track changes at all.
Question 92
A company wants to implement real-time fraud detection on payment transactions. What AWS architecture supports this?
A) Kinesis Data Streams with Lambda for real-time analysis
B) Batch processing once per week
C) Manual review of all transactions
D) No fraud detection
Answer: A
Explanation:
Amazon Kinesis Data Streams ingests payment transactions in real-time with low latency. AWS Lambda functions consume from the stream, applying fraud detection rules or machine learning models to each transaction as it arrives. Suspicious transactions can be flagged immediately, preventing fraudulent payments before they complete.
Lambda functions can invoke SageMaker endpoints hosting trained fraud detection models, combining rule-based and ML-based detection. Detected fraud triggers alerts through SNS or blocks transactions through API calls to payment systems. This real-time architecture enables sub-second fraud detection and response.
Stream processing provides continuous monitoring without the delays inherent in batch systems. Fraudsters exploit batch processing delays to complete fraudulent transactions before detection. Real-time systems detect and block fraud during transaction processing, minimizing losses.
Batch processing once per week allows fraud to occur undetected for days, resulting in massive losses. Weekly processing cannot prevent fraud, only detect it after completion. By that time, funds may be unrecoverable and customers impacted. Real-time detection is essential for fraud prevention.
Manual review of all transactions is impractical at scale and introduces delays that enable fraud. Humans cannot review thousands of transactions per second. Manual processes also lack the pattern recognition capabilities of machine learning models that detect subtle fraud indicators.
Operating without fraud detection exposes organizations to unlimited losses from fraudulent transactions. Fraud detection is essential for payment processing systems. The question is how to implement effective real-time detection, not whether to implement it.
Question 93
A data pipeline processes data from IoT devices across multiple time zones. How should timestamps be handled to ensure consistency?
A) Convert all timestamps to UTC during ingestion
B) Keep local time zones without conversion
C) Use random time zones
D) Ignore timestamps completely
Answer: A
Explanation:
Converting all timestamps to UTC during ingestion creates a consistent time reference across data from multiple sources and time zones. UTC eliminates ambiguity from daylight saving time changes and provides a standard baseline for temporal analysis. Timestamps can be converted to local time zones during presentation if needed.
AWS Glue and Lambda can perform timestamp normalization as part of ETL processing. Parse timestamps with their original time zone information, convert to UTC, and store the standardized value. This ensures all time-based calculations and comparisons work correctly regardless of data source location.
Storing timestamps in UTC also simplifies distributed processing where processing nodes may be in different time zones. Calculations on time ranges, aggregations by time periods, and event ordering all depend on consistent timestamp representation. UTC provides this consistency without complexity.
Keeping local time zones without conversion creates confusion when analyzing data from multiple sources. The same UTC moment has different representations in different time zones, making aggregation and comparison difficult. Daylight saving time changes create additional complexity with ambiguous or non-existent local times.
Using random time zones makes temporal analysis impossible. Timestamps must have defined meaning to support time-based queries and calculations. Random time zones provide no semantic value and prevent any meaningful time-based operations.
Ignoring timestamps completely eliminates the ability to perform time-series analysis, a fundamental requirement for IoT data. Timestamps are essential for tracking when events occurred, detecting patterns over time, and correlating events from different devices.
Question 94
A data engineer needs to optimize an AWS Glue job that processes Parquet files. The job reads entire files but only uses a few columns. What optimization should be applied?
A) Enable predicate and projection pushdown
B) Read all columns always
C) Convert Parquet to CSV first
D) Process files without optimization
Answer: A
Explanation:
Predicate and projection pushdown are query optimization techniques that Spark applies to Parquet files. Projection pushdown reads only required columns from Parquet’s columnar format rather than reading entire rows. Predicate pushdown applies filter conditions during file reading, skipping data that does not match filters.
AWS Glue automatically applies these optimizations when you select specific columns in transformations. Parquet’s columnar structure enables reading only required columns, dramatically reducing I/O and memory usage. For tables with many columns where queries use few, this optimization can reduce processing time by 90% or more.
To leverage these optimizations, structure Glue jobs to apply column selection and filtering early in the transformation pipeline. This ensures Spark pushes these operations down to the Parquet reader. Monitor Glue job metrics to verify that data processed is significantly less than total file size.
Reading all columns always wastes I/O bandwidth and memory processing unnecessary data. When only a few columns are needed, reading all columns can increase processing time by 10x or more. Columnar formats like Parquet are designed to enable selective column reading.
Converting Parquet to CSV eliminates the columnar storage benefits that make Parquet efficient. CSV requires reading entire rows regardless of how many columns are needed. This conversion would dramatically degrade performance rather than optimizing it.
Processing files without optimization ignores available performance improvements and wastes resources. Modern query engines and file formats provide sophisticated optimizations that should be leveraged. Optimization is essential for cost-effective processing at scale.
