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Question 141
A company needs to implement column-level encryption where different columns use different encryption keys. What approach enables this?
A) Client-side encryption before upload with per-column key management
B) Single encryption key for all columns
C) No column-level encryption
D) Encrypt only table names
Answer: A
Explanation:
Client-side encryption encrypts data before uploading to S3, enabling fine-grained control over which keys encrypt which columns. Applications or ETL jobs read data, encrypt sensitive columns using column-specific keys from AWS KMS or Secrets Manager, then write encrypted data to S3.
Per-column encryption enables security policies where different organizational roles have access to different keys. Finance personnel might have keys to decrypt salary columns, while HR has keys for personal information. Users without specific keys see encrypted values they cannot decrypt.
AWS Encryption SDK simplifies implementing client-side encryption with features like envelope encryption and key rotation. Libraries handle cryptographic operations while developers focus on business logic. Column-level encryption integrates into ETL pipelines transparently.
Using a single encryption key for all columns prevents fine-grained access control. Either users have access to all data or none. Column-level keys enable nuanced permissions where users see some columns decrypted and others remain encrypted.
Operating without column-level encryption when regulations or policies require it violates compliance requirements. Some sensitive columns like social security numbers may require higher protection than other data in the same table. Column-level granularity meets these requirements.
Encrypting only table names provides no protection for actual data content. Sensitive information resides in column values, not metadata. Content encryption is required for data protection.
Question 142
A data pipeline must ensure exactly-once delivery of messages from Kinesis Data Streams to downstream systems. What pattern ensures this?
A) Use Kinesis Client Library with checkpoint tracking and idempotent processing
B) Process messages multiple times deliberately
C) No deduplication logic
D) Random message processing
Answer: A
Explanation:
Kinesis Client Library (KCL) automatically manages checkpoints that track which stream records have been processed. After successfully processing a batch, KCL updates the checkpoint. If processing fails, the next attempt starts from the last checkpoint, preventing data loss while enabling exactly-once semantics.
Idempotent processing ensures that processing the same record multiple times produces the same result as processing once. Use record sequence numbers as idempotency keys to detect and skip duplicates. Combined with KCL checkpointing, this achieves exactly-once delivery guarantees.
KCL handles failover automatically, redistributing shards to available workers when instances fail. Checkpoint tracking ensures processing resumes from the correct position without gaps or duplicates. This reliability is essential for financial, healthcare, and other critical applications.
Deliberately processing messages multiple times without deduplication corrupts data with duplicates. Applications must implement exactly-once semantics where each message affects state exactly once despite potential retry attempts.
Operating without deduplication logic assumes at-most-once delivery that loses messages on failures. Production systems require at-least-once delivery for reliability and idempotent processing for correctness. Together these provide exactly-once semantics.
Random message processing provides no delivery guarantees and will lose messages or process duplicates unpredictably. Reliable systems require deterministic processing with checkpoint tracking to ensure all messages are processed exactly once.
Question 143
A data engineer needs to implement data quality rules that reject records failing validation while allowing valid records to proceed. What pattern enables this?
A) Filter bad records to error stream/bucket while good records continue processing
B) Fail entire batch on any error
C) Process all records regardless of quality
D) Discard all error information
Answer: A
Explanation:
Filtering separates valid and invalid records into different output streams or S3 locations. AWS Glue transformations can evaluate validation rules and route records based on results. Valid records proceed through the pipeline, while invalid records route to error buckets with metadata explaining failures.
Error streams preserve bad records for investigation and reprocessing. Data quality teams can analyze failure patterns, correct source system issues, and potentially reprocess corrected data. This visibility into quality problems drives continuous improvement.
Partial processing maximizes throughput by not blocking good records while quality issues in some records are investigated. Users access complete, valid data without delays from quarantined bad records. Error handling becomes a parallel process rather than blocking the main pipeline.
Failing entire batches on any error wastes processing and delays delivery of valid records. If 1 record in 1 million fails validation, there’s no reason to block the other 999,999. Partial failure handling is essential for operational efficiency.
Processing all records regardless of quality allows bad data to corrupt downstream systems and analytics. Quality gates exist to protect data integrity. Processing without validation produces unreliable results that users cannot trust.
