DEA-C01 Data Store Management Practice Test 27 — 15 Questions

Question 1

A data engineering team is designing a data lake on Amazon S3. They need to store raw data in its original format and transformed data in Parquet. The data is accessed by multiple analytics services, including Amazon Athena and Amazon Redshift Spectrum. Compliance requirements mandate that all data be encrypted at rest with AWS KMS and that the encryption keys be rotated every 90 days. Which S3 bucket configuration meets these requirements?

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

Accepted Answer

Use SSE-KMS with a customer-managed KMS key that has automatic key rotation enabled.. SSE-KMS with a customer-managed KMS key allows you to implement custom key rotation, such as every 90 days, by creating new keys and updating the bucket policy or key alias. AWS KMS automatic key rotation for customer-managed keys occurs yearly, not every 90 days, but you can achieve a 90-day rotation schedule manually or through automation (e.g., AWS Lambda). SSE-C requires manual key management and does not integrate with AWS services like Amazon Athena. SSE-S3 does not support configurable rotation, and the default encryption option (C) does not meet compliance if rotation is required.

Answer

Use SSE-C with client-managed keys and rotate them manually.

Answer

Use a bucket policy to enforce encryption and rely on default S3 encryption.

Answer

Use SSE-S3 with default encryption enabled.

Question 2

A company uses Amazon DynamoDB as the primary data store for a gaming application. The application stores user profiles and game state. During peak hours, the application experiences throttling on writes to the UserProfiles table. The table's read capacity is underutilized. Which solution should resolve the write throttling?

Accepted Answer

Increase the provisioned write capacity units for the table.. Write throttling occurs when the number of write requests exceeds the provisioned write capacity units (WCUs) for the DynamoDB table. Since the read capacity is underutilized, the correct solution is to increase the provisioned WCUs to accommodate the peak write traffic. This directly addresses the capacity deficit without affecting read operations.

Answer

Enable DynamoDB Accelerator (DAX) on the table.

Answer

Add a global secondary index (GSI) to the table.

Answer

Configure auto scaling for read capacity units.

Question 3

A company is running a data warehouse on Amazon Redshift. The data engineering team notices that query performance has degraded over time. They suspect that data distribution is causing excessive data movement between nodes. The table is joined frequently on the customer_id column. Which column should be chosen as the distribution key to optimize join performance?

Accepted Answer

customer_id. (customer_id) because Redshift distributes data across nodes based on the distribution key. When two tables are joined on customer_id, using it as the distribution key ensures that matching rows from both tables are co-located on the same node, eliminating the need for data redistribution (broadcast or shuffle) during the join. This minimizes network traffic and reduces query latency, directly addressing the performance degradation caused by excessive data movement.

Answer

AUTO distribution

Answer

order_date

Answer

EVEN distribution

Question 4

A company uses Amazon S3 to store sensitive financial data. The security team requires that all objects be encrypted at rest using AWS KMS with a customer-managed key. Additionally, they want to audit all KMS decrypt calls for compliance. Which configuration should be used to meet these requirements?

Accepted Answer

Use SSE-KMS with a customer-managed KMS key and enable CloudTrail data events for the key.. SSE-KMS with a customer-managed key allows the company to control the encryption key lifecycle and meet the requirement for customer-managed keys. Enabling CloudTrail data events for the KMS key captures all decrypt API calls, providing the necessary audit trail for compliance.

Answer

Enable default encryption on the bucket with SSE-KMS using an AWS managed key.

Answer

Use SSE-S3 with a bucket policy that denies uploads without encryption.

Answer

Use SSE-C with client-managed keys and log S3 API calls.

Question 5

A data engineer needs to store time-series data from IoT devices. The data is write-heavy and requires low-latency queries by device ID and timestamp. The data volume is expected to grow to terabytes. Which AWS database service is most suitable?

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical) to handle write-heavy IoT workloads at scale. It supports low-latency queries by device ID and timestamp via its SQL-compatible query engine, making it the most suitable choice for terabytes of time-series data.

Answer

Amazon RDS for MySQL

Answer

Amazon ElastiCache for Redis

Answer

Amazon DynamoDB

Question 6

A company is migrating its on-premises Oracle database to Amazon Aurora PostgreSQL. The migration must have minimal downtime. The source database is 2 TB and runs on a single server. Which AWS service should be used for the migration?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is the correct choice because it supports homogeneous migrations from Oracle to Amazon Aurora PostgreSQL with minimal downtime using ongoing replication (change data capture). DMS can handle a 2 TB source database by performing a full load followed by continuous replication of changes from the Oracle redo logs, allowing the target Aurora database to stay nearly in sync until cutover.

Answer

AWS DataSync

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Snowball Edge

Question 7

A company is using Amazon DynamoDB with auto scaling enabled. During a marketing campaign, write traffic spikes, and some write requests fail with ProvisionedThroughputExceededException. The auto scaling policy has a target utilization of 70% and a maximum capacity that is high enough. What is the most likely cause of the throttling?

