DEA-C01 Data Store Management Practice Test 42 — 15 Questions

Question 1

A data engineer needs to store large volumes of infrequently accessed compliance data in Amazon S3 for 10 years. The data must be retrievable within 12 hours if required for audits. The engineer wants the most cost-effective storage solution. Which S3 storage class should be used?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

S3 Glacier Deep Archive. The scenario requires long-term retention (10 years) with infrequent access and retrieval within 12 hours. S3 Glacier Deep Archive is specifically designed for this use case, offering the lowest storage cost among S3 classes while providing standard retrieval within 12 hours. It is the most cost-effective choice for compliance data that is rarely accessed.

Answer

S3 Glacier Instant Retrieval

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S3 Standard-Infrequent Access (S3 Standard-IA)

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S3 Glacier Flexible Retrieval

Question 2

A data engineer is optimizing an Amazon S3 data lake for cost and performance. The data lake contains large volumes of CSV files that are queried by Amazon Athena. The engineer wants to reduce query costs and improve query performance. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Partition the data by a frequently filtered column, such as date.. Converting CSV to Parquet and partitioning the data by a frequently filtered column are the two most effective actions to reduce Athena query costs and improve performance. Parquet reduces scanned data through columnar compression and predicate pushdown, while partitioning limits the data scanned to relevant partitions. The other actions do not affect query execution or data scanned.

Answer

Enable S3 server access logging on the bucket.

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Enable S3 Transfer Acceleration on the bucket.

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Increase the number of Athena workgroups.

Question 3

A financial services company stores transaction records in an Amazon DynamoDB table. An audit requires that all data older than 7 years be automatically and permanently deleted. The data engineering team must implement this with minimal operational overhead and no application code changes. What should the team do?

Accepted Answer

Configure a TTL attribute on the table with an expiry timestamp set to 7 years from the transaction date.. DynamoDB TTL is the native, serverless mechanism for automatic item expiration. By storing an epoch timestamp attribute set to the transaction date plus seven years, DynamoDB deletes expired items in the background without consuming write capacity or requiring custom code. It directly satisfies the audit's permanent deletion requirement with minimal operational effort, unlike stream-based, step-function, or restore-based approaches.

Answer

Enable point-in-time recovery and restore the table to a point before the 7-year window, then delete the original table.

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Create a scheduled Amazon EventBridge rule that invokes an AWS Step Functions state machine to scan and delete old items.

Answer

Enable DynamoDB Streams and write an AWS Lambda function that deletes items older than 7 years.

Question 4

A data engineer is managing an Amazon Redshift cluster that experiences performance degradation during peak hours. The cluster uses a single node type and has no concurrency scaling enabled. Analysis shows that many long-running queries are queued behind short ad-hoc queries, causing delays for critical reports. The engineer needs to ensure that critical reports run promptly without affecting ad-hoc queries. Which solution meets these requirements?

Accepted Answer

Create a workload management (WLM) queue with a higher priority for critical reports and assign them to that queue.. Amazon Redshift workload management (WLM) enables you to define queues with different priorities. By routing critical reports to a high-priority queue, they are scheduled ahead of ad-hoc queries, ensuring timely execution. This approach directly addresses the need to prioritize specific workloads without impacting others.

Answer

Use short query acceleration (SQA) to run short queries in a dedicated space.

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Increase the cluster size by adding more nodes to reduce overall query time.

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Enable concurrency scaling to automatically add transient clusters for queued queries.

Question 5

A media company ingests millions of small JSON files per day into an Amazon S3 bucket. Analysts run Amazon Athena queries over this data and report that each query scans far more data than the files matching their filters, resulting in high cost and slow performance. The files are partitioned by year/month/day in S3. What should a data engineer do to reduce the data scanned per query?

Accepted Answer

Convert the JSON files to Apache Parquet, store them in the existing partition structure, and define the table in the AWS Glue Data Catalog with the correct partition columns.. Athena's cost model is based on bytes scanned, so the most effective optimization is to store data in a columnar format such as Parquet and rely on partition pruning. Parquet enables column projection and predicate pushdown, while year/month/day partitions let Athena skip entire prefixes. Together they sharply reduce scanned bytes, cutting cost and improving query speed for the analyst workload.

Answer

Move the files into an Amazon Redshift cluster using COPY and query them with Redshift Spectrum.

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Create an Amazon CloudFront distribution in front of the S3 bucket and run Athena against the distribution.

Answer

Enable S3 Transfer Acceleration on the bucket and increase the Athena query timeout.

Question 6

A data engineer is using AWS Glue to catalog data stored in Amazon S3. The engineer needs to run an AWS Glue ETL job that reads from a large dataset in Parquet format, performs transformations, and writes the output to Amazon Redshift. The job must handle data skew and optimize performance. Which AWS Glue feature should the engineer use to address data skew during the join operation?

