DEA-C01 Data Operations and Support Practice Test 11 — 15 Questions

Question 1

A data engineering team uses AWS Glue ETL jobs to process data from Amazon S3. The jobs recently started failing with 'Access Denied' errors when writing to the output S3 bucket. What is the most likely cause?

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

Accepted Answer

The S3 bucket policy was modified to deny write access to the Glue job's IAM role.. AWS Glue ETL jobs use an IAM role for permissions. If the S3 bucket policy was modified to explicitly deny write access to that role, the job would fail with 'Access Denied' errors. Option A is incorrect because KMS key access issues would cause encryption-related errors, not generic access denied. Option B is incorrect because default encryption is not required for write access; it only affects encryption at rest. Option C is incorrect because out-of-memory errors would manifest as runtime errors, not access denied.

Answer

The KMS key used for server-side encryption is not accessible to the Glue job.

Answer

The S3 bucket does not have default encryption enabled.

Answer

The job ran out of memory due to large data volume.

Question 2

Refer to the exhibit. A data engineer sees this output from the AWS CLI for a failed Glue job. The job uses 10 workers of Standard type. What is the MOST appropriate action to resolve the OutOfMemoryError?

Accepted Answer

Change WorkerType to G.1X. The OutOfMemoryError occurs because each Standard worker has a memory limit of 4 GB. Increasing MaxCapacity to 20 does not increase memory per worker when NumberOfWorkers is explicitly set; it only increases the total DPU allocation but the job still uses 10 workers each with 1 DPU. The correct solution is to change the worker type to G.1X, which provides 2 DPUs (8 GB memory) per worker, effectively doubling the memory per worker and resolving the error. Option A (increase workers) adds parallelism but does not increase per-worker memory. Option B (reduce workers) decreases parallelism and may aggravate memory pressure. Option D (increase MaxCapacity) is ineffective when NumberOfWorkers is fixed.

Answer

Increase NumberOfWorkers to 20

Answer

Reduce NumberOfWorkers to 5

Answer

Increase MaxCapacity to 20

Question 3

A data engineer needs to monitor the number of records processed by a Kinesis Data Firehose delivery stream and set an alarm if the count drops below a threshold. Which CloudWatch metric should be used?

Accepted Answer

IncomingRecords. 'IncomingRecords' counts records received by Firehose, which directly indicates processing volume. Option B is wrong because 'IncomingBytes' measures bytes, not records. Option C is wrong because 'DeliveryToS3.Success' is a success metric but measures successful deliveries, not record count. Option D is wrong because 'PutRecord.Success' is a per-API call metric for Kinesis Data Streams, not Firehose.

Answer

PutRecord.Success

Answer

DeliveryToS3.Success

Answer

IncomingBytes

Question 4

A company runs a Redshift cluster for analytics. The data engineering team notices that COPY commands from S3 are failing for large files (>1 GB) with the error 'S3ServiceException: SlowDown'. What is the most effective solution?

Accepted Answer

Split the large files into smaller parts (e.g., 100 MB each) and use parallel COPY.. The SlowDown error indicates throttling from S3. Splitting large files into smaller parts increases parallelism and reduces the chance of throttling per object. Option A is wrong because using Redshift Spectrum is for querying external tables, not for addressing S3 throttling during COPY. Option B is wrong because enabling automatic compression is for compression, not throttling. Option C is wrong because increasing the number of Redshift nodes does not directly address S3 throttling.

Answer

Use Redshift Spectrum to query the data directly in S3.

Answer

Enable automatic compression on the target tables.

Answer

Increase the number of Redshift nodes to distribute the load.

Question 5

Refer to the exhibit. This IAM policy is attached to a user who is trying to read the object s3://data-bucket/confidential/report.csv. The user's principal tag 'role' is set to 'analyst'. What will happen when the user attempts to read the object?

Accepted Answer

Denied because the condition in the Deny statement evaluates to true. The Deny statement applies when the role tag is not 'admin'. The user's tag is 'analyst', so the condition matches and access is denied. Option A is wrong because the Allow statement is overridden by the explicit Deny. Option B is wrong because the Deny applies to all actions in the confidential prefix. Option D is wrong because Deny overrides Allow.

Answer

Denied because the Deny statement covers all actions under confidential

Answer

Allowed because there is an explicit Allow and no explicit Deny that matches

Answer

Allowed because of the Allow statement for s3:GetObject

Question 6

A data engineer is troubleshooting a step function that orchestrates ETL jobs. The state machine fails with 'State Machine Execution Throttled' error. What should the engineer do to resolve this?