Accepted Answer

Auto scaling cannot react quickly enough to sudden traffic spikes.. Auto scaling in DynamoDB adjusts capacity based on the average utilization over a period (typically 5-10 minutes). When a sudden traffic spike occurs, the write requests can exceed the current provisioned capacity before the auto scaling policy has time to react and increase the capacity. This delay causes ProvisionedThroughputExceededException errors, even though the maximum capacity is set high enough.

Answer

The table has a global secondary index that is throttling.

Answer

The table does not have enough maximum capacity.

Answer

The auto scaling policy is not configured correctly.

Question 8

A company is using Amazon Redshift for its data warehouse. The data engineering team needs to improve query performance for a large fact table that is frequently joined with multiple dimension tables. Which THREE strategies should be considered?

Accepted Answer

Define sort keys on columns used in WHERE clauses.. Option A is correct because defining sort keys on columns frequently used in WHERE clauses allows Redshift to use zone maps and block-level metadata to skip irrelevant blocks, dramatically reducing I/O for filtered queries on the large fact table. Option D is correct because choosing a distribution key based on the fact table's join columns co-locates matching rows on the same node slice, enabling collocated joins that avoid costly data redistribution (broadcast or shuffle) across the cluster. Option E is correct because columnar compression reduces the amount of data read from disk and improves I/O efficiency, which is especially beneficial for large fact tables scanned by analytical queries. Option B is not ideal here because DISTSTYLE EVEN spreads rows uniformly but does not co-locate join keys, so joins with dimension tables require network redistribution, hurting performance for frequent joins. Option C is not a targeted strategy for join performance; adding nodes increases compute and storage capacity but does not by itself address sort keys, distribution keys, or compression, and may not resolve join-related bottlenecks.

Answer

Use DISTSTYLE EVEN to distribute data evenly.

Answer

Increase the number of nodes in the cluster.

Question 9

A data engineer runs the AWS CLI command to retrieve the lifecycle configuration of the 'my-data-lake' bucket. The output is shown in the exhibit. What is the effect of this lifecycle policy?

Accepted Answer

Objects in the 'logs/' prefix are moved to S3 Standard-IA after 30 days, to Glacier after 90 days, and deleted after 365 days.. The lifecycle policy explicitly applies to the 'logs/' prefix, transitioning objects to S3 Standard-IA after 30 days, then to Glacier after 90 days, and finally expiring (deleting) them after 365 days. The 'Expiration' action with 'Days: 365' permanently removes the objects, while the 'Transitions' define the storage class changes at the specified intervals.

Answer

Objects in the 'logs/' prefix are deleted after 365 days and their delete markers are removed.

Answer

All objects in the bucket are moved to STANDARD_IA after 30 days.

Answer

Objects in the 'logs/' prefix are moved to Glacier after 90 days and expired after 90 days.

Question 10

A company stores its application logs in Amazon S3. The logs are generated daily and need to be retained for 3 years for compliance. The logs are accessed frequently for the first 30 days, occasionally for the next 6 months, and rarely after that. The data engineering team wants to minimize storage costs while ensuring that logs are available for retrieval within 12 hours for the first 6 months and within 48 hours after that. The team also wants to automatically delete logs after 3 years. Which lifecycle policy should the team implement?

Accepted Answer

Transition to S3 Standard-IA after 30 days, to S3 Glacier Deep Archive after 6 months, delete after 3 years.. It aligns with the access patterns and retrieval requirements: S3 Standard-IA after 30 days for occasional access with immediate retrieval, then S3 Glacier Deep Archive after 6 months for rare access with a 12-hour retrieval time (via expedited or standard retrieval), and deletion after 3 years. This minimizes storage costs while meeting the 48-hour retrieval window for older logs.

Answer

Transition to S3 One Zone-IA after 30 days, delete after 6 months.

Answer

Transition to S3 Standard-IA after 30 days, delete after 3 years.

Answer

Transition to S3 Standard-IA after 30 days, to S3 Glacier after 6 months, delete after 3 years.

Question 11

A data engineering team is designing a data lake on Amazon S3 for storing sensor data from IoT devices. The data is written in near real-time and needs to be queried using Amazon Athena. Which TWO configurations should the team implement to optimize query performance and minimize costs?

