DEA-C01 Practice Test 47 — 15 Questions

Question 1

A data engineer is designing a data ingestion pipeline to load millions of small JSON files from an on-premises FTP server into Amazon S3. The pipeline should minimize cost and operational overhead. Which approach is most suitable?

Accepted Answer

Deploy AWS DataSync to transfer files from the FTP server to S3. Option C is correct because AWS DataSync can transfer data from on-premises to S3 efficiently, handling many small files. Option A is wrong because S3 Transfer Acceleration speeds up transfers over long distances but requires S3 API access. Option B is wrong because Direct Connect provides dedicated network but still needs a transfer mechanism. Option D is wrong because Snowball Edge is for large data volumes and incurs shipping delays.

Answer

Use S3 Transfer Acceleration to upload files directly from the FTP server

Answer

Use AWS Snowball Edge to ship the data to AWS

Answer

Set up an AWS Direct Connect connection and use AWS CLI to copy files

Question 2

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data engineer needs to transform the data before delivery. Which THREE options can be used to perform the transformation?

Accepted Answer

Amazon Kinesis Data Firehose data format conversion (e.g., JSON to Parquet). Options A, C, and D are correct. Lambda can transform records, Firehose can convert to Parquet/ORC, and Firehose can invoke Lambda for dynamic partitioning. Option B (Glue) is not integrated with Firehose directly. Option E (Athena) is for querying.

Answer

Amazon Athena queries

Answer

AWS Glue ETL job

Question 3

A company uses Amazon Kinesis Data Streams to ingest clickstream data from a website. The data must be transformed (e.g., enrich with user location) before being stored in Amazon S3. Which architecture is MOST efficient for this transformation?

Accepted Answer

Use Amazon Kinesis Data Analytics to transform the stream and output to Amazon Kinesis Data Firehose, which writes to S3.. Option B is correct because Kinesis Data Analytics can run SQL on the stream for enrichment, then output to Firehose for delivery to S3. Option A is wrong because Lambda can process each record but may be slower and more expensive for high throughput. Option C is wrong because Glue is batch, not streaming. Option D is wrong because EMR is more complex than needed.

Answer

Use AWS Glue to run a streaming ETL job.

Answer

Use Amazon EMR to consume the stream using Spark Streaming.

Answer

Use AWS Lambda to process each record from the stream and write to S3.

Question 4

A data engineering team is troubleshooting a slow AWS Glue ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3 in Parquet format. The job processes 50 GB of data. Which action would most effectively improve job performance?

Accepted Answer

Increase the number of DPUs. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job provides more parallelism and memory, which can significantly speed up processing. Using G.1X worker type with more memory can also help. Using S3 Select is for filtering within S3, not for DynamoDB. Changing to JSON format may reduce performance due to larger file size. Reducing batch size could slow down the job.

Answer

Use S3 Select to push down filters

Answer

Reduce the batch size in the DynamoDB connector

Answer

Change output to JSON format to reduce overhead

Question 5

Which TWO AWS services can be used to transform data in an Amazon S3 data lake before loading into Amazon Redshift? (Choose 2.)

Accepted Answer

AWS Glue. AWS Glue can run ETL jobs to transform S3 data and load into Redshift. Amazon Redshift Spectrum can query S3 data directly and load results into Redshift tables. Amazon Athena is for querying, not loading. Amazon EMR requires cluster management. AWS Lambda is for small data volumes.

Answer

AWS Lambda

Answer

Amazon Athena

Answer

Amazon EMR

Question 6

A data engineer is designing a pipeline to ingest change data capture (CDC) events from an Amazon RDS for PostgreSQL database into Amazon S3. The CDC events are captured using AWS DMS. The data must be available for querying within 5 minutes of the change. Which approach meets these requirements?

Accepted Answer

Use AWS DMS to replicate data directly to S3 in near real-time.. Option A is correct because DMS can stream CDC data to S3 in near real-time, meeting the 5-minute SLA. Option B is wrong because a Lambda function triggered by S3 events is not needed for the initial ingestion. Option C is wrong because exporting to S3 and then using Glue adds latency. Option D is wrong because Redshift is a separate service and adds complexity.