Accepted Answer

Configure the job to use the `spark.sql.adaptive.enabled` and `spark.sql.adaptive.skewJoin.enabled` properties.. Data skew in joins can be mitigated by enabling Spark's adaptive query execution (AQE) with skew join optimization. This feature dynamically detects and splits skewed partitions, balancing the workload across executors. Other options do not directly address skew: job bookmarks track processed data, Relationalize flattens nested data, and adding DPUs increases capacity without redistributing data.

Answer

Use the AWS Glue DynamicFrame and apply the `Relationalize` transform to flatten nested data.

Answer

Enable job bookmarks to track processed data and avoid reprocessing.

Answer

Increase the number of DPUs allocated to the job to provide more resources for handling skewed data.

Question 7

A data engineer manages an Amazon Redshift cluster that runs a nightly ETL load followed by complex analytical queries. Users report that queries during the day are slower than expected, and the team wants to isolate the ETL workload so it cannot consume resources needed by the analytical queries. The cluster uses provisioned nodes. What is the MOST appropriate solution?

Accepted Answer

Create a separate Redshift workload management (WLM) queue for the ETL role and assign the analytical queries to a different queue.. Redshift WLM provides workload isolation by letting you define multiple queues, each with its own memory allocation and query concurrency, and by routing queries to queues based on user groups or query groups. Placing the ETL job in a dedicated queue ensures it cannot consume the memory and slots reserved for analytical queries, which addresses the slowdowns without adding clusters or nodes.

Answer

Increase the number of nodes in the cluster to provide more resources for all workloads.

Answer

Enable concurrency scaling on the cluster and route all queries through it.

Answer

Move the ETL workload to a separate Redshift cluster and use Amazon Redshift Spectrum for analytics.

Question 8

A data engineer is configuring an Amazon S3 bucket that stores sensitive customer records for analytics. The security team requires that all data be encrypted at rest with keys that are rotated automatically every year and that access be auditable per key. The engineer must minimize operational overhead. Which encryption configuration should be used?

Accepted Answer

SSE-KMS with a customer managed key with automatic rotation enabled. The requirement combines automatic annual key rotation, per-key auditability, and minimal operational overhead. A KMS customer managed key with automatic rotation enabled provides all three: S3 encrypts objects server-side, CloudTrail records key usage, and rotation occurs yearly without manual intervention. AWS managed keys rotate on a different schedule and offer less policy control, while SSE-S3 and SSE-C lack the needed audit or rotation behavior.

Answer

SSE-S3 with bucket key enabled

Answer

SSE-C with customer-provided keys stored in AWS Secrets Manager

Answer

SSE-KMS with an AWS managed key (aws/s3)

Question 9

A company stores application logs in an Amazon S3 bucket. A compliance policy states that log objects must be retained for exactly 90 days and then permanently deleted, and that no one, including administrators, should be able to delete them earlier. The data engineer must enforce this with the least effort. What should the engineer do?

Accepted Answer

Apply an S3 Object Lock retention period of 90 days in compliance mode to the bucket, and configure a lifecycle rule to expire objects after 90 days.. S3 Object Lock in compliance mode provides WORM protection that even the root user cannot override before the retention period ends, which satisfies the immutability requirement. Pairing it with a lifecycle rule that expires objects after 90 days ensures automatic permanent deletion at the required time. Together they enforce the compliance policy with minimal operational effort.

Answer

Create an S3 Lifecycle rule that transitions objects to S3 Glacier Deep Archive after 90 days.

Answer

Use AWS Backup to create a vault with a 90-day retention and a vault lock in compliance mode.

Answer

Enable S3 Versioning and add a bucket policy that denies s3:DeleteObject to all principals.

Question 10

A data engineer needs to store large volumes of semi-structured JSON data in Amazon S3 and query it with Amazon Athena. The engineer wants to minimize query costs and improve performance. Which action should be taken?

Accepted Answer

Convert the data to columnar format like Apache Parquet and partition it.. Using a columnar format like Parquet with partitioning allows Athena to scan only the necessary columns and partitions, drastically reducing the amount of data scanned and thus the cost. This is the recommended best practice for optimizing Athena queries on S3 data.

Answer

Increase the number of partitions to one per day.

Answer

Store the data in a single large JSON file.

Answer

Use gzip compression on each JSON file.

Question 11

A data engineer manages an Amazon S3 data lake with millions of small JSON files under prefixes partitioned by year/month/day. Amazon Athena queries scan far more data than expected and return slowly. The engineer wants to reduce bytes scanned and improve query performance while keeping files in S3 and queryable by Athena. Which solution meets these requirements with the LEAST operational overhead?

