DEA-C01 Practice Test 3 — 30 Questions

Question 1

Match each AWS data compression format to its typical use case.

Question 2

Match each AWS storage class to its description.

Question 3

Match each AWS monitoring tool to its primary use.

Question 4

A company uses Amazon DynamoDB as a source for an AWS Glue job. The job reads a large table using a DynamoDB export to S3 feature. The job is failing with 'ThrottlingException' from DynamoDB. What should the data engineer do to resolve this issue WITHOUT changing the job's logic?

Accepted Answer

Use the DynamoDB export to S3 feature and read the exported data from S3. Option C is correct because the DynamoDB export to S3 feature creates a point-in-time snapshot of the table data in S3 without consuming any read capacity units (RCUs) from the DynamoDB table. By reading the exported data from S3 instead of directly scanning the DynamoDB table, the Glue job avoids triggering ThrottlingException entirely, as the export operation uses the table's backup and restore mechanism, not the read path. This resolves the issue without altering the job's logic, as the job can be reconfigured to read from the S3 export location.

Answer

Use DynamoDB Streams to capture changes and process them incrementally

Answer

Reduce the number of DynamoDB read segments in the Glue job

Answer

Increase the read capacity units (RCU) of the DynamoDB table

Question 5

A company has an Amazon RDS for MySQL DB instance with read replicas. The primary DB instance fails. What is the correct procedure to promote a read replica to become the new primary?

Accepted Answer

Manually promote the read replica to a standalone DB instance.. When an Amazon RDS for MySQL primary DB instance fails, read replicas do not automatically become the new primary. The correct procedure is to manually promote the read replica using the AWS Management Console, CLI, or API, which converts it into a standalone DB instance. After promotion, you must update your application endpoints to point to the new primary, as RDS does not handle this automatically.

Answer

Modify the read replica to be a Multi-AZ deployment and failover will occur.

Answer

RDS automatically fails over to the read replica within 5 minutes.

Answer

Delete the primary and the read replica will automatically become the primary.

Question 6

A data engineer has set up an Amazon S3 lifecycle policy to transition objects to Glacier Instant Retrieval after 30 days. After 60 days, objects should transition to Deep Archive. However, objects are not transitioning to Deep Archive. What is the most likely cause?

Accepted Answer

The transition to Deep Archive requires a minimum of 30 days after the previous transition.. Amazon S3 lifecycle policies require a minimum of 30 days between successive transitions when moving objects from one storage class to another. Since the policy transitions objects to Glacier Instant Retrieval after 30 days, the subsequent transition to Deep Archive cannot occur until at least 60 days after creation (30 days for the first transition plus 30 days minimum gap). The current policy attempts the Deep Archive transition at 60 days, which is exactly 30 days after the first transition, meeting the minimum requirement. However, the most likely cause of the failure is that the policy is incorrectly configured or the objects are too new; given the options, the correct answer is that the transition to Deep Archive requires a minimum of 30 days after the previous transition, and the policy as described should work if the objects are older than 60 days, but the question implies a timing issue.

Answer

The bucket has versioning enabled.

Answer

Deep Archive is not supported in the bucket's region.

Answer

Objects are smaller than 128 KB.

Question 7

A company is migrating its on-premises MySQL database to Amazon RDS for MySQL. They want to minimize downtime and ensure data consistency. 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 is specifically designed for migrating databases to AWS with minimal downtime. DMS supports homogeneous migrations like MySQL to Amazon RDS for MySQL, and it uses ongoing replication (change data capture) to keep the source and target databases in sync during the migration, ensuring data consistency and allowing a cutover with only seconds of downtime.

Answer

AWS S3 Transfer Acceleration

Answer

AWS Glue

Answer

AWS Snowball Edge

Question 8

A data engineer is monitoring an Amazon Kinesis Data Analytics application that uses a SQL query to aggregate streaming data. The application is falling behind and the millisBehindLatest metric is increasing. Which action should the engineer take to improve performance?