Discarding error information prevents understanding quality issues and prevents reprocessing corrected data. Error details are valuable for root cause analysis and process improvement. Errors should be logged with context for troubleshooting.
Question 144
A company uses AWS Glue to process data but needs to integrate with proprietary on-premises systems. What connectivity approach enables this?
A) AWS Glue connections with VPN or Direct Connect to on-premises networks
B) No connectivity to on-premises systems
C) Manual data transfer via email
D) Public internet exposure of on-premises systems
Answer: A
Explanation:
AWS Glue connections can access on-premises systems through VPN or Direct Connect network connectivity. Glue jobs run in VPCs and can reach on-premises databases, APIs, or applications through private network connections. JDBC connections enable Glue to read from and write to on-premises databases.
Network connectivity is established once and used by multiple Glue jobs. Security groups and network ACLs control access, maintaining security while enabling data integration. This hybrid architecture bridges cloud and on-premises systems during migration or for long-term hybrid operations.
Glue connections support various database types including Oracle, SQL Server, MySQL, and PostgreSQL. Custom connectors can be built for proprietary systems using JDBC or APIs. This flexibility enables integration with diverse on-premises data sources.
Operating without connectivity to on-premises systems forces manual data movement that does not scale. Automated ETL requires programmatic access to source systems. Manual processes create bottlenecks and delays.
Manual data transfer via email is insecure, slow, and inappropriate for production data pipelines. Email systems are not designed for large data transfers or automated integration. Proper network connectivity enables reliable, automated data flow.
Exposing on-premises systems directly to the public internet creates severe security risks. VPN or Direct Connect provides secure, private connectivity without internet exposure. On-premises systems should never be publicly accessible.
Question 145
A data pipeline processes financial data and must ensure audit logs are tamper-proof. What approach provides immutable audit trails?
A) Write logs to S3 with Object Lock in compliance mode
B) Store logs in mutable database
C) Keep logs in memory only
D) Allow unrestricted log modification
Answer: A
Explanation:
S3 Object Lock in compliance mode makes objects immutable for a specified retention period. Once audit logs are written with Object Lock, they cannot be deleted or modified by anyone, including AWS root users, until the retention period expires. This provides regulatory-grade immutability for audit trails.
Compliance mode Object Lock is specifically designed for regulatory requirements mandating write-once-read-many (WORM) storage for audit logs, financial records, and other compliance data. The immutability guarantees satisfy regulations like SEC 17a-4 for financial services.
Audit log immutability prevents tampering with evidence of system activities. Even compromised administrator credentials cannot alter historical audit logs. This protection is essential for forensic investigations and compliance audits where log integrity must be unquestionable.
Storing logs in mutable databases allows modification or deletion that could hide malicious activities or errors. Audit logs must be tamper-proof to serve as reliable evidence. Mutable storage is insufficient for audit requirements.
Keeping logs only in memory means they are lost when systems restart or fail. Audit logs must be durably persisted to survive failures and be available for long-term retention. Memory storage provides no durability or retention capabilities.
Allowing unrestricted log modification defeats the entire purpose of audit logging. Audit trails must be immutable to be trustworthy. The ability to alter logs makes them useless for security and compliance purposes.
Question 146
A data engineer needs to optimize costs for a Redshift cluster that has periods of low usage. What cost optimization strategy should be implemented?
A) Use Redshift pause and resume for idle periods
B) Keep cluster running 24/7 regardless of usage
C) Provision maximum capacity always
D) Never scale cluster resources
Answer: A
Explanation:
Redshift pause and resume allows stopping clusters during idle periods without losing data or configuration. Paused clusters incur only storage costs, not compute charges. For development, test, or analytics workloads with predictable idle times like nights and weekends, pausing saves 60-75% of costs.
Pause and resume operations complete in minutes. CloudWatch Events or Lambda functions can automate pausing based on schedules or inactivity detection. Clusters resume automatically when users submit queries, providing transparent on-demand access with cost optimization.
Combining pause/resume with right-sizing ensures clusters match workload requirements during active periods. Rather than maintaining excess capacity constantly, provision for actual needs and pause during idle times. This dual optimization approach maximizes savings.
Keeping clusters running 24/7 regardless of usage wastes money during idle periods. Redshift charges by the hour for provisioned capacity. Clusters receiving no queries during nights pay full compute costs for no value. Pausing eliminates this waste.
