AWS DAS-C01 Free Practice Questions — Page 2

Correct Answer: C. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.

Why C is correct: Amazon Kinesis Data Firehose is purpose-built for streaming Amazon Connect data (call records, agent events) directly to S3 with automatic batching, compression, and transformation capabilities - all fully managed. Amazon AppFlow is specifically designed for SaaS application integration, including native Salesforce connectivity with pre-built connectors, automatic schema detection, and data transfer directly to S3. Both services are fully managed with minimal operational overhead. Why others are wrong: A Kinesis Data Streams requires additional consumers and code to write to S3, adding operational overhead B Kinesis Data Streams doesn't natively ingest Salesforce data D AppFlow doesn't directly support Amazon Connect; Firehose is the native integration

Correct Answer: A. Enable concurrency scaling in the workload management (WLM) queue.

Why A is correct: Concurrency scaling in Amazon Redshift automatically adds transient cluster capacity when queries are queued, specifically designed for read-heavy workloads with periodic spikes. It handles the morning query surge automatically without downtime, and you only pay for the additional capacity when it's actually used (per-second billing for concurrency scaling clusters). This directly addresses the queuing issue with minimal cost and zero downtime. Why others are wrong: B requires manual intervention during peak hours and doesn't avoid downtime during resize operations; distribution style ALL is also not cost-effective for 500 TB C elastic resize requires brief downtime (usually minutes) which violates the "avoid any downtime" requirement D snapshot, restore, and resize involves significant downtime during the switch

Correct Answers: B. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.; C. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.

Why B is correct: Apache Parquet is a columnar storage format that significantly improves Athena query performance through better compression and column pruning. By extracting and converting daily, you maintain updated data in an optimized format, reducing query latency dramatically compared to CSV files. Why C is correct: This solution combines two optimizations: converting to Parquet format (columnar, compressed) and partitioning the data (allowing partition pruning to scan less data). The AWS Glue crawler automatically discovers new partitions daily, keeping the catalog updated. This addresses both the format inefficiency and the growing data volume through partitioning, providing the best long-term query performance improvement. Why others are wrong: A using MySQL Workbench doesn't improve underlying query performance; it's just a different client D & E while compression helps, gzip and lzo don't provide the same performance benefits as columnar Parquet format, and they don't address the growing data volume through partitioning

Correct Answers: A. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.; C. The marketing department creates an IAM role that has permissions to the Lake Formation tables.

Why A is correct: Lake Formation uses a resource sharing model where the data owner (finance department) must explicitly grant permissions on specific tables to external AWS accounts. This Lake Formation grant is essential for cross-account data access in a Lake Formation-managed environment. Why C is correct: The marketing department needs an IAM role with appropriate permissions to assume and access the shared Lake Formation tables. This role acts as the identity that will access the finance department's data through Lake Formation's permission model. Why B is wrong: While IAM permissions are needed, Lake Formation uses its own permission model for table-level access control, not direct cross-account IAM table permissions. The proper approach is Lake Formation grants plus IAM roles, not pure IAM permissions to tables.

Correct Answer: A. Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

Why A is correct: This solution leverages AWS serverless services for minimal operational overhead. AWS Glue crawler automatically discovers the schema of WAF logs in S3 and creates/updates tables in the Glue Data Catalog. Athena provides ad-hoc SQL querying against S3 data with pay-per-query pricing (low cost for infrequent analysis). QuickSight connects directly to Athena for visualization with minimal development - just point and click dashboard creation. This is the most cost-effective and low-effort solution. Why others are wrong: B requires managing an OpenSearch cluster which adds operational overhead and continuous costs even when not querying C requires Lambda development for CSV conversion and managing a Redshift cluster (high operational overhead and cost) D requires managing an EMR cluster and developing Spark jobs, adding significant operational complexity and cost