Accepted Answer

Increase the Parallelism setting of the Kinesis Data Analytics application. Increasing the Parallelism setting of the Kinesis Data Analytics application allows the SQL query to process data across more in-application streams and operators concurrently, directly addressing the lag indicated by the rising millisBehindLatest metric. This action scales the compute resources allocated to the application without changing the source stream or the query logic, making it the most direct way to improve throughput for a SQL-based Kinesis Data Analytics application.

Answer

Switch from SQL to Apache Flink for the analytics application

Answer

Increase the number of shards in the source Kinesis stream

Answer

Decrease the window duration of the SQL query

Question 9

A company uses Amazon DynamoDB for a gaming leaderboard. The table has a partition key of 'game_id' and a sort key of 'score'. The read capacity is provisioned at 1000 RCUs. During peak hours, users report high latency when querying the top 10 scores for a specific game. The DynamoDB metrics show ConsumedReadCapacityUnits averaging 800 but occasional throttling. What is the most likely cause and solution?

Accepted Answer

The hot game_id partition is exceeding its throughput; add DynamoDB Accelerator (DAX) to cache reads. The hot game_id partition is exceeding its provisioned throughput because DynamoDB distributes RCUs evenly across partitions, and a single partition can only handle up to (1000 RCUs / number of partitions) per second. When a specific game_id becomes popular, all reads hit the same partition, causing throttling despite low overall consumed capacity. Adding DynamoDB Accelerator (DAX) caches the top 10 scores for that partition, reducing read pressure and eliminating throttling without increasing RCUs.

Answer

Create a global secondary index with the same key schema to distribute reads

Answer

Remove the sort key and use a global secondary index

Answer

Increase the provisioned RCUs to 2000

Question 10

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by a custom consumer application that writes to Amazon S3 every 5 minutes. The consumer is falling behind and processing lag is increasing. Which action is MOST effective to reduce the lag?

Accepted Answer

Increase the number of shards in the Kinesis stream. The consumer is falling behind because the stream's throughput capacity is insufficient for the incoming data volume. Increasing the number of shards in the Kinesis stream directly increases the total read capacity (each shard provides 2 MB/s read throughput and 5 transactions/second), allowing the consumer to process more data in parallel and reduce lag.

Answer

Switch to Amazon Kinesis Data Firehose to deliver data directly to S3

Answer

Increase the batch size of records written to S3

Answer

Reduce the retention period of the stream

Question 11

A data engineer is troubleshooting a slow-running query on an Amazon Redshift cluster. The query involves joining two large tables. The engineer notices that the query plan shows a large number of distribution and broadcast operations. Which design change would most likely improve query performance?

Accepted Answer

Change the distribution style of both tables to KEY on the join column. Option B is correct because changing the distribution style of both tables to KEY on the join column ensures that rows with the same join key value are co-located on the same node. This eliminates the need for expensive broadcast or redistribution operations during the join, as Redshift can perform the join locally on each slice without moving data across the network.

Answer

Change the distribution style of both tables to ALL

Answer

Change the distribution style of both tables to EVEN

Answer

Add a sort key on the join column

Question 12

A company uses Amazon Kinesis Data Firehose to ingest log data from web servers into Amazon S3. The data is in JSON format and each record is approximately 2 KB. The delivery stream is configured to buffer incoming records for 60 seconds or 5 MB, whichever comes first. The company notices that the data in S3 is delayed by up to 5 minutes during peak hours. Which action would most effectively reduce the delivery latency?

Accepted Answer

Decrease the buffer interval to 15 seconds.. The observed delay of up to 5 minutes during peak hours indicates that the buffer size threshold (5 MB) is rarely reached because each record is only ~2 KB, so the delivery stream relies on the buffer interval (60 seconds) to trigger delivery. By decreasing the buffer interval to 15 seconds, Kinesis Data Firehose will push data to S3 more frequently, directly reducing the maximum latency from 60 seconds to 15 seconds per batch, which eliminates the compounding delays caused by queuing during high-throughput periods.

Answer

Increase the buffer size to 10 MB to allow more records per delivery.

