DEA-C01 Practice Test 26 — 15 Questions

Question 1

Refer to the exhibit. A data engineer configured the lifecycle policy shown. The 'logs/' prefix contains important audit logs. After 365 days, what happens to the objects?

Accepted Answer

Objects are permanently deleted.. The lifecycle policy shown has a single rule that expires objects in the 'logs/' prefix after 365 days. In Amazon S3, an 'expiration' action permanently deletes the objects once the specified number of days has passed since object creation. There is no transition action configured, so objects are not moved to any storage class; they are simply deleted.

Answer

Objects are transitioned to Glacier Deep Archive.

Answer

Objects are transitioned to Glacier.

Answer

Objects are transitioned to Standard-IA.

Question 2

Refer to the exhibit. A data engineer runs an AWS Glue ETL job that writes output to an S3 bucket. The job fails with the error shown. What is the most likely cause?

Accepted Answer

The IAM role used by the Glue job lacks the s3:PutObject permission for the output bucket. The error shown in the exhibit indicates an access denied or permission failure when the AWS Glue ETL job attempts to write its output to the S3 bucket. The most likely cause is that the IAM role assigned to the Glue job does not include the s3:PutObject permission for the target bucket, which is required to upload objects. Without this permission, the job cannot complete the write operation, resulting in the failure.

Answer

The Glue job attempted to write data in an unsupported format

Answer

The S3 bucket does not exist

Answer

The output file name contains invalid characters

Question 3

A company uses AWS Glue to run ETL jobs that process data from an Amazon RDS for MySQL database and load it into an Amazon S3 data lake. The Glue job runs daily and processes incremental data. Recently, the job has been taking longer than expected. The engineer checks the CloudWatch logs and sees that the job is spending most of its time on the 'Reading from JDBC' phase. The MySQL table has 10 million rows and is indexed on the primary key. The Glue job uses a 'job bookmark' to track processed data. The engineer wants to improve the performance of the read phase. Which action is most likely to help?

Accepted Answer

Modify the job to use a 'query' parameter that selects only the new or modified rows based on a timestamp column.. Using a 'query' parameter with a timestamp filter allows the Glue job to push down the filtering to the JDBC source, so only new or modified rows are read from MySQL. This drastically reduces the volume of data transferred and the time spent in the 'Reading from JDBC' phase, especially when combined with an index on the timestamp column.

Answer

Increase the JDBC 'fetchSize' parameter to 10000.

Answer

Disable job bookmark and perform a full refresh each time.

Answer

Increase the number of DPUs for the Glue job.

Question 4

A company is storing large amounts of log data in Amazon S3. The data is accessed frequently for the first 30 days, then rarely after that. The company wants to automatically transition the data to a lower-cost storage class after 30 days. Which S3 feature should the data engineer use?

Accepted Answer

S3 Lifecycle policies. S3 Lifecycle policies allow you to define rules that automatically transition objects between storage classes based on age or other criteria. In this scenario, a lifecycle rule can be configured to transition objects from S3 Standard to a lower-cost class like S3 Glacier Deep Archive after 30 days, directly meeting the requirement for automated cost optimization.

Answer

S3 Intelligent-Tiering

Answer

S3 Cross-Region Replication

Answer

S3 Batch Operations

Question 5

A company runs a daily batch processing job on Amazon EMR that reads data from Amazon S3 and writes results back to S3. The job takes longer than expected. The engineer wants to monitor the job's resource utilization. Which AWS service should be used to collect and visualize metrics such as CPU and memory usage of the EMR cluster's nodes?

Accepted Answer

Amazon CloudWatch with the CloudWatch Agent installed on the EMR nodes.. Amazon CloudWatch is the AWS service for monitoring and visualizing metrics. While EMR automatically sends some metrics (like CPU utilization) to CloudWatch, memory usage is not a default metric. To collect memory metrics (and other OS-level metrics) from EMR nodes, the CloudWatch Agent must be installed on each node. This agent collects the desired metrics and publishes them to CloudWatch, where they can be graphed and alarmed. Thus, CloudWatch with the CloudWatch Agent is the correct solution for monitoring CPU and memory usage of EMR cluster nodes.