Accepted Answer

Request a service quota increase for concurrent executions.. The 'State Machine Execution Throttled' error indicates that the state machine has exceeded the default concurrent execution limit for Step Functions. The proper resolution is to request a service quota increase from AWS Support for the concurrently running executions. Option A is incorrect: reducing the number of steps does not affect the limit on concurrent executions. Option B is incorrect: a CloudWatch alarm can detect throttling but cannot resolve it; the alarm would only notify, not increase quotas. Option C is incorrect: API rate limits are not the same as execution throttling; Step Functions has separate limits for API call rate and concurrent executions. Adjusting API rate limits would not address execution throttling.

Answer

Reduce the number of steps in the state machine.

Answer

Set up a CloudWatch alarm to detect throttling and retry.

Answer

Adjust the API rate limits in the state machine definition.

Question 7

Refer to the exhibit. A data engineer runs this CLI command to check an object's metadata. The engineer wants to verify if the object is eligible for lifecycle transition to S3 Glacier based on its age. What additional information is needed?

Accepted Answer

The current date. The CLI command output provides the last modified date of the object. To determine if the object is eligible for lifecycle transition to S3 Glacier based on its age, the engineer needs the current date to compute the object's age. The age is calculated as the difference between the current date and the last modified date. Option A is correct because without the current date, the engineer cannot verify the age condition. Option B (ETag) is used for integrity checking and not for age calculations. Option C (metadata archive flag) is not relevant; the archive flag indicates storage class but not age. Option D (ContentLength) is the object size and does not affect lifecycle age eligibility.

Answer

The ETag value

Answer

The metadata archive flag

Answer

The ContentLength value

Question 8

A company uses Amazon Athena to query data in S3. Recently, queries have become slow. The data is stored as CSV files in a partitioned table. What is the most effective way to improve query performance?

Accepted Answer

Convert the data to Parquet format and optimize partitioning.. The correct answer. Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, reducing I/O and improving performance. Combined with effective partitioning, it enables partition pruning, which further limits the data scanned. CSV files are row-based and require full scans, even with partitioning. Option A is incorrect because Athena is serverless and users cannot increase nodes; resources are managed automatically. Option C is incorrect: JSON is also row-based and verbose, making it even slower than CSV. Option D is incorrect because larger CSV files still lead to full scans; Parquet's columnar nature is more impactful than file size.

Answer

Increase the number of nodes in the Athena query engine.

Answer

Convert the data to JSON format.

Answer

Increase the size of the CSV files to reduce the number of files.

Question 9

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from an S3 bucket and writes to an Amazon Redshift table. The job fails with a permission error. Which IAM policy addition is MOST likely required for the Glue job's role?

Accepted Answer

Add redshift:GetClusterCredentials. Redshift:GetClusterCredentials. When AWS Glue writes to Amazon Redshift using the JDBC driver with IAM authentication, the Glue job's IAM role needs the redshift:GetClusterCredentials permission to obtain temporary credentials for the Redshift database. Option A (redshift:DataAPI) is not a valid IAM action; the actual Data API action is redshift:ExecuteStatement. Options B and C are unrelated to executing queries. Therefore, D is the correct permission required when using the JDBC connection method commonly used by Glue jobs.

Answer

Add redshift:DataAPI

Answer

Add redshift:ModifyCluster

Answer

Add redshift:DescribeStatement

Question 10

A data engineer is troubleshooting a DMS task that is replicating data from an on-premises Oracle database to an RDS for MySQL instance. The task is failing with 'ORA-1555: snapshot too old' error. What is the best course of action?

Accepted Answer

Increase the UNDO tablespace size and set UNDO_RETENTION to a higher value.. The ORA-1555 'snapshot too old' error occurs when a long-running query (such as Change Data Capture (CDC) in AWS DMS) needs to read consistent data from undo segments, but the undo information has been overwritten or retained for too short a period. Increasing the UNDO tablespace size and setting UNDO_RETENTION to a higher value ensures that undo data is preserved longer, allowing CDC to read consistent snapshots without encountering this error. Option A (disabling supplemental logging) would prevent CDC from capturing changes, thus is incorrect. Option B (increasing redo logs) does not address the undo retention issue. Option C (enabling batch optimized apply) may improve apply performance but does not resolve the source-side undo problem.

Answer

Disable full supplemental logging on the source tables.

Answer

Increase the size of the redo logs on the source database.

