DEA-C01 Data Operations and Support Practice Test 7 — 15 Questions

Question 1

A data engineer is troubleshooting a slow-running Amazon Athena query on a large dataset stored in S3. The query scans many small files. Which TWO actions can improve query performance?

Accepted Answer

Concatenate small files into larger files. Option A is correct because compacting small files into larger ones reduces overhead. Option C is correct because partitioning limits data scanned. Option B is wrong because converting to Parquet helps but is not one of the two selected here (though it is also good). Option D is wrong because more files increase overhead. Option E is wrong because disabling encryption does not affect performance.

Answer

Increase the number of files to increase parallelism

Answer

Disable S3 server-side encryption

Answer

Convert files from CSV to JSON

Question 2

A company is running a Redshift cluster and wants to improve query performance for a frequently used dashboard. Which THREE approaches are recommended?

Accepted Answer

Apply column compression encoding. Option A is correct because distribution keys reduce data movement. Option C is correct because sort keys enable range-restricted scans. Option E is correct because compression reduces I/O. Option B is wrong because more nodes adds cost and may not be needed. Option D is wrong because concurrency scaling addresses concurrent queries, not single query speed.

Answer

Enable concurrency scaling

Answer

Add more nodes to the cluster

Question 3

A data engineer is designing a data lake on Amazon S3. The data is accessed frequently for the first 30 days, then rarely after that. Which lifecycle policy is MOST cost-effective?

Accepted Answer

Transition to S3 Standard-Infrequent Access (Standard-IA) after 30 days.. Transitioning to S3 Standard-IA after 30 days reduces costs for infrequently accessed data while retaining low latency. Option A is wrong because Glacier has retrieval times not suitable for rare but possible access. Option B is wrong because One Zone-IA is less durable. Option D is wrong because keeping in Standard is more expensive.

Answer

Transition to S3 One Zone-IA after 30 days.

Answer

Transition to S3 Glacier Deep Archive after 30 days.

Answer

Keep in S3 Standard for 90 days, then delete.

Question 4

A data engineer is setting up a data pipeline using AWS Glue. The engineer wants to monitor job failures and receive notifications. Which TWO services can be used together for this purpose?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch (B) is correct because it is the native monitoring service for AWS Glue, capturing job metrics, logs, and state changes. You can configure CloudWatch alarms to trigger on job failures, which then invoke Amazon SNS (C) to send notifications via email, SMS, or other endpoints. Together, they provide a complete monitoring and alerting solution without additional orchestration.

Answer

AWS Step Functions

Answer

Amazon Kinesis Data Streams

Answer

Amazon SQS

Question 5

A company runs an Amazon EMR cluster with Spark jobs. One job fails with 'Container killed by YARN for exceeding memory limits'. The data engineer has already increased the executor memory. What is the NEXT best step to resolve the issue?

Accepted Answer

Increase the YARN container memory allocation (yarn.nodemanager.resource.memory-mb).. Increasing the yarn.nodemanager.resource.memory-mb allows the node manager to allocate more memory per container, which can prevent YARN from killing containers. Option A is wrong because increasing driver memory may not help if executors are the issue. Option C is wrong because reducing parallelism may mitigate but does not address the root cause. Option D is wrong because it is about memory overhead, but the main issue is YARN limits.

Answer

Set spark.executor.memoryOverhead to a higher value.

Answer

Decrease the number of Spark partitions.

Answer

Increase the driver memory.

Question 6

Refer to the exhibit. A data engineer runs the command on an object in S3. The engineer expected the object to have a tag 'type=raw' but sees no metadata. What is the likely cause?

Accepted Answer

Object tags are not returned by head-object; use get-object-tagging instead. Option B is correct because object tags are not returned by head-object; they require a separate get-object-tagging call. Option A is wrong because the command works. Option C is wrong because bucket policies affect access, not tag visibility. Option D is wrong because lifecycle rules do not remove tags.

Answer

The S3 bucket is in a different AWS Region

Answer

The bucket policy blocks reading tags

Answer

The object was created without tags because of lifecycle rules

Question 7

A data engineer needs to automate the backup of an Amazon RDS for PostgreSQL database. Which AWS service can be used to schedule and manage the backups?

Accepted Answer

AWS Backup. AWS Backup is a fully managed backup service that can automate backups of RDS databases. Option B is wrong because CloudWatch is for monitoring. Option C is wrong because S3 is storage. Option D is wrong because Lambda can be used but is not the primary managed service for backup scheduling.

Answer

Amazon S3

Answer

AWS Lambda

Answer

Amazon CloudWatch

Question 8

Refer to the exhibit. A data engineer has attached this IAM policy to a user. The user reports being unable to upload files to my-bucket from an on-premises network with a public IP of 203.0.113.5. What is the issue?

Accepted Answer

The user's IP address is not within the allowed IP range. Option A is correct because the policy restricts access to the IP range 10.0.0.0/16, which is a private range, not the user's public IP. Option B is wrong because the actions are allowed. Option C is wrong because if encryption were required, there would be a condition. Option D is wrong because the resource includes the bucket.

