DEA-C01 Data Store Management Practice Test 16 — 15 Questions

Question 1

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.. Apache Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, drastically reducing I/O and scan costs. Combined with compression (like Snappy or GZIP), Parquet minimizes the amount of data scanned per query, which directly lowers Athena's cost (charged per TB scanned) and improves query performance through predicate pushdown and efficient encoding.

Answer

Compress the data using GZIP.

Answer

Use S3 Standard-IA storage class.

Answer

Enable Requester Pays on the S3 bucket.

Question 2

A data engineer is troubleshooting an AWS Glue ETL job that reads from an S3 bucket and writes to a DynamoDB table. The job fails with an AccessDeniedException. The IAM role attached to the Glue job has the policy shown in the exhibit. Which TWO additional permissions are required to resolve the issue?

Accepted Answer

iam:PassRole. Option C is correct because the AWS Glue job must pass the IAM role to itself when it runs, which requires the `iam:PassRole` permission. Without this, the job cannot assume the role and will fail with an AccessDeniedException, even if the role has the necessary S3 and DynamoDB permissions.

Answer

kms:Decrypt

Answer

dynamodb:DescribeTable

Answer

s3:ListBucket

Question 3

A data engineering team is migrating a MySQL database to Amazon RDS for MySQL. They need to ensure high availability and automated failover. Which THREE configurations should they implement?

Accepted Answer

Enable automated backups with a retention period.. Option B is correct because automated backups with a retention period enable point-in-time recovery (PITR) and are required for Multi-AZ failover to function properly. RDS uses automated backups to keep the standby instance synchronized and to support recovery after a failover event.

Answer

Enable Enhanced Monitoring.

Answer

Create a read replica in a different region.

Question 4

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.. Option D is correct because 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 5

A data engineering team is building a real-time analytics pipeline using Amazon Kinesis Data Streams, AWS Lambda, and Amazon DynamoDB. The Lambda function consumes records from the stream and writes aggregated data to a DynamoDB table. The application requires that each record be processed exactly once to avoid duplicates. The Lambda function is idempotent, but occasionally duplicate records are written due to retries from Kinesis. The team needs to ensure exactly-once semantics for DynamoDB writes. Which solution should they implement?

Accepted Answer

Use DynamoDB TransactWriteItems with a condition check on a unique transaction ID.. Option B is correct because DynamoDB TransactWriteItems with a condition check on a unique transaction ID ensures that the write only succeeds if the transaction ID does not already exist in the table. This provides exactly-once semantics by preventing duplicate writes even when Kinesis retries deliver the same record multiple times. The condition check acts as a distributed lock at the item level, guaranteeing idempotency without relying on downstream deduplication.

Answer

Enable DynamoDB Streams and use a second Lambda to deduplicate.

Answer

Use the Kinesis Client Library (KCL) to checkpoint after processing and ignore duplicates.

Answer

Ensure the Lambda function is idempotent by using upsert operations.

Question 6

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.. Option C is correct because 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 7

A data engineering team is managing an Amazon DynamoDB table that stores user session data. The table has a primary key of user_id (partition key) and session_id (sort key). The application performs strongly consistent reads on individual items. The team notices that read latency increases during peak hours. They suspect that the table is experiencing hot partitions. The team needs to improve read performance without changing the application code. Which solution should they implement?

Accepted Answer

Enable DynamoDB Accelerator (DAX) for the table.. DynamoDB Accelerator (DAX) is a fully managed, in-memory cache that delivers up to 10x read performance improvement by caching hot partition data. Since the application uses strongly consistent reads and cannot be changed, DAX provides a drop-in caching layer that reduces read latency on hot partitions without requiring any code modifications.

Answer

Enable DynamoDB global tables to distribute reads across regions.

Answer

Increase the read capacity units for the table.

Answer

Change the application to use eventually consistent reads.

Question 8

A data team uses the CloudFormation template in the exhibit to create an S3 bucket for storing log files. After one year, they notice that the bucket size is larger than expected. They investigate and find that older versions of objects are not being deleted or transitioned. What is the most likely cause?

Accepted Answer

The lifecycle rule does not apply to noncurrent versions because it lacks NoncurrentVersionExpiration or NoncurrentVersionTransition.. Option A is correct because the lifecycle rule in the CloudFormation template is missing the `NoncurrentVersionExpiration` or `NoncurrentVersionTransition` properties. When S3 bucket versioning is enabled, lifecycle rules that only specify `ExpirationInDays` or `Transition` apply exclusively to the current version of objects. To manage older (noncurrent) versions, you must explicitly include `NoncurrentVersionExpirationInDays` or `NoncurrentVersionTransitionInDays` in the rule. Without these, noncurrent versions accumulate indefinitely, causing the bucket size to grow larger than expected.

Answer

The lifecycle rule is not enabled because the Status property is not set to 'Enabled' properly.

Answer

The bucket has versioning enabled, but the lifecycle rule only applies to current versions.

Answer

The expiration in days is set to 365, which is too short.

Question 9

A company is migrating its on-premises Oracle data warehouse to Amazon Redshift. The data engineering team needs to load data from Oracle to Redshift using AWS DMS (Database Migration Service). The source database is 2 TB in size. The team wants to minimize downtime and ensure data consistency during full load. Which approach should they take?