Provisioning maximum capacity always optimizes for peak workload but pays for unused capacity most of the time. Right-sizing for typical workload with autoscaling for peaks provides better cost/performance balance than persistent overprovisioning.
Never scaling cluster resources means clusters are either constantly underprovisioned (poor performance) or overprovisioned (wasted costs). Dynamic resource management adapting to workload provides optimal efficiency.
Question 147
A company needs to provide data analysts with pre-joined, denormalized tables optimized for reporting. What data modeling approach should be used?
A) Star schema with dimension and fact tables in gold layer
B) Highly normalized 3NF schema
C) No schema design
D) Single flat file for everything
Answer: A
Explanation:
Star schema organizes data into fact tables containing metrics and dimension tables containing descriptive attributes. Facts reference dimensions through foreign keys. This denormalized structure optimizes query performance for analytical workloads by minimizing joins and providing intuitive query patterns.
Fact tables store measurements like sales amounts, quantities, or durations along with foreign keys to dimensions. Dimension tables store attributes like customer names, product categories, or date hierarchies. Queries join one fact table with multiple dimensions, a pattern optimized by analytics engines.
Star schemas specifically target business user query patterns where analysts slice and dice metrics by various dimensions. Pre-joined dimensions with facts in a gold layer provide query-ready data that analysts access directly. This design balances query performance with storage efficiency.
Highly normalized 3NF schemas optimize for transactional systems and data integrity but require complex joins for analytical queries. Multiple join hops across normalized tables degrade query performance. Analysts prefer denormalized structures that are simpler to query.
Operating without schema design produces unstructured data that is difficult to query consistently. Schema design documents relationships and provides query optimization opportunities. Structured schemas enable efficient query execution.
Single flat files denormalize to the extreme, duplicating dimension data in every fact record. This maximizes storage waste while providing no query benefits over properly designed star schemas. Flat files also complicate updates to dimension attributes.
Question 148
A data pipeline must coordinate execution across multiple AWS accounts. What service provides cross-account orchestration?
A) AWS Step Functions with cross-account IAM role assumption
B) Separate uncoordinated jobs in each account
C) Manual email coordination
D) No cross-account orchestration
Answer: A
Explanation:
AWS Step Functions can assume IAM roles in other AWS accounts to invoke services like Lambda, Glue, or EMR across account boundaries. Workflow state machines orchestrate multi-account processes by assuming appropriate roles for each step. This enables centralized orchestration of distributed resources.
Cross-account roles are configured with trust policies allowing the orchestrator account to assume them. Each role grants permissions to invoke specific services in its account. Step Functions transitions between roles as workflows execute across accounts, maintaining cohesive end-to-end orchestration.
This architecture supports organizational structures where different teams own different AWS accounts but workflows span multiple teams. Centralized orchestration provides visibility and coordination while respecting account boundaries and governance policies.
Running separate uncoordinated jobs in each account eliminates the ability to manage dependencies and sequence operations. Cross-account workflows require coordination to ensure tasks execute in the correct order. Uncoordinated execution leads to race conditions and failures.
Manual email coordination does not scale and introduces human delays in automated workflows. Humans become bottlenecks in processes that should be fully automated. Cross-account orchestration must be programmatic for reliability and efficiency.
Operating without cross-account orchestration capabilities means multi-account workflows are impossible or require complex custom coordination logic. Step Functions provides native cross-account capabilities that simplify multi-account architectures.
Question 149
A data engineer needs to ensure that data quality metrics are accessible to business users through dashboards. What approach enables this visibility?
A) Publish quality metrics to CloudWatch custom metrics and display in dashboards
B) Keep quality metrics hidden
C) Only share metrics verbally
D) No quality visibility for users
Answer: A
Explanation:
CloudWatch custom metrics store data quality measurements like null percentages, validation failure counts, and completeness scores. Metrics are published from Glue jobs or Lambda functions after quality checks. CloudWatch dashboards visualize metrics with trend lines, thresholds, and alerts visible to business users.
Alternatively, QuickSight dashboards can query quality metrics stored in databases or S3, providing rich interactive visualizations. Business users can drill into quality trends, compare quality across datasets, and correlate quality with business outcomes. Transparency builds trust and accountability.
