DEA-C01 Timed Practice Test 3 — 10 Questions

Question 1

A data engineer is configuring an Amazon S3 lifecycle policy to transition objects to S3 Glacier Deep Archive after 90 days. The bucket receives new objects daily. The engineer wants to ensure that objects are not deleted before 90 days. Which lifecycle action should be used?

10 minute time limit — choose an answer to begin.

Accepted Answer

Transition. (Transition) is correct because the S3 Lifecycle Transition action moves objects between storage classes over time. To ensure objects are moved to S3 Glacier Deep Archive after 90 days without deletion, a Transition rule is configured to specify the target storage class and the number of days from object creation.

Answer

Expiration

Answer

NoncurrentVersionTransition

Answer

AbortIncompleteMultipartUpload

Question 2

A data engineer is monitoring an Amazon RDS for PostgreSQL instance. The engineer wants to set up alerts for high CPU utilization and low free storage space. Which AWS services can be used together to achieve this? (Choose TWO.)

Accepted Answer

Amazon Simple Notification Service (SNS). Amazon CloudWatch [CORRECT] is the AWS monitoring service that collects RDS for PostgreSQL metrics such as CPUUtilization and FreeStorageSpace, and it lets you create alarms that trigger when those metrics cross defined thresholds. Amazon Simple Notification Service (SNS) [CORRECT] is the notification service that CloudWatch alarms publish to, delivering email, SMS, or other messages to the engineer when high CPU or low free storage is detected. Together, CloudWatch alarms plus an SNS topic form the standard alerting pipeline for RDS metric thresholds. AWS CloudTrail records API activity and audit events, not performance metrics, so it cannot detect high CPU or low storage. AWS Config evaluates resource configuration compliance and does not monitor runtime utilization metrics. Amazon Route 53 is a DNS and traffic-routing service and has no role in RDS metric alerting.

Answer

AWS CloudTrail

Answer

AWS Config

Answer

Amazon Route 53

Question 3

A company uses Amazon Redshift for its data warehouse. The data engineering team loads data daily from Amazon S3 using COPY commands. Recently, the load performance has degraded because the S3 bucket contains many small files. The team needs to optimize the COPY operation to improve performance. Which approach should they take?

Accepted Answer

Use a manifest file that lists only the necessary files, and consolidate small files into larger ones before loading.. The performance degradation is caused by the overhead of processing many small files during the COPY command. Consolidating small files into larger ones (e.g., 100 MB–1 GB each) reduces the number of S3 GET requests and the metadata overhead on Redshift, directly improving load throughput. Using a manifest file further optimizes by explicitly listing only the required files, avoiding unnecessary S3 list operations.

Answer

Use Redshift Spectrum to query the data directly from S3 without loading.

Answer

Increase the number of nodes in the Redshift cluster.

Answer

Enable automatic compression on the Redshift table.

Question 4

A data engineer is designing a data lake on Amazon S3 that will store sensitive financial data. The engineer needs to implement encryption at rest and ensure that only authorized users can access the data. Which TWO actions should the engineer take to meet these requirements? (Choose TWO.)

Accepted Answer

Configure a bucket policy that denies writes if the object is not encrypted.. A bucket policy with a condition that denies writes if the object is not encrypted (e.g., using `s3:x-amz-server-side-encryption` or `s3:PutObject` with `aws:SecureTransport`) enforces encryption at rest at the time of upload. This ensures that all objects written to the bucket are encrypted, meeting the encryption requirement without relying on client-side behavior.

Answer

Use server-side encryption with customer-provided keys (SSE-C).

Answer

Enable S3 Transfer Acceleration for the bucket.

Answer

Enable object-level access control lists (ACLs).

Question 5

A company has CSV files in an S3 bucket that need to be converted to Parquet and loaded into a Redshift table daily. The transformation is a simple schema mapping without joins. Which AWS Glue feature is BEST suited for this task?

Accepted Answer

AWS Glue ETL job. AWS Glue ETL jobs are purpose-built for extracting data from sources like S3, applying transformations (including schema mapping and format conversion), and loading the results into targets like Redshift. For a simple CSV-to-Parquet conversion with schema mapping and no joins, a Glue ETL job using the built-in transforms or a Spark script is the correct tool. It handles the conversion and load in a single managed job.

