DEA-C01 Practice Test 38 — 15 Questions

Question 1

A company is using Amazon EMR to process large datasets stored in Amazon S3. The data engineer wants to reduce the time it takes to read data from S3 by optimizing the data format. Which file format should the engineer recommend?

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

Accepted Answer

Parquet. Parquet is the correct choice because it is a columnar storage format that significantly reduces the amount of data read from Amazon S3 during analytical queries. By storing data column-wise, Parquet enables predicate pushdown and compression, which minimizes I/O and speeds up data processing in Amazon EMR, especially for large datasets.

Answer

CSV

Answer

ORC

Answer

JSON

Question 2

A data engineer is designing a data pipeline that ingests streaming data from an IoT device fleet. The data must be processed in near real-time and stored in Amazon S3 for long-term analytics. Which TWO AWS services should the engineer use together to achieve this?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Streams (Option D) enables real-time ingestion of streaming data from IoT devices. Amazon Kinesis Data Firehose (Option C) can consume data from a Kinesis Data Stream and deliver it to Amazon S3 for long-term analytics. Option A (Athena) is a query service, not an ingestion or delivery service. Option B (AWS Glue) is designed for batch ETL, not real-time streaming. Option E (SQS) is a message queue service, not optimized for real-time streaming and delivery to S3.

Answer

Amazon Athena

Answer

AWS Glue

Answer

Amazon Simple Queue Service (SQS)

Question 3

A data engineer is designing a solution to securely store and rotate database credentials used by an application. The credentials should be automatically rotated every 90 days. Which AWS service should be used?

Accepted Answer

AWS Secrets Manager. (AWS Secrets Manager) is correct because it can automatically rotate secrets, including database credentials, with built-in rotation support. Option B (AWS Systems Manager Parameter Store) can store secrets but lacks automatic rotation capability. Option C (AWS Key Management Service) manages encryption keys, not secrets. Option D (AWS Identity and Access Management) manages users and roles, not credential rotation.

Answer

AWS Systems Manager Parameter Store

Answer

AWS Key Management Service (KMS)

Answer

AWS Identity and Access Management (IAM)

Question 4

A company is migrating its on-premises data warehouse to Amazon Redshift. The data includes tables with up to 100 columns and 500 million rows. The migration involves a full load followed by incremental updates. The company needs to minimize downtime during the final cutover. Which THREE strategies should the data engineer use to facilitate the migration? (Choose THREE.)

Accepted Answer

Use the COPY command to load data from Amazon S3.. The COPY command is the most efficient way to load data from Amazon S3 into Redshift, enabling high-speed parallel ingestion. Option C is correct because using columnar formats like Parquet minimizes data scanned and reduces storage costs, speeding up data transfer. Option D is correct because running VACUUM and ANALYZE after loading reorganizes data and updates statistics, optimizing query performance. Option A is incorrect because increasing WLM queues does not improve COPY performance; COPY operations bypass WLM. Option E is incorrect because disabling distribution keys can cause data skew and degraded performance; proper distribution keys are essential for efficient cluster operation.

Answer

Increase the number of WLM queues to allow more concurrent loads.

Answer

Disable distribution keys on the target tables to simplify loading.

Question 5

A data pipeline uses AWS Glue to run ETL jobs that read from and write to an Amazon Redshift cluster. The pipeline recently started failing with the error 'ERROR: cannot execute INSERT in a read-only transaction'. The Glue job's IAM role has the necessary permissions. What could be the cause of this error?

Accepted Answer

The Glue job is using a transaction that was opened in read-only mode.. The error 'ERROR: cannot execute INSERT in a read-only transaction' occurs when attempting to write data within a transaction that was opened as read-only. In AWS Glue ETL jobs, if the Spark session is configured with `auto_commit=False` or if the job explicitly starts a read-only transaction (e.g., via `SET TRANSACTION READ ONLY`), any subsequent INSERT operation will fail. Option A correctly identifies this cause. Option B is incorrect because Redshift clusters do not enter a read-only mode during maintenance; instead, they are briefly unavailable. Option C is incorrect because Glue connections do not have a 'read-only' property; the connection string may set `defaultTransactionIsolation`, but not a read-only flag. Option D is incorrect because the IAM role has necessary permissions per the question stem, and insufficient permissions would produce a different error (e.g., permission denied).

Answer

The Redshift cluster is in read-only mode due to maintenance.

Answer

The Glue connection is configured with 'read-only' set to true.

Answer

The Glue job's IAM role does not have sufficient Redshift permissions.

