Amazon Web Services · Free Practice Questions · Last reviewed May 2026
24real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
A data engineer needs to ingest streaming data from an IoT fleet into Amazon S3 for near-real-time analytics. The data volume is approximately 5 GB per hour, and each event is less than 1 KB. Which AWS service should be used as the ingestion endpoint?
AWS IoT Core
Designed for IoT device ingestion.
AWS DataSync
Amazon AppFlow
Amazon Kinesis Data Streams
A company uses AWS Glue ETL jobs to transform data from Amazon S3 to Amazon Redshift. The job reads JSON files, applies schema mapping, and writes to a Redshift table. Recently, the job started failing with memory errors. The data volume has increased tenfold. Which approach should a data engineer take to resolve this issue with minimal code changes?
Switch from Spark to Python Shell job type.
Implement batch processing with smaller file sizes.
Increase the number of DPUs allocated to the Glue job.
Provides more resources for processing.
Use Redshift Spectrum to query data directly from S3.
A financial services company processes real-time stock trade data. They use Amazon Kinesis Data Streams with a shard count of 5, each shard receiving about 500 records per second. The consumer application uses the Kinesis Client Library (KCL) with DynamoDB for checkpointing. Lately, some records are being processed multiple times. What is the most likely cause?
The consumer application is crashing and restarting, causing re-processing of records.
KCL reprocesses from last checkpoint after failure.
The Kinesis stream's iterator age is exceeding the retention period.
The DynamoDB table used for checkpointing is throttling write requests.
The record size exceeds the 1 MB API limit, causing retries.
A data engineering team needs to transform CSV files stored in Amazon S3 into Parquet format using AWS Glue. The files are partitioned by date and are updated hourly. Which AWS Glue feature should be used to automatically detect the schema and partition structure?
AWS Glue Crawler
Discovers schema and partitions automatically.
AWS Glue DataBrew
AWS Lake Formation
Amazon Athena
An e-commerce company ingests clickstream data from their website into Amazon S3. The data is in JSON format, and each file is about 10 MB. They need to transform the data into a columnar format for analytics and load it into Amazon Redshift nightly. The transformation should be cost-effective and require minimal operational overhead. Which approach meets these requirements?
Use AWS Glue ETL job to convert to Parquet and load into Redshift.
Serverless and minimal overhead.
Use Amazon Redshift COPY command to load JSON directly.
Use Amazon EMR with Spark to transform and load data.
Use AWS Lambda to transform each file and write to Redshift.
A company uses AWS Database Migration Service (DMS) to continuously replicate data from an on-premises Oracle database to Amazon S3 in Parquet format. The replication is used for near-real-time analytics. Recently, the DMS task started failing with an error indicating insufficient memory. The source database is large (2 TB). What should a data engineer do to resolve this issue while minimizing changes to the existing architecture?
Change the target format to JSON to reduce memory usage.
Split the DMS task into multiple smaller tasks.
Use Change Data Capture (CDC) only, without full load.
Increase the DMS replication instance size.
Provides more memory.
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Practice this domainA data engineer notices that an AWS Glue job processing data from an Amazon S3 bucket frequently fails with 'OutOfMemoryError'. The job reads CSV files, applies transformations, and writes Parquet to another S3 bucket. The job has 10 workers of type G.1X. Which change is MOST likely to resolve the issue?
Change the worker type from G.1X to G.2X
G.2X provides 2x the memory of G.1X, directly addressing the OutOfMemoryError.
Increase the number of workers to 20
Change the worker type from G.1X to G.8X
Enable the Spark UI to monitor memory and tune the job
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?
Switch to Amazon Kinesis Data Firehose to deliver data directly to S3
Increase the batch size of records written to S3
Increase the number of shards in the Kinesis stream
More shards increase parallelism and throughput, allowing the consumer to keep up.
Reduce the retention period of the stream
A data team runs a daily AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes results to Amazon S3. The job completes successfully but takes 2 hours longer than expected. The job uses the JDBC connection to Redshift. The Redshift cluster is 4 dc2.large nodes. The Glue job has 10 workers of type G.1X. Which change would MOST likely reduce the job duration?
Use Redshift Spectrum to query data directly from S3
Use the S3 staging option in the Glue connection to unload data from Redshift to S3 first
UNLOAD is parallel and faster than JDBC; Glue can then read from S3.
