DEA-C01 Practice Test 8 — 10 Questions

Question 1

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 2

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 3

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 4

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

Question 5

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

Question 6

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

Question 7

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

Question 8

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 9

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 10

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.