Question 6

A 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?

Accepted Answer

Change the worker type from G.1X to G.2X. The G.1X worker type provides 16 GB of memory per worker. An OutOfMemoryError indicates that the job's memory requirements exceed this limit. Upgrading to G.2X doubles the memory per worker to 32 GB, directly addressing the memory shortage without changing the parallelism or incurring the overhead of additional workers.

Answer

Increase the number of workers to 20

Answer

Change the worker type from G.1X to G.8X

Answer

Enable the Spark UI to monitor memory and tune the job

Question 7

A company has an Amazon DynamoDB table with a provisioned write capacity of 1000 WCU. During a flash sale, the write traffic spikes to 5000 WCU for 10 minutes. The table is not auto-scaled. Which action should the data engineer take to handle the spike without throttling?

Accepted Answer

Convert the table to on-demand capacity mode before the sale.. The table is currently provisioned with 1000 WCU and cannot handle a spike to 5000 WCU. Converting to on-demand mode before the sale allows DynamoDB to automatically handle varying traffic without throttling, as on-demand capacity scales instantly to meet demand. Option D (auto-scaling) might not react quickly enough for a short 10-minute spike, and the table is not currently auto-scaled. Option C is incorrect because DAX is a read cache and does not buffer or improve write capacity. Option B is reactive and would not prevent initial throttling.

Answer

Set a CloudWatch alarm to increase provisioned capacity when write throttling occurs.

Answer

Use DynamoDB Accelerator (DAX) to cache writes.

Answer

Enable auto-scaling with a target utilization of 70% and a maximum capacity of 5000 WCU.

Question 8

A data engineer is monitoring an Amazon Redshift cluster and notices that the disk space usage is increasing rapidly. The engineer wants to reclaim space from deleted rows. Which command should the engineer run?

Accepted Answer

VACUUM. The VACUUM command in Amazon Redshift reclaims disk space from deleted rows and reorganizes the data to improve query performance. Option B is wrong because ANALYZE updates statistics for the query optimizer, not reclaims space. Option C is wrong because UNLOAD exports data from the cluster to Amazon S3. Option D is wrong because COPY loads data from files into the cluster.

Answer

ANALYZE

Answer

UNLOAD

Answer

COPY

Question 9

A company uses Amazon S3 to store log files that are generated every hour. Each log file is about 1 GB. The logs must be stored for 5 years for compliance. The data engineer wants to minimize storage costs while ensuring that logs can be retrieved within 24 hours for the first year, and within 48 hours thereafter. Which TWO lifecycle actions should the engineer configure? (Choose TWO.)

Accepted Answer

Transition objects to S3 Glacier Deep Archive after 1 year.. Transitioning objects to S3 Glacier Deep Archive after 1 year provides the lowest storage cost while meeting the 5-year retention requirement. After the first year, the retrieval window of up to 48 hours for Glacier Deep Archive satisfies the compliance need. Option E is correct because after 30 days, logs are less frequently accessed but still need retrieval within 24 hours during the first year; S3 Standard-IA offers lower cost than S3 Standard for such infrequent access. Option A is incorrect because transitioning to S3 Standard after 30 days is more expensive than Standard-IA and does not provide cost savings. Option C is incorrect because 'Set a retrieval window of 48 hours for Glacier Deep Archive' is not a lifecycle action; retrieval windows are configured during restore operations, not in lifecycle rules. Option D is incorrect because deleting objects after 2 years violates the 5-year retention policy.

Answer

Transition objects to S3 Standard after 30 days.

Answer

Set a retrieval window of 48 hours for Glacier Deep Archive.

Answer

Delete objects after 2 years to reduce storage costs.

Question 10

A data engineer is configuring S3 bucket policies to restrict access to a specific VPC. Which condition key should be used in the bucket policy to enforce that requests originate only from the desired VPC?

Accepted Answer

aws:SourceVpc. Aws:SourceVpc is the condition key used in S3 bucket policies to restrict access to requests originating from a specific VPC. Option A is incorrect because aws:VpcSourceIp is not a valid AWS condition key. Option C is incorrect because aws:RequestedRegion is used to restrict based on the region, not VPC. Option D is incorrect because aws:SourceIp restricts based on IP addresses, not VPC.

Answer

aws:VpcSourceIp

Answer

aws:RequestedRegion

Answer

aws:SourceIp

Question 11

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The delivery occasionally fails due to 'ThrottlingException' from S3. What should the team do to resolve this issue without losing data?

