DEA-C01 Data Store Management Practice Test 8 — 15 Questions

Question 1

A company runs a real-time analytics platform on AWS. Data is ingested from thousands of IoT devices into Amazon Kinesis Data Streams. A Lambda function consumes the stream, processes the data, and writes the results to an Amazon DynamoDB table. The DynamoDB table has a provisioned write capacity of 1000 WCU, and the read capacity is set to 200 RCU. Recently, the company noticed that the Lambda function is failing with ProvisionedThroughputExceededException on DynamoDB writes. The Lambda function is configured with a batch size of 100 and a concurrency limit of 10. The Kinesis shard count is 4. The number of devices has increased, but the data volume per device has remained the same. The company needs to resolve the write throttling without increasing the DynamoDB write capacity. Which action should the data engineer take?

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

Accepted Answer

Reduce the batch size of the Lambda function to 10.. Reducing the batch size from 100 to 10 decreases the number of records processed per Lambda invocation, which reduces the burst of write requests to DynamoDB per invocation. This helps stay within the 1000 WCU limit without increasing capacity, as the same total throughput is spread across more invocations with smaller batches.

Answer

Increase the number of Kinesis shards to 8.

Answer

Increase the Lambda concurrency limit to 20.

Answer

Increase the batch size to 200.

Question 2

A company is migrating its on-premises PostgreSQL database to Amazon RDS for PostgreSQL. The database is 5 TB in size and supports a critical application that requires less than 30 minutes of downtime. The company has a 1 Gbps network connection to AWS. The data engineering team plans to use AWS Database Migration Service (DMS) with change data capture (CDC) to keep the target in sync. During the full load phase, DMS is taking longer than expected, and the team is concerned about meeting the downtime window. Which action should the team take to speed up the full load?

Accepted Answer

Create multiple DMS tasks to load different tables in parallel.. Creating multiple DMS tasks to load different tables in parallel (Option D) is the correct action because DMS performs full load sequentially within a single task. By splitting tables across multiple tasks, the team can parallelize the data transfer, utilizing the 1 Gbps network more efficiently and reducing the overall full load time to meet the 30-minute downtime window.

Answer

Increase the compute capacity of the target RDS instance.

Answer

Enable DMS validation to ensure data integrity.

Answer

Use AWS Snowball Edge to transfer the data offline.

Question 3

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

kms:Decrypt. The Glue job needs s3:ListBucket (option D) because listing the bucket is required to discover and read the source objects; without it, S3 access can fail with AccessDenied even when GetObject is allowed. It also needs kms:Decrypt (option A) because if the S3 bucket or DynamoDB table is encrypted with a customer-managed KMS key, Glue must be granted decrypt permission on that key to read the data. dynamodb:DescribeTable (B) is not strictly required for the write operation itself and is not the missing permission causing the failure. iam:PassRole (C) is only needed when a service must assume a role, which is already handled by the Glue job's attached role. glue:GetJobRun (E) is a monitoring permission and does not affect the ETL job's ability to read from S3 or write to DynamoDB.

Answer

dynamodb:DescribeTable

Answer

iam:PassRole

Answer

glue:GetJobRun

Question 4

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 enabling automated backups with a retention period is required for RDS to support point-in-time recovery and is a prerequisite for Multi-AZ failover operations, ensuring data durability and recoverability during a failover event. Option C is correct because a Multi-AZ deployment maintains a synchronous standby replica in a different Availability Zone and provides automatic failover to that standby if the primary DB instance fails, directly satisfying the high availability and automated failover requirement. Option D is correct because a Multi-AZ DB instance requires a DB subnet group that spans at least two Availability Zones, which is the networking foundation that allows RDS to place the primary and standby instances in separate AZs. Option A is not correct because Enhanced Monitoring only provides granular OS-level metrics (via the RDS monitoring agent) and does not contribute to high availability or failover. Option E is not correct because a cross-region read replica is used for disaster recovery and read scaling, not for automatic failover of the primary DB instance within the Multi-AZ high-availability design.

Answer

Enable Enhanced Monitoring.

Answer

Create a read replica in a different region.

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.. 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 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 7

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.. 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 8

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.. 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 9

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

Modify the source DB instance to use a larger instance class.. Modifying the source DB instance to a larger instance class provides more compute and memory resources. If the source instance is resource-constrained, this can reduce I/O contention and allow it to generate and ship write-ahead logs (WAL) more efficiently, potentially decreasing the replication lag to the cross-region read replica. Enabling Multi-AZ on the source database adds a synchronous standby in a different Availability Zone, which does not offload cross-region replication tasks and can increase write latency and resource overhead on the primary, possibly exacerbating replication lag. Deleting and recreating the replica from a snapshot or increasing backup retention does not address ongoing replication performance.