Question 95
A company needs to implement row-level access control where users only see data from their own department. What AWS service provides this capability for data lakes?
A) AWS Lake Formation with row-level security
B) Public S3 buckets
C) No access controls
D) Single password for everyone
Answer: A
Explanation:
AWS Lake Formation supports row-level security through data filters that restrict which rows users can access based on their attributes. You define filters using SQL WHERE clause conditions that are automatically applied to queries. Different users or groups can have different filters, seeing only authorized rows from shared tables.
For departmental access control, create filters that match a department column in data tables against user attributes from IAM or SAML identity providers. Users from the Sales department automatically see only sales data, while Finance users see only finance data. Filters are enforced transparently across Athena, Redshift Spectrum, and other integrated services.
Lake Formation’s centralized permission management eliminates the need to implement access controls separately in each query tool or application. Permissions and filters defined once in Lake Formation apply consistently across all access methods. This simplifies administration and ensures security policy compliance.
Public S3 buckets eliminate all access controls and expose data to everyone. This violates security requirements and makes row-level access control impossible. Public access is appropriate only for truly public datasets, never for departmental data requiring access restrictions.
Operating without access controls allows all users to access all data, violating security principles and regulatory requirements. Most data has sensitivity classifications requiring access restrictions. Unrestricted access creates security risks and compliance violations.
Using a single password for everyone provides no individual accountability and cannot implement row-level controls based on user identity. Shared credentials violate security best practices and make audit trails meaningless because actions cannot be attributed to specific individuals.
Question 96
A data engineer needs to test ETL code changes without affecting production data pipelines. What environment strategy should be implemented?
A) Separate development and production environments with isolated resources
B) Test directly in production
C) No testing needed
D) Use production data for all testing
Answer: A
Explanation:
Separate development and production environments allow safe testing of code changes without risking production data or pipelines. Development environments use separate S3 buckets, Glue jobs, and Data Catalog databases. Code changes are developed and tested in the isolated environment before promotion to production.
Environment isolation prevents test failures from impacting production pipelines. Developers can experiment freely, test edge cases, and debug issues without concerns about affecting business operations. This separation also enables parallel development where multiple team members work on different features simultaneously.
Infrastructure as code tools like CloudFormation or Terraform can provision identical development and production environments with parameter differences. This ensures consistency while maintaining separation. CI/CD pipelines can automate deployment from development through staging to production with appropriate testing gates.
Testing directly in production risks data corruption, pipeline failures, and business disruption. Production systems should only run validated, tested code. Testing in production exposes users to bugs and failures that should be caught before deployment.
Claiming no testing is needed ignores the reality that all code contains bugs and misunderstandings. Testing is essential for quality assurance and risk mitigation. Deploying untested code to production guarantees problems will impact users and business operations.
Using production data for all testing can be acceptable if proper safeguards exist, but test operations should still occur in isolated environments. Testing operations in production pipelines risks data corruption and operational disruptions. Even with production data, test processing should occur in dedicated test environments.
Question 97
A company stores analytical data in Amazon Redshift but needs to archive historical data older than 2 years to reduce costs. What approach maintains query access while reducing costs?
A) Use Redshift Spectrum to query archived data in S3
B) Delete historical data permanently
C) Keep all data in Redshift forever
D) Move data to RDS
Answer: A
Explanation:
Amazon Redshift Spectrum enables querying data stored in S3 directly from Redshift using external tables. Historical data can be unloaded from Redshift to S3 in Parquet format and deleted from Redshift tables. Spectrum external tables reference the S3 data, allowing queries to seamlessly join current Redshift data with historical S3 data.
This tiered approach provides significant cost savings because S3 storage costs much less than Redshift storage. Frequently accessed recent data remains in Redshift for fast query performance, while historical data in S3 incurs lower storage costs. Queries spanning both tiers work transparently without application changes.
The UNLOAD command exports data from Redshift to S3 efficiently in parallel. Data can be unloaded in compressed Parquet format for optimal storage costs and query performance. After verification, the unloaded data is deleted from Redshift, freeing storage capacity and reducing cluster costs.
Deleting historical data permanently eliminates the ability to analyze historical trends or meet compliance retention requirements. Many regulations mandate retaining financial and customer data for years. Deletion should only occur after retention periods expire and no business value remains.
Keeping all data in Redshift forever incurs unnecessarily high storage costs for infrequently accessed historical data. Redshift storage is optimized for query performance, which is valuable for recent data but wasteful for archives. Tiering data by access patterns optimizes costs without sacrificing capability.
Moving data to RDS does not reduce costs meaningfully and introduces incompatibilities. RDS is designed for transactional workloads, not analytical archives. RDS storage costs are comparable to or higher than Redshift, providing no cost benefit.
Question 98
A data pipeline must process sensitive healthcare data in compliance with HIPAA requirements. What security controls must be implemented?