Answer

The resource ARN does not include the bucket itself

Answer

The user does not have s3:PutObject permission

Answer

The bucket requires server-side encryption

Question 9

A data engineer is troubleshooting a slow-running Amazon Athena query. The query scans a large amount of data. Which TWO actions can improve query performance? (Choose TWO.)

Accepted Answer

Convert the data to Parquet or ORC format.. Partitioning the table and converting to columnar formats like Parquet reduce the amount of data scanned, improving performance. Option C is wrong because using SELECT * scans all columns. Option D is wrong because increasing timeout does not improve performance. Option E is wrong because it is not a standard optimization.

Answer

Enable encryption at rest.

Answer

Increase the Athena query timeout.

Answer

Use SELECT * to retrieve all columns.

Question 10

Refer to the exhibit. A data engineer runs this CloudWatch Logs Insights query on a log group but gets no results. What is the most likely reason?

Accepted Answer

There are no log events containing the string 'ERROR' in the log group. Option A is correct because the query filters for 'ERROR' and if logs don't contain that string, no results are returned. Option B is wrong because the query has a limit of 20. Option C is wrong because the query is syntactically correct. Option D is wrong because the query does not use a time filter, so it scans all available logs, but if no error logs exist, results are empty.

Answer

The query syntax is invalid

Answer

The query limit of 20 is too low

Answer

The time range is not specified and defaults to the last 15 minutes

Question 11

A company is experiencing high costs from Amazon Redshift. The data engineer wants to optimize costs. Which THREE actions should the engineer take? (Choose THREE.)

Accepted Answer

Right-size the cluster based on workload analysis.. Option B is correct because right-sizing the cluster based on workload analysis ensures that the provisioned resources (number and type of nodes) match the actual compute and storage demands. Over-provisioned clusters waste money on unused capacity, while under-provisioned clusters cause performance issues. Analyzing metrics like CPU utilization, disk usage, and query queue wait times helps identify the optimal node count and instance type, directly reducing costs.

Answer

Increase the frequency of automated snapshots.

Answer

Increase the number of nodes to improve performance.

Question 12

A data engineer notices that an AWS Glue ETL job processing data from Amazon S3 to Amazon Redshift has been failing intermittently with the error 'S3ServiceException: SlowDown'. Which action is MOST likely to resolve this issue?

Accepted Answer

Implement exponential backoff and retry logic in the Glue job.. Option B is correct because the 'SlowDown' error indicates request throttling by S3; implementing exponential backoff and retries reduces request rate and prevents throttling. Option A is wrong because increasing partitions could increase the number of requests. Option C is wrong because switching to a larger instance type does not affect S3 request rate. Option D is wrong because S3 does not have a burst concurrency limit.

Answer

Increase the number of partitions in the Glue job to parallelize reads.

Answer

Switch from a Standard to a G.2X large Glue worker type.

Answer

Enable S3 Transfer Acceleration on the source bucket.

Question 13

A data engineer needs to ensure that data in an Amazon S3 bucket is not publicly accessible. Which TWO measures should the engineer implement? (Choose TWO.)

Accepted Answer

Attach a bucket policy that denies access to 'Principal': '*' unless specific conditions are met.. Blocking public access at the bucket level and using bucket policies to deny public access are effective controls. Option C is wrong because encryption does not prevent public access. Option D is wrong because versioning does not control access. Option E is wrong because lifecycle policies do not control access.

Answer

Create a lifecycle policy to delete objects after 30 days.

Answer

Enable S3 Versioning on the bucket.

Answer

Enable default encryption on the bucket.

Question 14

A company runs a data pipeline that ingests streaming data via Amazon Kinesis Data Streams, processes it with an AWS Lambda function, and stores results in Amazon DynamoDB. The Lambda function sometimes fails due to 'ProvisionedThroughputExceededException' on the DynamoDB table. Which combination of steps should a data engineer take to resolve this issue?

Accepted Answer

Enable DynamoDB auto scaling and configure a dead-letter queue for the Lambda function.. Option C is correct because DynamoDB auto scaling adjusts capacity based on load, and adding a dead-letter queue (DLQ) for failed records prevents data loss and allows reprocessing. Option A is wrong because increasing Lambda timeout does not address DynamoDB throttling. Option B is wrong because Lambda reserved concurrency may limit processing, not help. Option D is wrong because Kinesis shard count does not affect DynamoDB throughput.

Answer

Increase the Lambda function timeout and enable batch windows.

Answer

Increase the number of Kinesis shards to reduce Lambda invocations.

Answer

Increase Lambda reserved concurrency and disable retries.

Question 15

A data engineer notices that an AWS Glue ETL job is failing with an OutOfMemory error when processing a large dataset. The job uses a Standard worker type. Which action is MOST effective to resolve this issue without changing the job script?

Accepted Answer

Increase the number of DPUs per worker. Option B is correct because increasing the number of DPUs per worker provides more memory per task, addressing OutOfMemory errors directly. Option A is wrong because G.1X worker type has less memory than Standard. Option C is wrong because increasing the number of workers does not increase memory per worker. Option D is wrong because changing to G.2X may help but is not as direct as increasing DPUs per worker, and it may also increase cost unnecessarily.

Answer

Increase the number of workers

Answer

Switch to G.1X worker type

Answer

Change to G.2X worker type