Answer

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

Answer

Increase the backup retention period to 35 days.

Answer

Enable Multi-AZ on the source database instance.

Question 10

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.. 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 11

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.. 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.

Question 12

A company runs a MySQL-compatible Amazon Aurora database for its e-commerce platform. The database experiences high write latency during peak hours. The application performs frequent INSERT and UPDATE operations on a table with 50 million rows. The DB instance is db.r5.large with 500 GB of Provisioned IOPS storage. A recent performance analysis shows that the average queue depth is consistently above 32 and write latency exceeds 50 ms. The company needs to reduce write latency without changing the application code. What should a data engineer do?

Accepted Answer

Resize the DB instance to a larger instance type such as db.r5.xlarge.. The high queue depth (consistently above 32) and write latency exceeding 50 ms indicate that the current db.r5.large instance is CPU or I/O constrained for the write workload. Resizing to a larger instance type such as db.r5.xlarge increases the available vCPUs, memory, and network bandwidth, which directly reduces queue depth and write latency by allowing more concurrent write operations to be processed. This solution does not require application code changes and addresses the root cause of insufficient compute capacity for the frequent INSERT and UPDATE operations on the 50-million-row table.

Answer

Enable Aurora Auto Scaling to automatically add reader instances.

Answer

Convert the cluster to Aurora Serverless v2 to automatically scale compute capacity.

Answer

Create a read replica and configure the application to offload read queries.

Question 13

A media company stores video files in an Amazon S3 bucket with S3 Standard storage class. The files are accessed frequently for the first 30 days, then rarely after that. However, the company must be able to restore any deleted file within 7 days. The company wants to minimize storage costs while meeting the access and retention requirements. What should a data engineer do?

Accepted Answer

Use S3 Intelligent-Tiering and enable S3 Versioning on the bucket.. S3 Intelligent-Tiering automatically moves objects between access tiers (frequent, infrequent, and archive instant access) based on changing access patterns, which minimizes storage costs for data with unknown or changing access patterns. Enabling S3 Versioning allows the company to restore any deleted file within 7 days by reverting to a previous version, meeting the retention requirement without additional cost for a separate backup.

Answer

Use S3 Standard-IA storage class from the start.

Answer

Use a lifecycle policy to transition objects to S3 One Zone-IA after 30 days.

Answer

Use S3 Glacier Deep Archive after 30 days and enable S3 Object Lock for retention.

Question 14

A company stores sensitive customer data in Amazon S3. The security team requires that all data be encrypted at rest using server-side encryption with AWS KMS managed keys (SSE-KMS). Which S3 bucket policy condition will enforce this requirement?

Accepted Answer

s3:x-amz-server-side-encryption-aws-kms-key-id. The condition `s3:x-amz-server-side-encryption-aws-kms-key-id` enforces that objects uploaded to S3 must use a specific AWS KMS key for server-side encryption (SSE-KMS). This satisfies the security team's requirement that all data be encrypted at rest using SSE-KMS with AWS KMS managed keys, as it explicitly checks for the presence and value of the KMS key ID in the request. Option C, `s3:x-amz-server-side-encryption`, can also enforce SSE-KMS if the condition value is set to 'aws:kms', but it does not require a specific KMS key ID, which the question's phrasing 'AWS KMS managed keys' implies is needed.

Answer

s3:x-amz-server-side-encryption-customer-algorithm

Answer

s3:x-amz-server-side-encryption

Answer

s3:x-amz-server-side-encryption-aws-kms-key-id or s3:x-amz-server-side-encryption

Question 15

A data engineer needs to store semi-structured JSON logs from AWS CloudTrail. The logs are append-only and rarely accessed after 90 days. Which storage solution is MOST cost-effective?

Accepted Answer

Amazon S3 Glacier Deep Archive. Amazon S3 Glacier Deep Archive is the most cost-effective storage solution for CloudTrail logs that are append-only and rarely accessed after 90 days. It offers the lowest storage cost among AWS options (approximately $0.00099 per GB/month) and is designed for data that is accessed at most once or twice per year, with retrieval times of 12–48 hours. Since the logs are rarely accessed after 90 days, the retrieval latency is acceptable, and the cost savings over S3 Standard (which costs ~$0.023 per GB/month) are substantial.

Answer

Amazon S3 Standard

Answer

Amazon EBS with cold HDD volumes

Answer

Amazon DynamoDB with on-demand capacity