Accepted Answer

Increase the Firehose buffer size and buffer interval to reduce the number of S3 PUT requests.. The ThrottlingException from S3 indicates that Kinesis Data Firehose is sending too many PUT requests to S3. Increasing the buffer size or buffer interval causes Firehose to accumulate more records before writing, reducing the number of PUT requests and preventing throttling. Option A (S3 Transfer Acceleration) improves transfer speed, not request rate limits. Option B (disabling error logging) does not reduce API calls and hides issues. Option C (deliver to DynamoDB) is not supported by Kinesis Data Firehose. Therefore, option D is correct.

Answer

Enable S3 Transfer Acceleration on the destination bucket.

Answer

Disable error logging in Firehose to reduce API calls.

Answer

Configure Firehose to deliver data to Amazon DynamoDB instead.

Question 12

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

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose (Option B) is a fully managed service that can ingest streaming data and deliver it to Amazon S3 with minimal configuration and no code required. Amazon MSK with the S3 sink connector (Option C) allows streaming data from Apache Kafka topics to be automatically written to S3 with minimal code, as the connector handles the integration. Option A (AWS Lambda) requires custom code to process and write data to S3. Option D (AWS DMS) is designed for database migration, not streaming ingestion. Option E (AWS DataSync) is for batch file transfers, not real-time streaming.

Answer

AWS Lambda

Answer

AWS Database Migration Service (DMS)

Answer

AWS DataSync

Question 13

Refer to the exhibit. A data engineer created this IAM policy for a Lambda function that reads from a Kinesis stream and writes to an S3 bucket. The Lambda function fails with an 'AccessDenied' error when trying to write to S3. What is the missing permission?

Accepted Answer

s3:PutObjectAcl on the bucket. The IAM policy includes s3:PutObject, which is sufficient for writing objects to S3. The AccessDenied error indicates the bucket policy or the resource ARN in the policy is misconfigured. Among the given options, s3:PutObjectAcl might be required if the bucket is configured to require ACLs on write operations.

Answer

s3:ListBucket on the bucket

Answer

s3:GetObject on the bucket

Answer

s3:DeleteObject on the bucket

Question 14

A company is using AWS Glue to process data from an Amazon S3 data lake. The Glue job runs daily and transforms data into multiple output formats. Which TWO actions can the company take to optimize the Glue job's performance and reduce costs? (Choose TWO.)

Accepted Answer

Increase the number of DPUs allocated to the job.. Options A and E are correct. Increasing the number of DPUs (A) can parallelize processing and improve performance for data-intensive Glue jobs, potentially reducing runtime and cost if the job runs faster. Enabling job bookmarking (E) allows Glue to track processed data and process only new or changed data in incremental runs, reducing processing time and cost by avoiding full reprocessing. Option B (reducing DPUs) would likely decrease performance. Option C (disabling bookmarking) would force full reprocessing, increasing cost and time. Option D (increasing job timeout) does not optimize performance or cost; it only allows the job to run longer, which could increase cost if the job is inefficient.

Answer

Reduce the number of DPUs (Data Processing Units) allocated to the job.

Answer

Disable job bookmarking to force full reprocessing every run.

Answer

Increase the job timeout to allow more time for processing.

Question 15

A company wants to ingest data from a SaaS application into Amazon S3. The SaaS application supports streaming data via HTTP POST requests. The data volume is approximately 100 MB per hour, and the company needs to store the raw data in S3 for archival and later analysis. Which approach is the most cost-effective and operationally efficient?

Accepted Answer

Use Amazon API Gateway to create a REST API that receives the data and triggers an AWS Lambda function to store it in S3.. Amazon API Gateway combined with AWS Lambda is the most appropriate and cost-effective approach for this use case. API Gateway provides a fully managed HTTP endpoint that can receive the HTTP POST requests from the SaaS application. The data is then passed to a Lambda function, which writes the raw data directly to Amazon S3. This serverless architecture eliminates the need to manage servers, scales automatically, and incurs cost only when data is processed. Option B is incorrect because Amazon Kinesis Data Firehose does not natively support HTTP endpoints as a source; it can ingest data from Kinesis Data Streams, AWS IoT, or custom agents, but not directly via HTTP POST.

Answer

Launch a t3.nano EC2 instance that runs a script to receive HTTP POST requests and write to S3.

Answer

Use Amazon Kinesis Data Firehose with HTTP endpoint as the source, and configure S3 as the destination.

Answer

Use Amazon Simple Queue Service (SQS) to queue the HTTP POST data and have an AWS Lambda function read from SQS and write to S3.