Answer

Enable compression (GZIP) on the delivery stream.

Answer

Enable data transformation with AWS Lambda to convert JSON to Parquet.

Question 13

A data pipeline uses AWS Glue ETL to process data from an S3 bucket and write results to a Redshift cluster. The job fails with a 'DiskFull' error on the Glue worker nodes. What is the best way to resolve this issue?

Accepted Answer

Increase the number of Glue DPUs or use G.1X worker type.. Increasing the number of DPUs or using G.1X worker types provides more disk space. Option A (increase timeout) doesn't help; Option B (use G.2X) is better; Option D (compression) might reduce data size but not the root cause.

Answer

Decrease the number of partitions in the output.

Answer

Use a different file format like Parquet to reduce storage.

Answer

Increase the job timeout setting.

Question 14

Order the steps to migrate an on-premises database to Amazon RDS using AWS DMS.

Question 15

Arrange the steps to set up a streaming ETL pipeline using Amazon Kinesis Data Firehose to Amazon S3.

Question 16

Order the steps to set up a Kinesis Data Analytics application for real-time stream processing.

Question 17

Order the steps to query data in Amazon Redshift Spectrum from an external table in Athena.

Question 18

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error: 'An error occurred while calling o123.pyWriteDynamicFrame. Access Denied when writing to S3 bucket: my-bucket'. The job uses a Glue service role named 'GlueServiceRole'. Which TWO actions should the engineer take to resolve the issue? (Choose TWO.)

Accepted Answer

Check if the S3 bucket policy denies access from the GlueServiceRole.. Option C is correct because the error message indicates an access denied when writing to S3, which can be caused by a bucket policy that explicitly denies the Glue service role's access, even if the IAM policy allows it. Option D is correct because the IAM policy attached to GlueServiceRole must include the s3:PutObject permission on the specific bucket to allow the Glue job to write data.

Answer

Disable S3 Block Public Access on the bucket.

Answer

Grant the GlueServiceRole permission to write to the AWS Glue Data Catalog.

Answer

Ensure the Glue job is in the same VPC as the S3 bucket.

Question 19

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Redshift. The migration is successful, but after a few days, data in Redshift becomes inconsistent with the source due to ongoing changes. The company needs to keep Redshift synchronized with minimal latency. Which approach should the data engineer use?

Accepted Answer

Configure DMS with ongoing replication using change data capture (CDC).. AWS DMS supports ongoing replication using change data capture (CDC), which captures incremental changes from the Oracle source (via Oracle LogMiner or binary logs) and applies them to Amazon Redshift in near real-time. This approach ensures that Redshift remains synchronized with the source database with minimal latency, meeting the requirement for ongoing consistency after the initial full load.

Answer

Use Amazon Redshift COPY with S3 staging and AWS Lambda triggers.

Answer

Schedule a full DMS load every night.

Answer

Set up Amazon Redshift Spectrum to query the Oracle database directly.

Question 20

A company runs a batch ETL job on Amazon EMR every night. Recently, the job started failing with 'Out of Memory' errors in the Spark executors. The data volume has grown 20% in the past month. The cluster uses uniform instance groups with 5 core nodes of r5.xlarge (4 vCPU, 32 GB RAM). Which change should the data engineer implement to resolve the issue with minimal cost increase?

Accepted Answer

Configure instance fleets to include r5.xlarge and r5.2xlarge instances.. Option C is correct because using instance fleets allows the cluster to include both r5.xlarge and r5.2xlarge instances, enabling the Spark executors to use the larger instances for memory-intensive tasks while still leveraging the existing r5.xlarge nodes. This provides a cost-effective way to handle the 20% data growth by adding memory capacity without replacing the entire cluster or over-provisioning all nodes. Instance fleets also support Spot Instances, which can further reduce costs while addressing the Out of Memory errors.

Answer

Increase the number of core nodes to 7.

Answer

Change instance type to r5.2xlarge (8 vCPU, 64 GB RAM) for all nodes.

Answer

Tune Spark memory configurations to reduce executor memory overhead.