DEA-C01 Practice Test 24 — 15 Questions

Question 1

Refer to the exhibit. A CloudFormation stack outputs the Glue job name and S3 bucket names. The Glue job transforms CSV files from the raw bucket to Parquet in the processed bucket. However, the Glue job is failing with an error that it cannot write to the processed bucket. What is the most likely cause?

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

Accepted Answer

The Glue job does not have permission to write to the processed bucket. The most likely cause is that the Glue job's IAM role lacks the necessary permissions (e.g., s3:PutObject, s3:ListBucket) on the processed bucket. AWS Glue jobs require an IAM role with policies that grant write access to the target S3 bucket; without these permissions, the job fails with a write error. This is a common misconfiguration when the role is scoped only to read from the raw bucket.

Answer

The raw data bucket is in a different region

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The Glue job is not using the correct worker type

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The Glue job is using an incorrect file format

Question 2

A company runs a production Amazon Redshift cluster with a 5-node ra3.4xlarge configuration. The data engineer observes that write operations are failing with 'Disk Full' errors on some nodes. The cluster has not reached its total capacity. What should the engineer do to resolve this issue?

Accepted Answer

Recreate the table with a different distribution style to avoid data skew.. The 'Disk Full' errors on some nodes, despite the cluster not reaching total capacity, indicate data skew caused by an inappropriate distribution style. Recreating the table with a different distribution style (e.g., DISTKEY on a high-cardinality column or DISTSTYLE EVEN) redistributes data evenly across all nodes, eliminating the hot spots that exhaust local disk space.

Answer

Change the sort keys to distribute data evenly.

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Enable compression on all tables.

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Add more nodes to the cluster.

Question 3

A data engineer needs to ingest data from multiple on-premises relational databases into Amazon S3 for analytics. The data must be transformed and loaded daily. Which THREE AWS services should the engineer use together to build this pipeline? (Choose THREE.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless ETL (Extract, Transform, Load) service that can read data from Amazon S3, apply transformations (e.g., using PySpark or Scala), and write the transformed data back to S3. In this pipeline, AWS Glue jobs can be scheduled to run daily to perform the required transformations on the ingested data.

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Amazon Athena

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Amazon Kinesis Data Streams

Question 4

A data engineer is designing a data ingestion pipeline for clickstream data. The data arrives in batches of 10-50 MB every 5 seconds. The engineer needs to buffer the data, perform simple transformations (e.g., add timestamp, remove PII), and land it in S3 within 10 minutes. Which TWO services should be combined? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to ingest streaming data, buffer it, perform simple transformations (such as adding timestamps or removing PII) via built-in Lambda functions, and automatically deliver the data to S3. It can handle the 10-50 MB batches every 5 seconds and meet the 10-minute delivery window without requiring custom code for buffering or delivery.

Answer

Amazon Simple Queue Service (SQS)

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AWS Glue ETL

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Amazon Kinesis Data Streams

Question 5

A data engineer runs the AWS CLI command to retrieve the lifecycle configuration of the 'my-data-lake' bucket. The output is shown in the exhibit. What is the effect of this lifecycle policy?

Accepted Answer

Objects in the 'logs/' prefix are moved to S3 Standard-IA after 30 days, to Glacier after 90 days, and deleted after 365 days.. The lifecycle policy explicitly applies to the 'logs/' prefix, transitioning objects to S3 Standard-IA after 30 days, then to Glacier after 90 days, and finally expiring (deleting) them after 365 days. The 'Expiration' action with 'Days: 365' permanently removes the objects, while the 'Transitions' define the storage class changes at the specified intervals.

Answer

Objects in the 'logs/' prefix are deleted after 365 days and their delete markers are removed.

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All objects in the bucket are moved to STANDARD_IA after 30 days.

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Objects in the 'logs/' prefix are moved to Glacier after 90 days and expired after 90 days.