Answer

AWS Config to record configuration changes in the EMR cluster.

Answer

Amazon Athena to query EMR job logs stored in S3.

Answer

AWS CloudTrail to log API calls made by the EMR job.

Question 6

A data engineer needs to ingest data from multiple SaaS applications (Salesforce, Marketo) into Amazon S3 for a data lake. The data volumes are moderate and the sync needs to be scheduled daily. Which AWS service is most appropriate for this task?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is purpose-built for securely transferring data between SaaS applications (like Salesforce and Marketo) and AWS services (like S3). It supports scheduled, incremental data syncs with built-in connectors, making it the most appropriate choice for moderate-volume daily ingestion into a data lake.

Answer

AWS Glue

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Kinesis Data Firehose

Question 7

A company uses Amazon Redshift for its data warehouse. During a routine audit, the data engineer discovers that some queries are returning stale data even though the underlying source data has been updated. The engineer confirms that the COPY command completes successfully and that no errors are reported. Which action should the engineer take to ensure queries reflect the latest data?

Accepted Answer

Refresh the materialized views that the queries are using.. Materialized views in Amazon Redshift are not automatically refreshed when the underlying base tables are updated via COPY. To ensure queries return the latest data, the materialized views must be manually refreshed using the REFRESH MATERIALIZED VIEW command. Option A (VACUUM) reclaims disk space and re-sorts rows but does not update materialized views. Option B (RESET ALL) clears the query result cache, but stale materialized views persist even if the cache is cleared. Option C (ANALYZE) updates table statistics for query planning, not the actual data content. Therefore, refreshing the materialized views is the necessary action.

Answer

Run the VACUUM command on the source tables.

Answer

Clear the Redshift result cache by running RESET ALL.

Answer

Run the ANALYZE command on the source tables.

Question 8

A company is using an Amazon RDS for MySQL database for its e-commerce platform. During a recent flash sale, the database experienced high read traffic, causing slow query performance. The company needs a solution that offloads read traffic with minimal application changes. Which action should be taken?

Accepted Answer

Create an Amazon RDS read replica in the same region.. Creating an Amazon RDS read replica in the same region offloads read traffic from the primary DB instance by directing read queries to a read-only copy. This requires minimal application changes—only modifying the database connection string to point read queries to the replica endpoint. RDS read replicas use MySQL's native asynchronous replication, making them ideal for scaling read-heavy workloads like flash sales.

Answer

Enable DynamoDB Accelerator (DAX) on the RDS instance.

Answer

Migrate the database to Amazon Aurora and enable Aurora Global Database.

Answer

Implement Amazon ElastiCache for Redis to cache database queries.

Question 9

A data analyst needs to query a large Amazon S3 bucket containing CSV files using Amazon Athena. The bucket has millions of small files (less than 1 MB each). The analyst reports that queries are very slow and often time out. The data is partitioned by date and the partition columns are defined in the table. What is the most effective way to improve query performance?

Accepted Answer

Run a compaction job to consolidate small files into fewer larger files (e.g., 128 MB each).. Many small files (under 1 MB) cause high overhead because each file requires a separate read operation and metadata call. Consolidating them into fewer larger files (e.g., 128 MB each) reduces the number of read operations and improves I/O efficiency, directly addressing the root cause of slowdowns and timeouts. Option A (converting to Parquet) improves storage efficiency and query performance but does not reduce the file count; it is a beneficial addition but not the most effective standalone fix for the small file problem. Option C (adding more partitions) would increase overhead by creating even more directories/files to scan. Option D (S3 Select) applies within individual files and does not mitigate overhead from file quantity.

Answer

Convert the files to Apache Parquet format using an AWS Glue ETL job.

Answer

Add more partitions by including hour and minute as partition keys.

Answer

Use S3 Select to push down filtering to S3 before Athena processes the data.

Question 10

A company is building a data lake on S3 and needs to ingest data from on-premises Oracle database. The data is 5 TB and changes incrementally. The ingestion must capture changes in near real-time (less than 1 minute latency) and be cost-effective. Which approach should be used?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to S3. AWS DMS with ongoing replication captures incremental changes from Oracle using its native change data capture (CDC) mechanism, such as Oracle LogMiner or binary logs, and streams them to S3 in near real-time with latency under 1 minute. This approach is cost-effective because DMS charges only for the compute resources used during replication, and S3 storage is inexpensive, making it ideal for a 5 TB dataset with continuous changes.