Visibility into quality metrics enables proactive management. Business users understand data reliability and can factor quality into decision confidence. When quality degrades, visible metrics trigger investigation before downstream impacts occur.
Keeping quality metrics hidden from business users prevents them from understanding data reliability. Users make decisions based on data without knowing if it’s trustworthy. Transparency about quality is essential for responsible data use.
Sharing metrics only verbally is informal, inconsistent, and does not scale. Verbal communication lacks the detail and historical context that dashboards provide. Systematic, persistent visibility through dashboards is required.
Providing no quality visibility treats data quality as a technical concern rather than a business priority. Quality directly impacts business outcomes and requires business visibility and engagement. Metrics must be accessible to all stakeholders.
Question 150
A company wants to minimize Athena query costs for dashboards that run the same queries repeatedly. What optimization reduces costs?
A) Enable query result caching in Athena
B) Disable all caching
C) Re-scan data every time
D) Ignore cost optimization
Answer: A
Explanation:
Athena automatically caches query results for 24 hours. When identical queries execute within the cache window, Athena returns cached results without re-scanning S3 data. This eliminates data scanning costs and reduces query latency from seconds to milliseconds.
For dashboards refreshing hourly with the same underlying queries, result caching can reduce costs by 95%+. The first execution scans data and incurs costs; subsequent executions use cached results for free. Cache effectiveness increases with query repetition frequency.
Query results cache automatically without configuration and applies to all queries. Athena determines cache hits based on query text, input data versions, and catalog metadata. Even slight query modifications may miss the cache, so parameterized queries should be structured consistently.
Disabling caching (which isn’t actually possible, but conceptually) would force every query to re-scan S3 data, maximizing costs and latency. Caching provides free optimization that should always be leveraged. There’s no reason to avoid this automatic benefit.
Re-scanning data every time wastes money and time. Caching recognizes that query results haven’t changed when underlying data hasn’t changed. Leveraging cached results is an essential optimization for repeated queries.
Ignoring cost optimization opportunities leads to unnecessarily high AWS bills. Athena charges by data scanned, so optimizations like caching, partitioning, and columnar formats directly reduce costs. Cost consciousness is essential for efficient operations.
Question 151
A data pipeline must process XML files stored in S3. AWS Glue does not natively support XML. What approach enables XML processing?
A) Use custom Python libraries in Glue to parse XML into structured format
B) Process XML without parsing
C) Reject all XML files
D) Store XML as binary without transformation
Answer: A
Explanation:
AWS Glue supports custom Python code using libraries like xml.etree.ElementTree or lxml to parse XML documents. Glue jobs read XML files from S3, parse them into Python dictionaries or Spark DataFrames, and then write to structured formats like Parquet or JSON for downstream processing.
Custom XML parsing logic can be packaged as Python modules and referenced in Glue job scripts. The parsing code extracts elements and attributes from XML structure, flattening nested hierarchies into tabular formats. This transformation enables analytical queries on data originally in XML.
For complex XML schemas, consider preprocessing with AWS Lambda to convert XML to JSON before Glue processing. Lambda can use XML parsing libraries to transform files as they arrive in S3, triggering on S3 events. Glue then processes the JSON files using native support.
Processing XML without parsing treats it as opaque text, preventing access to structured data within. XML exists to represent structured data, and parsing is required to extract that structure. Unparsed XML is useless for analytics.
Rejecting XML files when they contain necessary business data is not viable. Source systems may only provide XML output. Data pipelines must handle whatever formats source systems produce, including XML.
Storing XML as binary without transformation preserves data but makes it unqueryable. Analytics requires structured data formats. XML must be parsed and transformed to enable SQL queries and analysis.
Question 152
A data engineer needs to implement slowly changing dimension Type 1 logic that overwrites historical values. What approach implements this?
A) Use UPSERT/MERGE operations to update existing records in place
B) Create new records for every change
C) Never update dimension records
D) Delete and recreate tables
Answer: A
Explanation:
Type 1 slowly changing dimensions overwrite existing attribute values when changes occur, maintaining only current state without history. UPSERT (INSERT or UPDATE) operations check if a record with the business key exists. If found, update attributes; if not found, insert a new record.
In Redshift, MERGE statements efficiently implement Type 1 logic by comparing staging tables with dimension tables. Matching records are updated with new values; unmatched records are inserted. This single statement handles both insert and update logic atomically.