Question 6

A data engineer is reviewing the S3 Lifecycle policy for a data lake bucket. The goal is to archive log data after 30 days and delete it after 365 days, and delete temporary data after 1 day. What is wrong with the current configuration?

Accepted Answer

The transition to GLACIER should be after 30 days, but the expiration should be after 365 days from the transition, not from creation.. The current lifecycle configuration is correct: it transitions objects to GLACIER after 30 days and expires them 365 days after creation. However, a common mistake is to configure expiration relative to the transition date. Option D identifies this error by stating that expiration should be based on creation date, not transition. The other options are incorrect: A - prefix filters do not require wildcards; B - expiration actions do not need a prior transition; C - S3 Lifecycle can expire GLACIER objects without restoration.

Answer

The prefix filter for the first rule does not include a wildcard, so it may not match all log files.

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The rule for temp data has no transition, so it will not expire objects.

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The expiration for the first rule will not delete objects in GLACIER storage class unless they are restored first.

Question 7

A company is ingesting Apache logs from multiple web servers into AWS. The logs are sent via Amazon CloudWatch Logs to a subscription filter that delivers to a Lambda function. The Lambda function parses the logs and writes to Amazon S3. However, there is a significant backlog. Which THREE actions can reduce the backlog?

Accepted Answer

Increase the Lambda function memory allocation. Increasing the Lambda function's memory allocation also increases its CPU allocation, allowing the function to process each log event faster. This reduces the per-invocation processing time, enabling the function to handle more log data per unit time and thus reduce the backlog.

Answer

Route the CloudWatch Logs subscription to an Amazon SQS queue first

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Change the Lambda function runtime from Python to Node.js

Question 8

A company is using Amazon DynamoDB to store session data for a web application. The data engineer needs to ensure that the data is encrypted at rest. Which action should the data engineer take?

Accepted Answer

Ensure encryption at rest is enabled on the DynamoDB table (default).. DynamoDB tables are encrypted at rest by default using AWS Key Management Service (KMS) with an AWS owned key. The data engineer does not need to take any additional action to enable encryption at rest, as it is automatically enabled for all new DynamoDB tables. This ensures that all data stored on disk, including the table's primary key, local secondary indexes, and global secondary indexes, is encrypted before being written to SSDs in AWS data centers.

Answer

Enable encryption at rest on the DynamoDB Accelerator (DAX) cluster.

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Use client-side encryption before writing to DynamoDB.

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Enable DynamoDB Time to Live (TTL) to encrypt data.

Question 9

A data engineer needs to store semi-structured JSON data that is accessed infrequently but must be retrievable within 5 minutes. The data is immutable once stored. Which storage solution is MOST cost-effective?

Accepted Answer

Amazon S3 Glacier Instant Retrieval. Amazon S3 Glacier Instant Retrieval is the most cost-effective choice for long-term archival of infrequently accessed, immutable data with a 5-minute retrieval requirement. It offers a storage cost of $0.004/GB/month, significantly lower than One Zone-IA at $0.01/GB/month, and retrieval times of milliseconds to minutes, easily meeting the 5-minute requirement. One Zone-IA is cheaper than S3 Standard but more expensive than Glacier Instant Retrieval, and its single-AZ design introduces availability risk. Glacier Deep Archive has a higher retrieval cost and typically takes hours, exceeding the 5-minute requirement.

Answer

Amazon S3 Glacier Deep Archive

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Amazon S3 Standard

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Amazon S3 One Zone-IA

Question 10

A data engineer needs to schedule an AWS Glue ETL job to run every hour and process new data that arrives in an S3 bucket. The job should only process files that have been added since the last run. Which approach should the engineer use to track which files have been processed?

Accepted Answer

Enable job bookmarks in the Glue ETL job.. AWS Glue job bookmarks automatically track the last processed data, allowing the job to incrementally process only new files since the last run. This is the native, built-in mechanism for stateful incremental processing in Glue ETL, eliminating the need for external tracking.