Accepted Answer

Use AWS Glue to convert the JSON files to Apache Parquet, write them to partitioned prefixes, and register the resulting tables in the AWS Glue Data Catalog.. Athena charges and performs based on bytes scanned, and row-oriented JSON forces reading entire objects. Converting to columnar Parquet lets the engine read only referenced columns, while partitioning by year/month/day enables partition pruning so irrelevant dates are skipped. Registering the converted data in the AWS Glue Data Catalog makes it available to Athena with minimal management, directly reducing scanned bytes and latency.

Answer

Enable S3 Transfer Acceleration on the bucket and configure Athena workgroups to use it for all queries.

Answer

Attach an S3 Lifecycle policy that transitions the JSON objects to S3 Glacier Instant Retrieval after 30 days and query them through Athena.

Answer

Create an Amazon S3 Inventory report for each partition and use it to rewrite the Athena queries so they reference only the inventory files.

Question 12

A data engineer manages an Amazon S3 data lake that ingests millions of small JSON files daily from an IoT fleet. Query performance in Amazon Athena has degraded significantly, and each query scans far more data than expected. The engineer wants to reduce per-query cost and improve performance without changing the raw data. Which solution should the engineer implement?

Accepted Answer

Convert the JSON files to Apache Parquet, partition the data, and compact small files with AWS Glue ETL jobs.. Athena performance and cost are driven by how many bytes a query scans, so converting row-oriented JSON to columnar Parquet, partitioning on common filter columns, and compacting small files directly reduce scanned data. These three changes work together: columnar storage reads only referenced columns, partitions prune entire prefixes, and compaction removes per-object overhead. The result is faster queries at lower cost without altering the source data in S3.

Answer

Move the data into an Amazon Redshift cluster and query it with Redshift Spectrum.

Answer

Increase the Athena workgroup data usage control limit and rerun the queries.

Answer

Enable S3 Transfer Acceleration on the ingestion bucket.

Question 13

A data engineer maintains an Amazon DynamoDB table that stores IoT telemetry. The table uses a partition key of deviceId and a sort key of timestamp, with on-demand capacity. A few very active devices generate millions of writes per hour while thousands of other devices write sporadically. The engineer observes throttling on writes for the active devices and wants to reduce it with the least application change. Which action should the engineer take?

Accepted Answer

Add a random suffix to the partition key to spread writes across partitions. Throttling from a few high-volume partition keys is a hot-partition problem, not a capacity problem. DynamoDB partitions have a fixed write ceiling regardless of table-level capacity mode. Write sharding by adding a suffix to the partition key spreads a single device's writes across many partitions, each with independent throughput. Provisioned capacity, streams, and GSIs do not raise the per-partition write limit, so they fail to address the observed throttling.

Answer

Enable DynamoDB Streams and process writes asynchronously

Answer

Switch the table to provisioned capacity with a high write capacity unit setting

Answer

Create a global secondary index on timestamp and write through the index

Question 14

A data engineer is setting up an Amazon DynamoDB table to store user session data. The table must handle sudden spikes in read traffic during peak hours, and the engineer wants to minimize operational overhead while ensuring consistent performance. The table's read capacity mode should automatically adjust to traffic changes. Which capacity mode should the engineer choose?

Accepted Answer

On-demand capacity mode.. On-demand capacity mode is designed for workloads with unpredictable traffic patterns. It automatically scales to handle sudden spikes in read and write traffic, eliminating the need for capacity planning and reducing operational overhead. This makes it the best choice for session data with variable demand.

Answer

Provisioned capacity mode with global secondary indexes.

Answer

Provisioned capacity mode with auto scaling enabled.

Answer

Provisioned capacity mode with reserved capacity.

Question 15

A financial services company stores trade records in Amazon DynamoDB. An application performs many reads per second for individual trades by tradeId and also runs a nightly analytics job that must read every trade for a given trading day. The table uses tradeId as the partition key only. The nightly job currently performs a full table scan and is slow and expensive. The company wants to optimize the nightly access pattern with minimal application changes. Which change should the data engineer make?

Accepted Answer

Create a global secondary index with a partition key of tradingDay and a sort key of tradeId, then query that index for each day.. DynamoDB access is most efficient when queries target a specific partition key value. Because the base table partitions by tradeId, retrieving all trades for a day requires a Scan. A global secondary index partitioned by tradingDay gives each day its own logical partition, so the nightly job can use Query to read only that day's items. The base table schema and write path stay the same, so application changes are minimal.

Answer

Add a local secondary index on the base table with tradeId as the sort key and query it for each day.

Answer

Increase the table's provisioned read capacity units and schedule the full table scan during off-peak hours.

Answer

Enable DynamoDB Streams on the table and have the nightly job consume the stream to reconstruct the day's trades.