Answer

AWS Glue DataBrew

Answer

AWS Glue Workflow

Answer

AWS Glue Crawler

Question 6

Refer to the exhibit. A data engineer runs the above CLI command to find files smaller than 1000 bytes in a bucket. The command returns an empty array, but the engineer knows there are small files. What is the issue?

Accepted Answer

The Size is compared as a string, not an integer; remove quotes around '1000'.. In the AWS CLI `list-objects-v2` command with `--query`, the `Size` field is a numeric value, but the query string `Size < '1000'` compares it as a string. This causes a lexicographic comparison, so files with sizes like '900' would be correctly matched, but any size with more digits (e.g., '1000' itself or '999') may fail due to string ordering. Removing the quotes around `1000` treats it as an integer, enabling proper numeric comparison.

Answer

The prefix is incorrect; it should be 'logs/2023/01/01/'.

Answer

The bucket policy does not allow listing objects.

Answer

The query syntax is invalid; use a filter instead.

Question 7

A company runs a time-series forecasting model that writes results to an S3 bucket every 5 minutes. A downstream ETL job reads this data, but sometimes fails because it encounters incomplete files (zero bytes). What is the MOST reliable way to ensure the ETL job only processes complete files?

Accepted Answer

Use S3 Event Notifications to trigger a Lambda function that checks file size and then moves the file to a 'ready' prefix.. S3 Event Notifications can trigger a Lambda function upon object creation, which can check the file size (e.g., > zero bytes) and then copy the file to a 'ready' prefix, ensuring the ETL job only processes complete files. Option A is wrong because an S3 Lifecycle policy can delete small files but does not prevent the ETL from reading incomplete files. Option B is wrong because S3 Copy does not verify completeness. Option C is wrong because S3 Select still reads the file even if it is incomplete; it doesn't guarantee completeness.

Answer

Set an S3 Lifecycle policy to delete files smaller than 1 MB.

Answer

Use S3 Copy to move files to a 'processed' folder after the ETL job reads them.

Answer

Configure S3 Select to query the files and only return rows if the file is complete.

Question 8

Which TWO actions should a data engineer take to optimize Amazon S3 query performance for Amazon Athena when dealing with large Parquet files? (Choose 2.)

Accepted Answer

Optimize file sizes to be around 64 MB to 256 MB. Option D is correct because Athena's performance degrades with very large Parquet files since a single file can only be read by one split; targeting roughly 64 MB to 256 MB per file allows Athena's split-based parallel reads to distribute work across more nodes. Option E is correct because partitioning data by frequently filtered columns (e.g., date, region) enables partition pruning, so Athena scans only the relevant S3 prefixes instead of the entire dataset, drastically reducing bytes scanned and query time. Option A is wrong because a single large unpartitioned file forces full-table scans and prevents parallelism. Option B is wrong because Parquet already uses columnar compression internally, and GZIP is not splittable, which can actually hurt parallelism for large files. Option C is wrong because splitting into many tiny files creates excessive per-file overhead (metadata, open/close costs) and degrades Athena performance rather than improving it.

Answer

Store data in a single large file without partitioning

Answer

Use GZIP compression on the Parquet files

Answer

Split large files into many small files

Question 9

A data engineer is designing a real-time streaming pipeline to ingest clickstream data from a website into Amazon S3. The data must be transformed before storage. Which TWO AWS services can be used together to build this pipeline? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. The correct combination is Amazon Kinesis Data Streams to ingest the streaming clickstream data, and Amazon Kinesis Data Firehose to read from the stream, perform transformations (e.g., via Lambda), and deliver the transformed data to Amazon S3. Kinesis Data Streams provides the durable ingestion layer, while Kinesis Data Firehose handles the delivery and transformation without managing servers.

Answer

AWS Glue

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Database Migration Service (DMS)

Question 10

Which TWO AWS services can be used to ingest streaming data into Amazon S3? (Choose two.)

Accepted Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into Amazon S3. It can capture, transform, and deliver streaming data to S3 destinations in near real-time with no code required. Amazon MSK (Managed Streaming for Apache Kafka) can also ingest streaming data into S3 by using Kafka Connect with an S3 sink connector, which writes data from Kafka topics directly to S3.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Elastic Block Store (Amazon EBS)

Answer

AWS Snowball