Answer

Configure S3 Event Notifications to trigger the Glue job on each new object creation.

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Store the last processed timestamp in a DynamoDB table and query it at the start of the job.

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Use S3 Inventory to list all objects and filter by last modified date in the job.

Question 11

A data engineer needs to transform data in an S3 data lake using AWS Glue ETL. The data is in CSV format and needs to be converted to Parquet with partitioning by date. The engineer wants to minimize the number of files written to S3 to improve query performance. Which TWO configuration options should the engineer use? (Select TWO.)

Accepted Answer

Use the coalesce method to reduce the number of output partitions.. Using `coalesce` reduces the number of output partitions, which directly minimizes the number of files written to S3. Fewer, larger Parquet files improve query performance by reducing the overhead of file listing and metadata operations in engines like Amazon Athena or Redshift Spectrum.

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Increase the number of workers in the Glue job to increase parallelism.

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Disable compression in the Parquet output.

Answer

Set the 'groupFiles' option to 'inPartition' in the DynamicFrame writer.

Question 12

A company needs to ingest streaming data from thousands of IoT devices. The data must be processed in real-time and stored in Amazon S3. Which TWO services should be used together?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is correct because it provides a scalable, real-time data ingestion service that can capture streaming data from thousands of IoT devices with low latency. Amazon Kinesis Data Firehose is correct because it can directly consume data from Kinesis Data Streams and automatically deliver it to Amazon S3 without requiring custom code for buffering, transformation, or loading.

Answer

AWS Glue

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Amazon Simple Queue Service (SQS)

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AWS Direct Connect

Question 13

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data must be transformed from JSON to Parquet format before delivery. Which approach should be used?

Accepted Answer

Configure Kinesis Data Firehose to invoke a Lambda function for data transformation.. Kinesis Data Firehose can invoke a Lambda function as a transformation step before data is delivered to S3. This allows you to convert JSON records to Parquet format inline, without needing an intermediate storage or separate processing pipeline. The Lambda function receives batches of records, transforms them (e.g., using PyArrow or similar libraries), and returns them to Firehose for delivery.

Answer

Use an AWS Glue ETL job to read from S3 and write Parquet back to S3.

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Use Amazon EMR to process the data and output Parquet.

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Use Kinesis Data Analytics to convert the data to Parquet.

Question 14

A data engineer is setting up cross-account access to an encrypted S3 bucket. The bucket uses a customer-managed KMS key. The engineer has configured the bucket policy and the IAM role in the source account. The target account still gets access denied errors when trying to read objects. What is the most likely cause?

Accepted Answer

The KMS key policy does not grant the target account's IAM role the kms:Decrypt permission.. For cross-account access to an S3 bucket encrypted with a customer-managed KMS key, the KMS key policy must explicitly grant the target account's IAM role the kms:Decrypt permission. Without this, the target account will get access denied errors even if the bucket policy and IAM role are correctly configured. Option B is incorrect because Object Ownership does not affect cross-account read access. Option C is incorrect because the bucket policy only needs to allow the target account's IAM role, not the root user. Option D is incorrect because VPC Endpoint policies are not relevant to this cross-account access issue.

Answer

The S3 bucket has Object Ownership set to BucketOwnerPreferred.

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The bucket policy does not allow the target account's root user.

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The VPC Endpoint policy blocks access from the target account.

Question 15

A company is designing a data ingestion pipeline to load CSV files from an SFTP server into Amazon S3. The files are generated hourly and range from 10 MB to 500 MB. Which AWS service should be used to orchestrate the transfer with minimal operational overhead?

Accepted Answer

AWS Transfer Family. AWS Transfer Family is the correct choice because it provides a fully managed, serverless SFTP endpoint that can directly receive files from an SFTP server and automatically store them in Amazon S3. This eliminates the need to manage any compute infrastructure or write custom code for the transfer, minimizing operational overhead for hourly CSV file ingestion.

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AWS Glue

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Amazon AppFlow

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AWS DataSync