Increase the Redshift cluster size to 8 nodes
Increase the number of Glue workers to 20
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?
Use DynamoDB Streams to capture changes and process them incrementally
Reduce the number of DynamoDB read segments in the Glue job
Use the DynamoDB export to S3 feature and read the exported data from S3
Export to S3 reads from the table without consuming RCU, avoiding throttling entirely.
Increase the read capacity units (RCU) of the DynamoDB table
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?
Switch from SQL to Apache Flink for the analytics application
Increase the number of shards in the source Kinesis stream
Increase the Parallelism setting of the Kinesis Data Analytics application
Higher parallelism increases processing capacity, reducing lag.
Decrease the window duration of the SQL query
A data engineer is troubleshooting an AWS Glue job that reads from an Amazon RDS for PostgreSQL database using a JDBC connection. The job fails with the error 'java.sql.SQLException: No suitable driver'. Which TWO actions should the engineer take to resolve this issue? (Select TWO.)
Verify that the connection string in the job's JDBC URL uses the correct format and includes the driver class
The JDBC URL must be correctly formatted, e.g., 'jdbc:postgresql://...'.
Check that the Glue job's VPC and security groups allow outbound traffic to the RDS instance
Restart the Glue job with a higher timeout value
Include the PostgreSQL JDBC driver JAR as a dependent library in the Glue job
Glue jobs need the driver JAR to be explicitly provided for non-AWS databases.
Update the IAM role associated with the Glue job to allow 'rds:*' permissions
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Practice this domainA data engineer needs to ensure that an Amazon S3 bucket used for sensitive data is encrypted at rest using a customer-managed AWS KMS key. The bucket policy must enforce encryption for all PUT requests. Which policy statement should be added to the bucket policy?
{"Effect":"Deny","Principal":"*","Action":"s3:PutObject","Resource":"arn:aws:s3:::bucket/*","Condition":{"Null":{"s3:x-amz-server-side-encryption":"true"}}}
{"Effect":"Allow","Principal":"*","Action":"s3:PutObject","Resource":"arn:aws:s3:::bucket/*","Condition":{"StringEquals":{"s3:x-amz-server-side-encryption-aws-kms-key-id":"arn:aws:kms:us-east-1:123456789012:key/abc123"}}}
{"Effect":"Deny","Principal":"*","Action":"s3:PutObject","Resource":"arn:aws:s3:::bucket/*","Condition":{"StringNotEquals":{"s3:x-amz-server-side-encryption":"aws:kms"},"Null":{"s3:x-amz-server-side-encryption-aws-kms-key-id":"true"}}}
This denies if encryption is not aws:kms or if the key ID is not provided, enforcing the required encryption.
{"Effect":"Deny","Principal":"*","Action":"s3:PutObject","Resource":"arn:aws:s3:::bucket/*","Condition":{"StringNotEquals":{"s3:x-amz-server-side-encryption-aws-kms-key-id":"arn:aws:kms:us-east-1:123456789012:key/abc123"}}}
A company uses AWS Glue to process sensitive data stored in Amazon S3. The security team requires that all data in transit between AWS Glue and S3 be encrypted. Which configuration should be used to meet this requirement?
Use an S3 bucket policy that denies requests not using HTTPS.
This enforces encryption in transit for all requests.
Use an AWS KMS key to encrypt the data before uploading to S3.
Configure AWS Glue to use SSL by setting the 'ssl' parameter to 'true'.
Enable default encryption on the S3 bucket using SSE-S3.
A data engineer is troubleshooting an AWS Lake Formation permissions issue. A user is able to query an Amazon Athena table but cannot see the underlying S3 data in the AWS Glue Data Catalog. The user has been granted SELECT permission on the table in Lake Formation. What is the most likely cause?
The user does not have DESCRIBE permission on the table in Lake Formation.
SELECT permission allows querying but not viewing the table metadata; DESCRIBE is needed to see the table in the catalog.
The data location is not registered with Lake Formation.
The S3 bucket policy does not grant the user access.
The user does not have the aws:SourceArn condition in the IAM policy.
A company uses Amazon Redshift to store customer data. The security team requires that all queries are logged for auditing purposes. Which combination of steps should be taken to meet this requirement? (Select TWO.)