Type 1 is simpler than Type 2 and appropriate when historical values are not needed. For reference data like product categories or organizational hierarchies where only current state matters, Type 1 provides adequate tracking without storage overhead.
Creating new records for every change implements Type 2, not Type 1 logic. Type 2 preserves history while Type 1 deliberately overwrites it. The choice depends on whether historical values provide analytical value.
Never updating dimension records leaves data stale and inaccurate. Dimensions must reflect current reality for accurate analytics. The question is whether to preserve history (Type 2) or overwrite it (Type 1), not whether to update at all.
Deleting and recreating tables is disruptive and breaks referential integrity with fact tables. UPSERT operations update tables in place without downtime or breaking relationships. Table recreation is unnecessary and harmful.
Question 153
A company stores application logs in CloudWatch Logs and needs to analyze them using SQL. What AWS service enables SQL querying of CloudWatch Logs?
A) CloudWatch Logs Insights or export logs to S3 and query with Athena
B) CloudWatch Logs has no query capability
C) Manual log reading only
D) Delete logs before querying
Answer: A
Explanation:
CloudWatch Logs Insights provides a purpose-built query language for analyzing log data directly in CloudWatch. While not standard SQL, it offers SQL-like syntax for filtering, aggregating, and analyzing logs. Insights queries execute directly against log streams without exporting data.
For true SQL access, export CloudWatch Logs to S3 using export tasks or subscriptions that stream logs continuously. Once in S3, Athena can query logs using standard SQL. External tables in Athena’s Data Catalog reference S3 log locations, enabling SQL analytics.
Logs Insights is optimal for interactive, ad-hoc log analysis with results in seconds. S3 export with Athena is better for complex analytics, long-term retention, and integration with other S3-based data. Both approaches enable programmatic log analysis.
CloudWatch Logs provides extensive query capabilities through Insights and export options. The service is designed for operational monitoring and troubleshooting through queryable log data. Claiming no capability exists ignores core service features.
Manual log reading does not scale beyond small volumes and cannot perform the aggregations, filtering, and pattern detection that query languages provide. Automated querying is essential for production log analysis.
Deleting logs before querying obviously prevents analysis. Logs exist to provide visibility into system behavior. Querying requires log retention, not deletion.
Question 154
A data pipeline processes personally identifiable information and must ensure data is encrypted throughout its lifecycle. What encryption strategy provides comprehensive protection?
A) Encryption at rest, in transit, and in processing with key management
B) No encryption anywhere
C) Encrypt only during storage
D) Use plaintext for all processing
Answer: A
Explanation:
Comprehensive encryption requires protecting data at rest (storage), in transit (network), and optionally during processing. S3 server-side encryption protects data at rest. TLS/SSL protects data in transit between services. Processing in encrypted memory or using services that support encryption during processing provides complete lifecycle protection.
AWS KMS manages encryption keys with automatic rotation, audit logging, and granular access controls. Services like Glue, EMR, and Redshift integrate with KMS to encrypt data throughout processing. Keys are never exposed to users, maintaining security while enabling encrypted operations.
End-to-end encryption ensures that even if one layer is compromised, data remains protected by other layers. Defense-in-depth through multiple encryption layers provides robust protection for sensitive data subject to regulations like GDPR or HIPAA.
Operating without encryption exposes PII to unauthorized access at multiple points. Data breaches can occur in storage, during network transmission, or in processing memory. Comprehensive encryption protects against all these vectors.
Encrypting only during storage leaves data vulnerable during transmission and processing. Network interception or memory dumps could expose plaintext PII. All lifecycle stages require protection for sensitive data.
Using plaintext for processing defeats the purpose of encryption and exposes data during transformations. Modern processing frameworks support encrypted operations, enabling transformation of encrypted data without decryption to plaintext.
Question 155
A data engineer needs to implement incremental data loading from a database that does not have change data capture. What pattern enables incremental loading?
A) Track maximum timestamp or ID and query for records greater than last maximum
B) Always load complete table each time
C) Random record selection
D) No incremental loading possible
Answer: A
Explanation:
High-water mark pattern stores the maximum value of a timestamp or auto-incrementing ID column from the last successful load. Each load queries for records where the timestamp/ID exceeds the stored maximum. After successful processing, update the high-water mark to the new maximum.
