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

Question 1

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?

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

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 2

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 3

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 4

A company is designing a data lake on Amazon S3. The data engineering team needs to implement a lifecycle policy to manage costs. Which TWO actions should be taken to reduce storage costs?

Accepted Answer

Transition objects to S3 Glacier Deep Archive after 90 days.. Transitioning objects to S3 Glacier Deep Archive after 90 days significantly reduces storage costs for data that is rarely accessed and can tolerate a retrieval time of 12 hours. This lifecycle policy is a standard cost-optimization strategy for data lakes where historical or cold data does not require immediate access.

Answer

Transition objects to S3 One Zone-IA after 30 days.

Answer

Enable S3 Intelligent-Tiering.

Answer

Transition objects to S3 Standard-IA after 30 days.

Question 5

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.. Defining sort keys on columns used in WHERE clauses allows Amazon Redshift to use zone maps to skip large blocks of data that do not satisfy the filter condition, dramatically reducing the amount of data scanned. This is especially effective for large fact tables where selective filters can prune entire disk blocks, improving query performance without additional hardware.

Answer

Use DISTSTYLE EVEN to distribute data evenly.

Answer

Increase the number of nodes in the cluster.

Question 6

A data engineer is setting up Amazon S3 bucket policies for a data lake. The security team requires that all objects uploaded to the bucket be encrypted at rest using server-side encryption. Which TWO methods can enforce encryption at upload time?

Accepted Answer

Enable S3 default encryption on the bucket.. Enabling S3 default encryption on the bucket automatically applies server-side encryption (SSE-S3 or SSE-KMS) to all objects uploaded without an encryption header, ensuring encryption at rest. Option E is correct because a bucket policy with a Deny effect on PutObject when the x-amz-server-side-encryption header is missing enforces encryption at upload time by rejecting unencrypted uploads, providing a complementary enforcement mechanism.

Answer

Enable S3 Transfer Acceleration.

Answer

Enable AWS CloudTrail to monitor uploads.

Answer

Enable AWS KMS automatic key rotation.

Question 7

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 8

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?

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 9

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 10

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 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.. 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 12

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.. 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 13

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 14

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 15

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.