AWS Certified Data Analytics - Specialty (DAS-C01)
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Free AWS Certified Data Analytics - Specialty (DAS-C01) Questions
A streaming application needs exactly-once delivery for financial transactions. What ensures no duplicate processing?
- A) At-least-once delivery accepting duplicates
- B) Best-effort delivery
- C) Idempotent consumers with DynamoDB deduplication tracking transaction IDs
- D) Manual deduplication
Explanation
Implement exactly-once: assign unique transaction IDs, use DynamoDB to track processed IDs with conditional writes for atomic check-and-process. Before processing, check if transaction_id exists; if yes skip (duplicate), if no process and insert ID. This guarantees each transaction processes exactly once despite retries.
Correct Answer Is:
C) Idempotent consumers with DynamoDB deduplication tracking transaction IDs
A streaming application needs durable state surviving restarts. What provides persistence?
- A) Local memory
- B) No persistent state
- C) Flink managed state with checkpointing to S3
- D) Manual database state
Explanation
Flink managed state automatically checkpointed to S3. Checkpoint interval configurable (balance durability vs overhead). On failure/restart, state is restored from the latest checkpoint. Savepoints for manual snapshots. State includes: session windows, aggregations, ML models. Provides transparent recovery with exactly-once guarantees without data loss.
Correct Answer Is:
C) Flink managed state with checkpointing to S3
An organization needs multi-dimensional streaming aggregations. What handles complex aggregations?
- A) CUBE/ROLLUP operations or multi-dimensional Flink windowing
- B) Single dimension only
- C) No multi-dimensional support
- D) Manual aggregation
Explanation
SQL CUBE operator generates aggregations across all dimension combinations. ROLLUP for hierarchical aggregations. Flink multi-dimensional keyed windows. GROUP BY CUBE(region, product, channel) produces all sub-aggregations. Enables OLAP-style analysis on streams with drill-down capabilities.
Correct Answer Is:
A) CUBE/ROLLUP operations or multi-dimensional Flink windowing
Your data pipeline has variable workload patterns—sometimes idle, sometimes heavy processing. What serverless approach optimizes costs?
- A) Fixed-size EMR cluster running 24/7
- B) Manually sized infrastructure over-provisioned for peak
- C) Over-provisioned resources for worst-case
- D) Lambda for lightweight tasks or AWS Glue for larger datasets with automatic scaling
Explanation
Serverless processing eliminates over-provisioning for variable workloads: (1) AWS Lambda for lightweight processing (files <6 MB, execution <15 minutes)—triggers on S3 events, auto-scales to process thousands of files concurrently, pay only for execution time (100ms granularity), (2) AWS Glue for larger datasets—Serverless Spark-based ETL, automatic scaling based on data volume and DPU (Data Processing Unit) requirements, job bookmarks for incremental processing, pay per minute per DPU. Both charge only during actual processing—zero cost during idle periods. Fixed EMR clusters waste resources during idle periods (paying for unused capacity). Manual sizing requires capacity planning and doesn't adapt dynamically to workload changes. Over-provisioning dramatically increases costs. Serverless benefits: zero infrastructure management, automatic scaling (Lambda: 0 to thousands of concurrent executions instantly; Glue: dynamic resource allocation), pay-per-use pricing (no charges during idle), built-in fault tolerance. Choose Lambda for: simple transformations, event-driven processing, rapid response to individual files. Choose Glue for: complex transformations requiring Spark, processing large files (GBs), distributed processing across multiple files. Essential for unpredictable or bursty workloads where serverless economics provide dramatic cost savings (50-90% reduction) compared to always-on infrastructure by eliminating idle capacity costs while maintaining performance through elastic scaling.
Correct Answer
D) Lambda for lightweight tasks or AWS Glue for larger datasets with automatic scaling
A streaming application needs complex pattern matching detecting event sequences. What enables pattern detection?
- A) Simple filtering only
- B) Complex Event Processing (CEP) with Flink pattern API
- C) No pattern matching
- D) Manual log analysis
Explanation
Flink CEP library defines patterns as state machines. Pattern specification with sequences, conditions, temporal constraints. Example: login → purchase within 10min → logout. CEP operators: begin(), next(), followedBy(), within(), where(). Detects significant event sequences triggering alerts or actions.
Correct Answer Is:
B) Complex Event Processing (CEP) with Flink pattern API
A data pipeline needs automated recovery from failures without manual intervention. What provides self-healing?