Control tables in DynamoDB, RDS, or parameter stores maintain high-water marks for each source table. Glue jobs query the control table to retrieve the last maximum, use it in WHERE clauses to fetch new records, and update the mark upon successful completion.
This pattern assumes that timestamps or IDs are monotonically increasing and reliably indicate new records. For updated records without timestamp tracking, this approach only captures inserts, not updates. For full change tracking, database-level change data capture is needed.
Always loading complete tables wastes processing time and bandwidth transferring unchanged data. As tables grow to millions of rows, full loads become impractical. Incremental loading scales efficiently by transferring only new or changed data.
Random record selection provides no guarantee of capturing all new records or avoiding duplicates. Systematic incremental loading based on timestamps or IDs ensures completeness and correctness.
Incremental loading is always possible with proper tracking mechanisms. High-water marks enable efficient incremental loading even without database CDC capabilities. The pattern is widely used and proven effective.
Question 156
A company wants to enable self-service data preparation for business analysts without requiring coding skills. What AWS service provides visual data preparation?
A) AWS Glue DataBrew
B) Manual coding in Python
C) Command-line ETL scripts
D) No self-service options
Answer: A
Explanation:
AWS Glue DataBrew provides a visual interface for data preparation where analysts can clean, normalize, and transform data using point-and-click operations. Over 250 pre-built transformations handle common tasks like removing duplicates, filling missing values, and standardizing formats without code.
DataBrew allows analysts to profile data, visualizing distributions, missing values, and data quality issues. Interactive transformations show immediate previews of results. Once recipes are finalized, they can be scheduled to run automatically on new data or integrated into production pipelines.
Self-service data preparation democratizes data work, reducing bottlenecks where analysts wait for engineers to write transformations. Analysts who understand business logic can implement transformations directly. Engineers focus on complex processing while analysts handle routine preparation.
Requiring manual Python coding limits data preparation to those with programming skills, excluding many business analysts. Code-based approaches create dependencies on technical staff for simple transformations that analysts could do themselves with visual tools.
Command-line ETL scripts are even less accessible than Python, requiring terminal expertise and scripting knowledge. CLI tools are powerful for automation but inappropriate for interactive, exploratory data preparation by non-technical users.
Claiming no self-service options exist ignores AWS services specifically designed for this purpose. DataBrew and similar tools enable self-service data preparation across organizations.
Question 157
A data pipeline must ensure that data loaded into Redshift matches the schema defined in the Glue Data Catalog. What validation prevents schema mismatches?
A) Compare incoming data schema against catalog schema before loading
B) Load data without schema validation
C) Ignore schema definitions
D) Assume all data matches schema
Answer: A
Explanation:
Schema validation compares the structure of incoming data against the expected schema defined in the Glue Data Catalog. Validation checks column names, data types, nullability, and ordering. Mismatches are logged and the load is rejected or corrected before reaching Redshift.
AWS Glue jobs can retrieve table definitions from the Data Catalog and validate incoming data against them. Data type incompatibilities (e.g., string data in an integer column) are detected early. This prevents load failures in Redshift and protects data integrity.
Schema validation enables early error detection where problems are identified during ETL rather than during Redshift COPY operations. Early detection provides better error messages and prevents partial loads that corrupt Redshift tables. Validation is a quality gate protecting downstream systems.
Loading data without schema validation risks runtime failures when data doesn’t match table definitions. Redshift COPY operations fail when data types mismatch, requiring troubleshooting and reload. Proactive validation prevents these failures.
Ignoring schema definitions when schemas exist for quality control is reckless. Schemas document expected structure and enable automated validation. Ignoring them eliminates these benefits and leads to data quality issues.
Assuming all data matches schemas without verification is wishful thinking. Source systems change, bugs introduce unexpected values, and schemas evolve. Validation verifies assumptions rather than blindly trusting them.
Question 158
A data engineer needs to implement data lineage tracking that shows how datasets are derived from sources through transformations. What AWS service provides automatic lineage capture?
A) AWS Glue with automatic lineage tracking in Data Catalog
B) Manual documentation in spreadsheets
C) No lineage tracking
D) Verbal communication only
Answer: A
Explanation:
AWS Glue automatically captures data lineage when ETL jobs run. Lineage metadata tracks which source tables were read, what transformations were applied, and which target tables were written. This information is stored in the Glue Data Catalog and accessible through APIs and console visualizations.