Answer

Enable batch optimized apply on the DMS task.

Question 11

A company runs an Amazon RDS for PostgreSQL database and wants to capture change data (inserts, updates, deletes) to stream into Amazon Kinesis Data Streams for real-time processing. Which AWS service should be used to capture the changes directly from the database?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication. AWS DMS with ongoing replication (change data capture) is the correct service because it can continuously capture insert, update, and delete operations from the PostgreSQL transaction logs (WAL) and stream them to a Kinesis Data Streams endpoint. This allows real-time processing without modifying the source database or requiring application-level triggers.

Answer

Amazon RDS automated snapshots

Answer

AWS Glue ETL job scheduled to run every minute

Answer

Amazon Kinesis Agent

Question 12

A data engineer is designing a data pipeline that ingests streaming data from an IoT fleet using Kinesis Data Streams and processes it with a Lambda function. The Lambda function often times out when the data volume spikes. What is the most scalable solution?

Accepted Answer

Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda.. Increasing the number of shards increases the parallelism of the stream, allowing higher throughput. Using Kinesis Data Analytics for windowed aggregation reduces the volume of data sent to Lambda, preventing timeouts during spikes. Option A is wrong because reducing batch size decreases the number of records per invocation, which can increase the number of Lambda invocations and overhead, but does not address the root cause of timeouts due to volume. Option B is wrong because increasing the Lambda timeout to 15 minutes allows the function to run longer, but it does not increase throughput or handle spikes efficiently; it only delays failures and is not scalable. Option C is wrong because increasing memory can improve performance, but setting reserved concurrency limits the maximum number of concurrent executions, which can throttle processing during spikes and reduce scalability.

Answer

Reduce the batch size in the event source mapping.

Answer

Increase the Lambda function timeout to 15 minutes.

Answer

Increase the Lambda function memory and set reserved concurrency.

Question 13

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an S3 bucket. The application is consistently running out of memory and failing. The operator has already increased the Parallelism and TaskManager memory. What is the next BEST step to troubleshoot?

Accepted Answer

Enable Apache Flink metrics in Amazon CloudWatch to monitor heap and checkpoint details. Enabling Apache Flink metrics in Amazon CloudWatch provides visibility into heap usage, checkpoint sizes, and backpressure, which can help diagnose the root cause of memory failures. After increasing parallelism and TaskManager memory without success, the next best step is to monitor these metrics to identify the specific bottleneck. Option A changes processing semantics from exactly-once to at-least-once, which can reduce overhead but does not help diagnose the memory issue and may alter data delivery guarantees. Option B reduces the number of shards in the source stream, which decreases throughput and might not address the underlying memory consumption. Option D increases the buffer timeout for the S3 sink, which could accumulate more data in memory before writing, potentially worsening the memory problem.

Answer

Change the processing mode from exactly-once to at-least-once

Answer

Reduce the number of shards in the source stream

Answer

Increase the buffer timeout for the S3 sink

Question 14

A company stores sensitive customer data in an S3 bucket. The data engineer needs to ensure that all data is encrypted at rest. Which S3 feature should be enabled?

Accepted Answer

Default encryption. Default encryption ensures that all new objects written to the bucket are encrypted at rest using SSE-S3, SSE-KMS, or SSE-C. Option A is incorrect because S3 Versioning tracks object versions but does not encrypt data. Option B is incorrect because S3 Block Public Access controls public access, not encryption. Option C is incorrect because the aws:SecureTransport condition enforces encryption in transit, not at rest.

Answer

S3 Versioning

Answer

S3 Block Public Access

Answer

Bucket policy requiring aws:SecureTransport

Question 15

A data engineer needs to export data from an Amazon DynamoDB table to Amazon S3 for archival purposes. The export should be a one-time operation and must not impact the read capacity of the table. Which approach meets these requirements?

Accepted Answer

Use DynamoDB on-demand backup feature to export to S3. DynamoDB's on-demand backup feature can export table data directly to S3 without consuming any read capacity units, making it ideal for a one-time archival export. Option A (Scan operation) consumes read capacity and impacts table performance. Option B (AWS Glue ETL with DynamoDB connector) also uses Scan operations that consume read capacity. Option C (DynamoDB Streams to Lambda) is designed for continuous change capture, not one-time bulk export.

Answer

Use a Scan operation in a script to read all items and write to S3

Answer

Use AWS Glue ETL with a DynamoDB connector

Answer

Set up a DynamoDB Stream to Lambda that writes to S3