Accepted Answer

Store the data in Apache Parquet format.. Option C is correct because storing the data in Apache Parquet, a columnar format, allows Athena to read only the columns referenced in a query and leverages columnar compression and predicate pushdown, which dramatically reduces the amount of data scanned and therefore query cost and latency. Option D is correct because partitioning the data by date and sensor ID lets Athena prune irrelevant partitions using the partition metadata in the AWS Glue Data Catalog, so queries only scan the specific date/sensor combinations needed instead of the entire dataset. Together, Parquet plus partitioning is the standard best practice for cost-efficient, high-performance Athena queries over S3 data lakes. Option A is not correct because GZIP is a row-based compression format that cannot be split for parallel reads and does not provide column pruning, so it is inferior to Parquet's built-in columnar compression for this use case. Option B is not correct because S3 Standard-IA is a storage-class cost optimization for infrequently accessed data, not a query-performance optimization, and it can even add retrieval costs for frequently queried near real-time data. Option E is not correct because Requester Pays shifts data-transfer costs to the requester and does nothing to improve Athena query performance or reduce the team's own query scanning costs.

Answer

Compress the data using GZIP.

Answer

Use S3 Standard-IA storage class.

Answer

Enable Requester Pays on the S3 bucket.

Question 12

A company uses Amazon S3 to store historical stock market data as CSV files. They run daily Amazon Athena queries to generate reports. Recently, the finance team reported that queries are timing out and costs have increased significantly. The data engineering team notices that the S3 bucket contains thousands of small files (average 100 KB) due to a misconfigured ingestion pipeline. They need to improve query performance and reduce costs without changing the existing reporting schedule. The team has access to AWS Glue and can create new tables. Which solution should they implement?

Accepted Answer

Use AWS Glue ETL to read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files.. Converting the thousands of small CSV files into fewer, larger Parquet files using AWS Glue ETL directly addresses the root cause of poor Athena performance and high costs. Parquet is a columnar format that reduces the amount of data scanned per query, and larger files minimize the overhead of S3 LIST and GET operations, improving throughput. This solution does not change the reporting schedule and leverages existing Glue capabilities to create new optimized tables.

Answer

Partition the data by date and create a new Athena table with partitions.

Answer

Use S3 Select to filter rows within each file before Athena processes them.

Answer

Increase the Athena query timeout to 30 minutes.

Question 13

A company uses Amazon Redshift for its data warehouse. The data engineering team loads data daily from Amazon S3 using COPY commands. Recently, the load performance has degraded because the S3 bucket contains many small files. The team needs to optimize the COPY operation to improve performance. Which approach should they take?

Accepted Answer

Use a manifest file that lists only the necessary files, and consolidate small files into larger ones before loading.. The performance degradation is caused by the overhead of processing many small files during the COPY command. Consolidating small files into larger ones (e.g., 100 MB–1 GB each) reduces the number of S3 GET requests and the metadata overhead on Redshift, directly improving load throughput. Using a manifest file further optimizes by explicitly listing only the required files, avoiding unnecessary S3 list operations.

Answer

Use Redshift Spectrum to query the data directly from S3 without loading.

Answer

Increase the number of nodes in the Redshift cluster.

Answer

Enable automatic compression on the Redshift table.

Question 14

A data engineering team is using Amazon EMR to process large datasets stored in Amazon S3. The cluster uses Spot Instances for cost savings. During processing, the team notices that tasks are failing due to Spot Instance interruptions. The team needs to make the EMR job resilient to Spot interruptions without increasing costs significantly. Which solution should they implement?

Accepted Answer

Use EMR instance fleets with a mix of Spot and On-Demand, setting the allocation strategy to 'diversified' and using On-Demand for core nodes.. Using EMR instance fleets with a mix of Spot and On-Demand instances, setting the allocation strategy to 'diversified' and using On-Demand for core nodes, provides resilience against Spot interruptions while controlling costs. The 'diversified' strategy spreads Spot instances across multiple instance pools and Availability Zones, reducing the impact of a single Spot interruption. Core nodes, which handle HDFS data and run tasks, are kept on On-Demand to ensure stability, while task nodes can use Spot for cost savings.

Answer

Use EMR instance fleets with a mix of Spot and On-Demand, but set the allocation strategy to 'lowest price'.

Answer

Increase the number of core nodes using On-Demand instances.

Answer

Use only Spot Instances but enable automatic termination and checkpointing.

Question 15

A data engineering team is using AWS Glue to catalog data in an S3 data lake. They have a Glue crawler that runs daily to update the Data Catalog. Recently, they noticed that the crawler is taking longer to run and sometimes fails because of a timeout. The team suspects the issue is due to the large number of small files in the S3 bucket. They need to improve crawler performance and reliability. Which solution should they implement?

Accepted Answer

Use AWS Glue ETL to consolidate small files into larger ones before crawling.. Consolidating small files into larger ones (e.g., using AWS Glue ETL with a groupFiles or groupSize option, or a separate compaction job) reduces the number of objects the crawler must list and sample. This directly addresses the root cause: a high volume of small files increases metadata operations and can cause crawler timeouts. By reducing file count, the crawler can complete within the default 24-hour timeout and avoid failures.

Answer

Configure the crawler to use a different classifier.

Answer

Increase the crawler timeout to 24 hours.

Answer

Schedule the crawler to run more frequently to avoid large data accumulation.