Accepted Answer

Use AWS DMS to perform a full load, then enable ongoing replication to capture changes.. Option B is correct because AWS DMS can perform a full load of the 2 TB Oracle database while simultaneously capturing ongoing changes via CDC (Change Data Capture). After the full load completes, DMS applies the cached changes to Redshift, ensuring data consistency with minimal downtime. This approach avoids stopping the source database and leverages DMS's native replication capabilities.

Answer

Use UNLOAD command to export data from Oracle to S3, then COPY into Redshift.

Answer

Stop the source database, export data to flat files, upload to S3, and use COPY to load into Redshift.

Answer

Use COPY command directly from Oracle to Redshift over JDBC.

Question 10

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.. Option D is correct because enabling auto-termination and using a mix of On-Demand and Spot instances for core and task nodes ensures job completion. Specifically, using On-Demand for core nodes and Spot for task nodes with a diversified allocation strategy reduces interruption impact. Option A is incorrect because increasing core nodes with On-Demand increases cost. Option B is incorrect because using only Spot instances increases failure risk. Option C is incorrect because instance fleets with only Spot do not help.

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 11

A data engineering team is responsible for an Amazon RDS for PostgreSQL instance that stores financial data. The database is 500 GB in size. The team needs to create a read replica in a different AWS Region for disaster recovery. The source database has automated backups enabled with a retention period of 7 days. The team initiates the cross-region read replica creation. After several hours, the replica status shows 'Replication Lag' of 30 minutes and is increasing. What should the team do to reduce the replication lag?

Accepted Answer

Enable Multi-AZ on the source database instance.. Option C is correct because enabling Multi-AZ on the source provides synchronous standby and reduces replication lag by offloading backups and improving stability. Option A is incorrect because increasing backup retention does not affect replication. Option B is incorrect because modifying the DB instance class may help but is not the primary solution; Multi-AZ is more effective. Option D is incorrect because deleting and recreating may not solve the underlying issue.

Answer

Modify the source DB instance to use a larger instance class.

Answer

Delete the replica and create a new one from a snapshot.

Answer

Increase the backup retention period to 35 days.

Question 12

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.. Option B is correct because 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.

Question 13

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is JSON and each record is about 2 KB. The delivery stream is configured to buffer incoming data to 5 MB or 60 seconds, whichever comes first. The data engineering team notices that the S3 bucket contains many small files (average 2 MB), which makes subsequent processing inefficient. They need to reduce the number of small files without increasing the latency beyond 5 minutes. Which solution should they implement?

Accepted Answer

Increase the buffer size to 50 MB and the buffer interval to 300 seconds.. Option B is correct because increasing the buffer size to 50 MB and the buffer interval to 300 seconds directly addresses the root cause: the current 5 MB buffer size triggers a flush too frequently, producing many 2 MB files. By raising the buffer size to 50 MB, each flush will contain more data, resulting in larger S3 objects (up to ~50 MB uncompressed), while the 300-second interval ensures latency stays within the 5-minute requirement. This reduces the number of small files without requiring additional services or post-processing.

Answer

Enable compression (GZIP) on the delivery stream.

Answer

Use a Lambda function to merge small files after delivery.

Answer

Decrease the buffer size to 1 MB and the buffer interval to 60 seconds.

Question 14

A data engineering team is managing an Amazon Redshift cluster that is used for BI reporting. The cluster has a mix of large tables (some over 1 TB) and many smaller tables. The team notices that queries on a large fact table are slow. The fact table is distributed using KEY distribution on the customer_id column, which has high cardinality. The team wants to improve query performance. They have the option to change the distribution style and sort key. Which redesign should they implement?

Accepted Answer

Change the distribution style to EVEN and set the sort key to a date column used in WHERE clauses.. Option D is correct because using EVEN distribution ensures data is evenly distributed across all nodes, avoiding data skew that can occur with KEY distribution on a high-cardinality column like customer_id. Setting the sort key to a date column used in WHERE clauses enables range-restricted scans, significantly reducing the amount of data scanned for common BI queries that filter by date. This combination improves query performance by maximizing parallelism and minimizing I/O.

Answer

Keep the distribution style as AUTO and set the sort key to customer_id.

Answer

Change the distribution style to ALL and set the sort key to customer_id.

Answer

Change the distribution style to KEY on a different column with high cardinality.

Question 15

A data engineering team is using Amazon DynamoDB to store time-series data for a monitoring application. The table has a primary key of device_id (partition key) and timestamp (sort key). The application queries data for a specific device over a time range. The team notices that read latency is high for devices that generate large amounts of data. They need to improve query performance. Which solution should they implement?

Accepted Answer

Create a global secondary index with device_id as partition key and a truncated timestamp as sort key to distribute writes.. Option C is correct because creating a global secondary index (GSI) with device_id as the partition key and a truncated timestamp as the sort key helps distribute write traffic more evenly across partitions. This reduces hot partitions caused by devices that generate large amounts of data, thereby improving read latency by preventing throttling and reducing contention on a single partition.

Answer

Enable DynamoDB Accelerator (DAX) for the table.

Answer

Change the application to use eventually consistent reads.

Answer

Increase the read capacity units for the table.