Answer

Export the database to S3 using pg_dump and then use AWS Glue to load into S3 in Parquet format.

Answer

Use AWS DMS to replicate data to Amazon Redshift, then unload to S3.

Answer

Use AWS DMS to replicate data to an SQS queue, then process with Lambda to write to S3.

Question 7

A company needs to ingest data from an on-premises database to Amazon S3 with minimal impact on the source database. The data volume is several TB. Which AWS service is best suited for this task?

Accepted Answer

AWS Database Migration Service (DMS). AWS DMS can perform a full load with minimal impact on source. Option B (S3 Transfer Acceleration) is for speeding up uploads; Option C (Direct Connect) is a network connection; Option D (Snowball) is for offline transfer.

Answer

AWS Direct Connect

Answer

AWS Snowball Edge

Answer

Amazon S3 Transfer Acceleration

Question 8

Which TWO AWS services can be used as sources for AWS Glue ETL jobs? (Choose two.)

Accepted Answer

Amazon S3. Glue can read from S3 and JDBC sources like RDS and Redshift. Option C (API Gateway) is not a data store; Option D (CloudFront) is a CDN; Option E (Route 53) is DNS.

Answer

Amazon Route 53

Answer

Amazon CloudFront

Answer

Amazon API Gateway

Question 9

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data is transformed using an AWS Lambda function. Recently, the transformation errors have increased due to Lambda timeouts. The data engineer needs to diagnose and resolve the issue without losing data. What should the engineer do?

Accepted Answer

Increase the Lambda function timeout and ensure that failed records are sent to a backup S3 bucket. Option A is correct because increasing the Lambda timeout gives the function more time to complete, and Kinesis Firehose retries failures; data is not lost if the Lambda fails because Firehose can retry or send failed records to a backup S3 bucket. Option B is wrong because Lambda never stores data. Option C is wrong because CloudWatch Logs captures logs but does not store failed records. Option D is wrong because Lambda cannot write to SQS directly from Firehose transformation; Firehose handles failed records internally.

Answer

Enable Amazon CloudWatch Logs for the Lambda function to capture errors and store failed records in CloudWatch

Answer

Configure the Lambda function to write failed records to an Amazon SQS queue for later reprocessing

Answer

Modify the Lambda function to store failed records in Amazon S3 before processing

Question 10

Refer to the exhibit. An IAM policy is attached to an AWS Glue ETL job. The job reads from the Kinesis stream 'input-stream' and writes to S3 bucket 'data-lake-bucket'. The job fails with an access denied error. Which missing permission is most likely the cause?

Accepted Answer

s3:ListBucket permission on the bucket. Option D is correct. The policy includes s3:PutObject but not s3:ListBucket, which is required for Glue to write to S3. Option A is wrong because the policy does not restrict to a prefix. Option B is wrong because kinesis:DescribeStream is not required for writing. Option C is wrong because the stream ARN is specific.

Answer

kinesis:PutRecord permission on a wildcard stream ARN

Answer

kinesis:DescribeStream permission

Answer

s3:PutObject permission on a specific prefix

Question 11

A company has a 100 TB dataset stored on-premises in a Hadoop cluster. They want to ingest this data into Amazon S3 for processing with AWS Glue. The company has a limited time window and a slow internet connection. Which strategy is MOST appropriate?

Accepted Answer

Use AWS Snowball Edge to physically ship the data to AWS.. Option A is correct because AWS Snowball Edge is designed for large data transfers with limited network. Option B is wrong because Direct Connect may not be feasible if the connection is slow. Option C is wrong because DataSync is for smaller volumes and needs network. Option D is wrong because S3 Transfer Acceleration also requires a network connection.

Answer

Use AWS DataSync over the existing internet connection.

Answer

Use Amazon S3 Transfer Acceleration to speed up the upload.