A) Encryption at rest and in transit with audit logging
B) No encryption needed
C) Public data access
D) Shared administrator passwords
Answer: A
Explanation:
HIPAA requires encryption of Protected Health Information both at rest and in transit to prevent unauthorized access. All S3 buckets storing PHI should enable encryption using KMS keys. Data transfer between services must use TLS/SSL encryption. These technical safeguards are mandated by the HIPAA Security Rule.
Comprehensive audit logging through CloudTrail and service-specific logs is required for HIPAA compliance. Logs must capture all access to PHI including who accessed what data and when. These logs support security monitoring, incident investigation, and compliance audits. Log data must be protected from tampering.
Additional HIPAA requirements include access controls limiting who can view PHI, Business Associate Agreements with AWS, and workforce training. Technical controls like encryption and logging form the foundation, but comprehensive compliance requires administrative and physical safeguards as well.
Claiming no encryption is needed violates HIPAA Security Rule requirements for protecting PHI. Organizations that fail to encrypt PHI face compliance violations, regulatory fines, and reputational damage. Encryption is a fundamental control that must be implemented.
Public data access directly violates HIPAA Privacy Rule requirements for protecting PHI from unauthorized disclosure. Healthcare data must be strictly access-controlled and never exposed publicly. Public access would constitute a massive HIPAA violation with severe penalties.
Shared administrator passwords violate HIPAA requirements for unique user identification and accountability. Individual accounts with specific permissions are required to create audit trails and enforce least privilege access. Shared credentials make accountability impossible and compliance unachievable.
Question 99
A data engineer needs to join large datasets stored in S3 using AWS Glue. The job fails with out-of-memory errors. What optimization technique should be applied?
A) Use broadcast joins for small tables and partitioning for large tables
B) Load everything in memory simultaneously
C) Use single small instance
D) Disable all optimizations
Answer: A
Explanation:
Broadcast joins are efficient when one table is small enough to fit in memory across all workers. The small table is replicated to all nodes, eliminating shuffle operations for the large table. This dramatically reduces memory and network requirements compared to shuffle joins where both tables are redistributed across nodes.
For joins between large tables that cannot use broadcast joins, partitioning both tables on the join key before joining ensures that matching records are colocated on the same workers. This reduces shuffle size and memory requirements. Glue provides repartition operations to optimize data distribution before expensive join operations.
AWS Glue also supports increasing DPU allocation to provide more memory per worker. Combining algorithmic optimizations like broadcast joins with increased resources often resolves memory issues. Monitor job metrics to understand whether memory pressure comes from data volume or inefficient operations.
Attempting to load everything in memory simultaneously guarantees out-of-memory failures on large datasets. Distributed processing frameworks like Spark are designed to process data larger than available memory through spilling and streaming operations. Proper join strategies and partitioning enable processing without exceeding memory limits.
Using a single small instance severely limits memory and prevents distributed processing benefits. Large joins require distributed execution across multiple workers to spread memory requirements. Single-node processing cannot handle large datasets efficiently.
Disabling optimizations makes performance worse rather than better. Query optimizers apply sophisticated techniques to minimize resource usage. Disabling these optimizations forces inefficient execution plans that are more likely to fail or run extremely slowly.
Question 100
A company wants to enable data scientists to use SQL notebooks for interactive analysis of S3 data. What AWS service provides this capability?
A) Amazon Athena with Query Editor or EMR Notebooks
B) Amazon S3 alone
C) Amazon CloudWatch
D) AWS CloudFormation
Answer: A
Explanation:
Amazon Athena’s Query Editor provides a SQL notebook interface for interactive analysis of data in S3. Data scientists can write and execute SQL queries, visualize results, and save queries for reuse. Athena is serverless, requiring no infrastructure management, making it ideal for self-service analytics.
Amazon EMR Notebooks provide Jupyter environments with SparkSQL support for interactive S3 data analysis. Notebooks combine code, queries, visualizations, and narrative text in a single document. EMR integrates with Git for version control and supports collaboration through shared notebooks.
Both services enable exploratory data analysis without requiring data to be loaded into databases. Athena is fully serverless and bills per query, while EMR Notebooks require a cluster but support more complex analysis with Python and Spark. The choice depends on workload characteristics and user preferences.
Amazon S3 is storage and does not provide query or notebook capabilities. While S3 stores the data being analyzed, additional services are needed to enable SQL querying and interactive analysis.
Amazon CloudWatch is a monitoring service that collects logs and metrics from AWS services. It does not provide SQL query capabilities or notebook interfaces for data analysis. CloudWatch is for operational monitoring, not data analytics.
AWS CloudFormation is an infrastructure-as-code service for provisioning AWS resources. It does not provide data analysis capabilities or interactive query interfaces. CloudFormation deploys resources but does not analyze data stored in those resources.