- A) Fail requiring manual restart
- B) No error handling
- C) Automatic retry with exponential backoff and DLQ
- D) Hope-based reliability
Explanation
Automatic retry with exponential backoff (1s, 2s, 4s, 8s). Maximum retry limits (e.g., 3 attempts). Dead-Letter Queues for persistent failures. Circuit breaker pattern. CloudWatch alarms on DLQ depth. Step Functions retry with BackoffRate. SQS maxReceiveCount with redrive policy. Reduces MTTR through automated recovery from transient failures.
Correct Answer Is:
C) Automatic retry with exponential backoff and DLQ
A data warehouse needs independent compute/storage scaling. What architecture enables separation?
- A) Redshift RA3 with managed storage or Redshift Serverless
- B) Traditional coupled
- C) Single-node
- D) Cannot separate
Explanation
Redshift RA3 instances use Redshift Managed Storage (S3-backed). Compute nodes cache hot data; full dataset in S3. Enables: resize cluster without moving data, scale storage independently, pause cluster retaining data, multiple clusters sharing data. Redshift Serverless automatically scales compute (RPU) independent of storage. Optimizes costs.
Correct Answer Is:
A) Redshift RA3 with managed storage or Redshift Serverless
A company analyzes logs in S3 buckets across multiple accounts with centralized processing. What enables cross-account access?
- A) Replicate all data
- B) S3 bucket policies with cross-account IAM roles for Glue/EMR/Athena
- C) Manual copying
- D) Public buckets
Explanation
Configure cross-account access: data owner accounts create bucket policies granting GetObject/ListBucket to processing account's IAM role. Processing account creates IAM role for Glue/EMR/Athena. CloudTrail logs access. Data stays in source accounts maintaining sovereignty while enabling centralized analytics. Avoids replication costs and staleness.
Correct Answer Is:
B) S3 bucket policies with cross-account IAM roles for Glue/EMR/Athena
A data pipeline serves diverse consumers needing different formats. What multi-format approach works?
- A) Single format for all
- B) Multi-output ETL writing JSON, Parquet, Avro to different S3 prefixes
- C) Consumers transform themselves
- D) No format flexibility
Explanation
Glue ETL with multiple output writers produces different formats from single processing job: JSON to /json/ (applications), Parquet to /parquet/ (analytics), Avro to /avro/ (streaming). Separate Catalog tables per format. Consumers use optimal format reducing duplicate transformations.
Correct Answer Is:
B) Multi-output ETL writing JSON, Parquet, Avro to different S3 prefixes
Your organization needs comprehensive encryption for a data lake containing sensitive PII data to meet compliance requirements. What encryption strategy provides defense-in-depth?
- A) S3 default encryption only
- B) S3 bucket policies preventing non-HTTPS access
- C) S3 SSE-KMS with customer managed keys, enforced HTTPS, EMR/Redshift/Athena encryption, and CloudTrail key usage logging
- D) Client-side encryption exclusively
Explanation
Implement comprehensive encryption strategy: (1) S3 SSE-KMS (Server-Side Encryption with AWS Key Management Service) using customer managed keys for data at rest with granular access control and audit trails, (2) enforce HTTPS for data in transit using bucket policies denying non-SSL requests, (3) enable encryption for all processing services—EMR cluster encryption (at-rest and in-transit), Redshift cluster encryption with KMS, Athena query results encryption, (4) CloudTrail logging all KMS key usage for compliance auditing. SSE-KMS benefits over SSE-S3: separate key permissions from data permissions, comprehensive audit trails via CloudTrail, key rotation capabilities, ability to disable keys immediately. Bucket policy example: {"Condition": {"Bool": {"aws:SecureTransport": "false"}}, "Effect": "Deny"}. EMR encryption: LUKS for EBS volumes, encryption zones in HDFS. Client-side encryption alone doesn't protect during processing. Essential for regulated data (HIPAA, PCI-DSS, GDPR) requiring end-to-end encryption with audit capabilities where defense-in-depth approach ensures data protected throughout lifecycle—at rest, in transit, and during processing—with comprehensive key usage logging meeting stringent compliance requirements.
Correct Answer
C) S3 SSE-KMS with customer managed keys, enforced HTTPS, EMR/Redshift/Athena encryption, and CloudTrail key usage logging
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