Answer

Use AWS Direct Connect to establish a high-bandwidth connection.

Question 12

A company needs to ingest data from an on-premises Oracle database into Amazon Redshift for analytics. The data volume is 500 GB and the network bandwidth is limited. Which AWS service should be used for the initial one-time data migration?

Accepted Answer

AWS Snowball. AWS Snowball is ideal for large data transfers over limited network bandwidth. It allows physical shipping of storage devices. AWS DMS can be used for ongoing replication, but for initial large volume with limited bandwidth, Snowball is more efficient. S3 Transfer Acceleration speeds up transfers, but still relies on network. Direct Connect improves network but may still be insufficient for 500 GB.

Answer

AWS Direct Connect

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Database Migration Service (DMS)

Question 13

The exhibit shows an AWS CLI command and its output. A data engineer wants to copy only objects larger than 10 MB from the S3 bucket to another bucket for processing. Which approach should be used to automate this task?

Accepted Answer

Use AWS CLI with a script to filter and copy objects. The command lists objects larger than 10 MB. To automate copying, a script using AWS CLI with the --query parameter can filter and copy. Using S3 Batch Operations allows performing actions on a list of objects. The correct approach is to use AWS CLI with a script that iterates over the filtered list and uses aws s3 cp. S3 replication is for continuous sync, not one-time copy. Lambda with S3 events triggers only on new objects, not existing ones. S3 Inventory provides metadata but not direct copy.

Answer

Use S3 replication rules to replicate objects above 10 MB

Answer

Use S3 Inventory to generate a list and then copy

Answer

Use AWS Lambda with S3 event notifications

Question 14

A company uses Amazon Kinesis Data Analytics for real-time anomaly detection on clickstream data. The application uses a sliding window of 1 minute. The data engineer notices that the application is producing incorrect results because late-arriving records are not being handled properly. What should the data engineer do to ensure late records are included in the window calculations?

Accepted Answer

Increase the watermark delay in the Kinesis Data Analytics application to allow more time for late records.. Option B is correct because Kinesis Data Analytics allows configuring a higher watermark delay, which tells the application to wait longer for late-arriving records. Option A is wrong because increasing the window size changes the semantics. Option C is wrong because modifying the stream retention does not affect the application's window. Option D is wrong because ordering is not the issue; it's about waiting for late data.

Answer

Use a Kinesis Data Firehose to buffer the data and then send to Kinesis Data Analytics.

Answer

Increase the window size from 1 minute to 2 minutes.

Answer

Increase the retention period of the Kinesis stream to 7 days.

Question 15

A company is ingesting streaming data from social media feeds using Amazon Kinesis Data Streams. The data volume peaks at 10,000 records per second, and each record is up to 1 KB. The company needs to archive the raw data in Amazon S3 in near real-time and also make it available for real-time analytics using Amazon Kinesis Data Analytics. What is the MOST efficient architecture to meet these requirements?

Accepted Answer

Use Kinesis Data Streams as the ingestion point. Use Kinesis Data Firehose to read from the stream and write to S3. Use Kinesis Data Analytics to read directly from the same stream.. Option D is correct because Kinesis Data Firehose can read from a Kinesis Data Stream and write to S3, while Kinesis Data Analytics can read from the same stream for real-time analytics. Option A is incorrect because Lambda adds cost and complexity. Option B is incorrect because two separate streams are wasteful. Option C is incorrect because Firehose cannot convert to Parquet without a schema; also Lambda is avoidable.

Answer

Use Kinesis Data Streams as the ingestion point. Use Kinesis Data Firehose to read from the stream, convert to Parquet, and write to S3. Use a Lambda function to send data to Kinesis Data Analytics.

Answer

Use Kinesis Data Streams as the ingestion point. Use a Lambda function to read from the stream, write to S3, and send data to Kinesis Data Analytics.

Answer

Use two Kinesis Data Streams: one for S3 delivery and one for Kinesis Data Analytics.