Answer

Use Amazon Kinesis Data Firehose with an Oracle JDBC connector

Answer

Use AWS Glue to perform a full table export daily

Answer

Use AWS DataSync to sync the Oracle data files to S3

Question 11

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Aurora MySQL. The migration is successful, but the ongoing replication task is experiencing high latency. Which configuration change is most likely to reduce latency?

Accepted Answer

Increase the size of the DMS replication instance.. Ongoing replication latency in AWS DMS is most commonly caused by insufficient compute, memory, or I/O capacity on the replication instance, especially when CDC processing, transformations, or high transaction volumes are involved. Increasing the replication instance size provides more CPU, memory, and network throughput to process change streams faster. This is the standard first remediation for sustained CDC latency before tuning task-level parameters.

Answer

Decrease the task's batch size and batch apply timeout.

Answer

Change the target endpoint to Amazon S3.

Answer

Enable Change Data Capture (CDC) from binary logs.

Question 12

A company runs a MySQL database on Amazon RDS. The database size is 500 GB and is experiencing high read traffic. The team wants to improve read performance with minimal operational overhead. Which action should they take?

Accepted Answer

Create a read replica in the same region. Creating a read replica in the same region offloads read traffic from the primary RDS instance by providing a separate, read-only copy of the database. This directly addresses high read traffic with minimal operational overhead, as RDS manages the asynchronous replication using MySQL's native binlog-based replication. The replica can serve SELECT queries, reducing load on the primary instance without requiring application changes.

Answer

Enable Multi-AZ deployment

Answer

Implement Amazon ElastiCache for caching

Answer

Upgrade to a larger instance class

Question 13

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?

Accepted Answer

The consumer application is crashing and restarting, causing re-processing of records.. The Kinesis Client Library (KCL) uses DynamoDB to track checkpoint progress for each shard. If the consumer application crashes and restarts, the KCL will resume processing from the last committed checkpoint, which may be behind the actual processing point. This causes records that were already processed (but not yet checkpointed) to be re-processed, leading to duplicate processing.

Answer

The Kinesis stream's iterator age is exceeding the retention period.

Answer

The DynamoDB table used for checkpointing is throttling write requests.

Answer

The record size exceeds the 1 MB API limit, causing retries.

Question 14

Refer to the exhibit. A data engineer notices that the Redshift cluster 'mycluster' does not have automated backups beyond 7 days. However, the compliance team requires a minimum of 35 days of backup retention. What should the engineer do?

Accepted Answer

Modify the cluster's automated snapshot retention period to 35 days.. Amazon Redshift allows you to modify the automated snapshot retention period for a cluster up to 35 days. The engineer can use the AWS Management Console, CLI, or API to change the `automated_snapshot_retention_period` parameter from the current 7 days to 35 days, meeting the compliance requirement without additional manual intervention.

Answer

Change the node type to ra3.xlplus to enable automatic backups for 35 days.

Answer

Enable audit logging to capture changes for recovery.

Answer

Take manual snapshots every day and retain them for 35 days.

Question 15

A company is building a data lake on S3. They have a large volume of CSV files (hundreds of GB) in a source bucket. They need to convert them to Parquet, partition by date, and ensure the data is encrypted at rest with SSE-KMS. The pipeline must be triggered automatically when new files arrive. Which THREE steps should be part of the solution? (Choose THREE.)

Accepted Answer

Configure S3 Event Notification to send events to an SQS queue. S3 Event Notifications can be configured to send events to an SQS queue when new CSV files arrive. This decouples the ingestion pipeline, allowing the Glue job to poll the queue for new file notifications and trigger processing without tight coupling or polling the S3 bucket directly. SQS provides reliable, scalable message delivery that can trigger downstream ETL workflows.

Answer

Use Amazon Kinesis Data Firehose to ingest new files

Answer

Use Amazon Athena CTAS query to convert files in batch