Enable AWS CloudTrail database audit logging.
Use AWS CloudTrail to log Redshift API calls.
CloudTrail logs API calls, including query execution.
Enable logging on the Redshift security group.
Enable VPC Flow Logs for the Redshift cluster.
Enable Amazon Redshift audit logging to an S3 bucket.
This logs all SQL queries.
A company is designing a data lake on Amazon S3. The security policy requires that all data be encrypted at rest using AWS KMS with automatic key rotation. Which encryption option meets these requirements? (Select THREE.)
Enable automatic key rotation on the KMS key.
This is a requirement.
Use SSE-KMS with an AWS managed key.
Set the default encryption on the S3 bucket to SSE-KMS with the CMK.
This ensures all objects are encrypted.
Use SSE-KMS with a customer-managed key (CMK).
CMKs support automatic rotation.
Use SSE-C with a customer-provided key.
Refer to the exhibit. A data engineer is running an AWS Glue job that reads from an S3 bucket encrypted with a customer-managed KMS key. The job fails with the error shown. What is the most likely cause?
The S3 bucket policy denies the kms:Decrypt action.
The IAM role used by the Glue job is missing the kms:Decrypt permission.
The error says no identity-based policy allows kms:Decrypt.
The Glue job does not have permission to call kms:GenerateDataKey.
The KMS key policy does not grant the Glue service principal access.
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Practice this domainA 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?
Modify the read replica to be a Multi-AZ deployment and failover will occur.
RDS automatically fails over to the read replica within 5 minutes.
Manually promote the read replica to a standalone DB instance.
This is the correct procedure to make the read replica the new primary.
Delete the primary and the read replica will automatically become the primary.
A data engineering team uses Amazon Redshift for analytics. They notice that queries on a large fact table are slow. The table is distributed using DISTSTYLE ALL. Which design change would most likely improve query performance?
Change DISTSTYLE to EVEN to distribute rows evenly across slices.
Increase the number of nodes in the Redshift cluster.
Change the table to use a SORTKEY on the most frequently filtered column.
Change DISTSTYLE to KEY on a column used in frequent joins.
KEY distribution collocates rows on the same node, reducing data movement during joins.
A company uses Amazon DynamoDB for a gaming application. They need to store player session data that expires after 24 hours. Which DynamoDB feature should they use to automatically delete expired items?
Time to Live (TTL)
TTL automatically deletes expired items based on a timestamp attribute.
DynamoDB auto scaling
DynamoDB Streams
Point-in-time recovery
Which TWO actions should a data engineer take to encrypt data at rest in an Amazon S3 bucket? (Select TWO.)
Enable S3 Transfer Acceleration on the bucket.
Use client-side encryption before uploading objects to S3.
Configure the bucket to use SSE-KMS.
SSE-KMS encrypts objects at rest using AWS KMS keys.
Enable default encryption on the bucket using SSE-S3.
SSE-S3 encrypts objects at rest with S3-managed keys.
Attach a bucket policy that denies unencrypted PUT requests.
A data engineer is designing a data lake on Amazon S3. The data must be immutable and support high-throughput streaming ingestion. Which THREE features should the engineer consider? (Select THREE.)
S3 Transfer Acceleration
S3 Lifecycle policies to transition data to Amazon S3 Glacier
Lifecycle policies automate data movement, cost-effectively managing the data lifecycle.
S3 Multipart Upload API
Multipart Upload allows large objects to be uploaded in parts, improving throughput for streaming ingestion.
S3 Object Lock in governance mode
Object Lock prevents objects from being deleted or overwritten for a specified period, providing immutability.
S3 Cross-Region Replication (CRR)
An e-commerce application uses Amazon ElastiCache for Redis to cache product catalog data. The cache currently uses lazy loading. The team wants to ensure that frequently accessed product data is always fresh. Which caching strategy should they implement?
Write-through caching
Write-through updates cache directly on writes, ensuring data is always fresh.
Set a TTL of 5 minutes for all cached items
Use database read replicas to serve data
Lazy loading with TTL
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Practice this domainThe DEA-C01 exam has 65 questions and must be completed in 130 minutes. The passing score is 720/1000.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 4 domains: Data Ingestion and Transformation, Data Operations and Support, Data Security and Governance, Data Store Management. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Amazon Web Services DEA-C01 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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