Lineage tracking enables impact analysis where you can determine which downstream datasets are affected by changes to upstream sources. For troubleshooting data quality issues, lineage traces problems back to their origin. For compliance, lineage demonstrates how sensitive data flows through systems.
Glue lineage integrates with AWS Lake Formation, providing end-to-end visibility across data lake architectures. Lineage graphs show complete data flows from ingestion through multiple transformation stages to consumption. This transparency is essential for governance and operations.
Manual documentation in spreadsheets becomes outdated quickly as pipelines evolve. Manual processes cannot capture the detail that automated lineage provides. Spreadsheets also aren’t queryable programmatically for automated impact analysis.
Operating without lineage tracking makes troubleshooting extremely difficult and impact analysis impossible. When data issues arise, you cannot trace them to their source. When sources change, you cannot identify affected downstream datasets. Lineage is essential for operational efficiency.
Verbal communication of lineage is informal, inconsistent, and doesn’t scale. Lineage information must be systematically captured and programmatically accessible. Verbal communication cannot support the automated workflows that lineage enables.
Question 159
A company uses AWS Glue to process sensitive healthcare data. What configuration ensures Glue jobs run in compliance with HIPAA requirements?
A) Enable encryption, use VPC endpoints, and sign BAA with AWS
B) Use public endpoints without encryption
C) Disable all security controls
D) No special configuration needed
Answer: A
Explanation:
HIPAA compliance requires encryption of Protected Health Information at rest and in transit. Enable S3 encryption for job inputs/outputs, encrypt Glue Data Catalog, and use encrypted connections between services. VPC endpoints ensure traffic doesn’t traverse the public internet.
AWS Glue is a HIPAA-eligible service when properly configured. Organizations must sign a Business Associate Agreement (BAA) with AWS. Jobs must run in VPCs with security groups restricting access. CloudTrail logging must be enabled for audit trails of data access.
Additional requirements include access controls through IAM limiting who can run jobs and access data, encryption key management through KMS, and compliance monitoring. HIPAA technical safeguards are implemented through AWS service configurations, but customers remain responsible for proper configuration.
Using public endpoints without encryption violates HIPAA encryption requirements. PHI transmitted over public internet without encryption exposes data to interception. Private connectivity through VPC endpoints is required.
Disabling security controls makes HIPAA compliance impossible. HIPAA Security Rule mandates specific technical safeguards including encryption, access controls, and audit logging. Disabling these violates requirements.
HIPAA compliance requires extensive configuration and organizational measures. Claiming no special configuration is needed ignores regulatory requirements. HIPAA-eligible services require proper configuration to achieve actual compliance.
Question 160
A data pipeline experiences occasional failures due to temporary service unavailability. What AWS service feature provides built-in retry logic?
A) AWS Step Functions with automatic retry and backoff configuration
B) No retry mechanism
C) Manual retry only
D) Immediate failure without retry
Answer: A
Explanation:
AWS Step Functions provides configurable retry policies for each state in a workflow. Retry configuration specifies which errors trigger retries, how many attempts to make, and backoff intervals between attempts. Exponential backoff with jitter prevents thundering herd problems.
Retries handle transient failures from temporary service unavailability, network issues, or throttling. Step Functions automatically retries failed operations without manual intervention. Retry policies distinguish between retriable errors (e.g., throttling) and permanent errors (e.g., invalid input) that should not be retried.
Built-in retry logic simplifies pipeline development by centralizing error handling in workflow definitions rather than implementing retries in each job. Consistent retry behavior across all pipeline stages reduces code duplication and ensures reliable failure handling.
Operating without retry mechanisms means transient failures cause permanent pipeline failures. Temporary issues that would resolve with retry instead require manual intervention to restart pipelines. This increases operational burden and reduces reliability.
Manual retry requires human intervention, introducing delays and preventing true automation. Pipelines should handle transient failures automatically. Manual processes only make sense for permanent failures requiring investigation and correction.
Immediate failure without retry treats all errors as permanent, reducing system reliability. Transient errors are common in distributed systems and should be automatically retried. Immediate failure significantly increases operational workload.
