DEA-C01 Practice Test 32 — 15 Questions

Question 1

Refer to the exhibit. A data engineer has configured an S3 event notification to send an event to an SQS queue when objects are created in the 'incoming/' prefix. The engineer wants to trigger an AWS Lambda function to process the object. However, the Lambda function is not being invoked. What is the most likely cause?

Accepted Answer

Lambda is not configured as an event source for the SQS queue.. Option B is correct. The event notification sends to SQS, not directly to Lambda. To invoke Lambda, the queue must be configured as an event source for Lambda. Option A is wrong because the prefix is correct. Option C is wrong because permissions are needed but not the primary cause; the Lambda function is not triggered by SQS events unless Lambda polls the queue. Option D is wrong because the queue exists and is configured.

Answer

The Lambda function lacks permission to read from the S3 bucket.

Answer

The SQS queue does not exist or is in a different account.

Answer

The S3 event notification filter prefix is incorrect.

Question 2

A data engineer is building a real-time data pipeline using Amazon Kinesis Data Streams with a Lambda consumer. The data volume is 2 MB/s with average record size of 5 KB. The Lambda function processes records and writes to DynamoDB. Occasionally, the Lambda function fails with 'ProvisionedThroughputExceededException' on DynamoDB. What is the best way to handle this?

Accepted Answer

Configure a Lambda destination on failure to send records to an SQS dead-letter queue, and implement retry logic in Lambda.. Implementing a dead-letter queue and retry logic with exponential backoff is best practice. Option A is correct. Option B is wrong because increasing Lambda concurrency may worsen throttling. Option C is wrong because KCL adds complexity. Option D is wrong because DynamoDB Streams is for change data capture, not for throttling errors.

Answer

Replace Lambda with the Kinesis Client Library (KCL) running on EC2.

Answer

Increase the Lambda function's reserved concurrency to process more records in parallel.

Answer

Use DynamoDB Streams to capture the records and process them asynchronously.

Question 3

A data engineer needs to ingest data from an external partner's FTP server to Amazon S3. The data arrives once daily as a CSV file. Which AWS service should be used for this ingestion?

Accepted Answer

AWS Transfer Family. Option A is correct because AWS Transfer Family supports SFTP and FTP-based transfers into S3. Option B is wrong because AWS DataSync is for transfers between storage systems, but it requires agent installation. Option C is wrong because Amazon AppFlow is for SaaS applications, not FTP. Option D is wrong because Amazon Kinesis Firehose is for streaming data.

Answer

AWS DataSync

Answer

Amazon Kinesis Data Firehose

Answer

Amazon AppFlow

Question 4

A company runs a SQL Server transactional database on Amazon RDS. They need to capture change data (inserts, updates, deletes) in near real-time and replicate them to an Amazon S3 data lake. Which AWS service is most suitable?

Accepted Answer

AWS Database Migration Service (DMS) with change data capture. AWS DMS with CDC continuously captures changes from the source and writes to S3 in Parquet/JSON. Kinesis, Glue, and Redshift are not designed for CDC from RDS to S3 directly.

Answer

AWS Glue DataBrew

Answer

Amazon Kinesis Data Streams with Kinesis Client Library

Answer

Amazon Redshift Spectrum

Question 5

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 Lambda concurrency, increasing memory (which also increases CPU), and using a reserved concurrency ensure capacity. Changing runtime has minor effect; SQS is not used in this path.

Answer

Route the CloudWatch Logs subscription to an Amazon SQS queue first

Answer

Change the Lambda function runtime from Python to Node.js

Question 6

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error: 'An error occurred while calling o137.pyWriteDynamicFrame. No such file or directory: s3://bucket/output/part-00000.parquet'. The job reads from a JDBC source and writes to S3. What is the most likely cause?

Accepted Answer

The output S3 path does not exist and the Glue job does not have permission to create it. Option A is correct because the error indicates the output path does not exist; the Glue job may not have permission to create the directory if the bucket or prefix does not exist. Option B (IAM role) would cause a permissions error, not 'No such file or directory'. Option C (schema mismatch) would cause a different error. Option D (memory) would cause out-of-memory or timeout errors.

Answer

The schema of the source data has changed, causing a mismatch during write

Answer

The Glue job ran out of memory during the transformation phase

Answer

The IAM role attached to the Glue job lacks permissions to read from the JDBC source

Question 7

Refer to the exhibit. A data engineer runs an AWS Glue job that fails with an 'Access Denied' error when writing to S3. The IAM role attached to the job has s3:PutObject permission on the output bucket. What additional configuration is most likely missing?

Accepted Answer

The S3 bucket policy does not grant access to the Glue job's IAM role. Option C is correct because the error shows 'Access Denied' when writing, and the job uses Glue version 3.0 (which includes Spark 3.1), which may require the S3 bucket to have a bucket policy that allows the Glue service principal or a VPC endpoint. Often the issue is that the S3 bucket policy does not allow the Glue job's IAM role. Option A (KMS) is possible but not mentioned. Option B (VPC) might be needed if the job is in a VPC. Option D (CloudWatch) is not related.

Answer

The Glue job is not configured to write to S3 with the correct prefix

Answer

The Glue job is running in a VPC without an S3 VPC endpoint

Answer

The S3 bucket is encrypted with AWS KMS and the IAM role lacks kms:Decrypt permission

Question 8

Which TWO options are valid methods to ingest on-premises relational database data into Amazon S3 for analytics? (Choose 2.)

Accepted Answer

AWS Glue ETL job with JDBC connection to source. AWS DMS can continuously replicate data to S3. AWS Glue ETL can connect to JDBC sources and write to S3. Both are valid ingestion methods.

Answer

AWS Snowball Edge

Answer

Amazon Kinesis Data Streams with Direct Put

Answer

Amazon AppFlow

Question 9

A data engineer is troubleshooting a Lambda function that reads from the Kinesis stream 'my-data-stream'. The Lambda function is able to read data but occasionally fails with 'KMS.AccessDeniedException'. What is the most likely cause?

Accepted Answer

The Lambda function's execution role does not have kms:Decrypt permission for the KMS key.. The stream uses KMS encryption. The Lambda function's IAM role likely lacks permission to decrypt using the KMS key. Option A is correct. Option B is wrong because the stream has 1 shard, which is fine. Option C is wrong because Lambda can read from streams. Option D is wrong because retention period is 24 hours, which is fine.

Answer

The retention period is too short; increase it.

Answer

The stream has too few shards; increase shard count.

Answer

The Lambda function is not authorized to consume from Kinesis streams.

Question 10

A company uses Amazon Kinesis Data Streams with enhanced fan-out consumers. The stream has 5 shards. Each consumer reads from all shards. The total incoming data rate is 25 MB/s. What is the maximum read throughput per consumer if enhanced fan-out is enabled?

Accepted Answer

10 MB/s. Option A is correct because enhanced fan-out provides each consumer with dedicated 2 MB/s per shard read throughput. With 5 shards, each consumer can read up to 10 MB/s. Option B is wrong because that is the total write capacity. Option C is wrong because that is the read capacity without enhanced fan-out. Option D is wrong because that is the per-shard write capacity.

Answer

2 MB/s

Answer

1 MB/s

Answer

5 MB/s

Question 11

A data engineer needs to ingest data from an Amazon S3 bucket into an Amazon Redshift table on a daily schedule. The data is in CSV format and the schema matches. Which service is simplest for this batch ingestion?

Accepted Answer

Amazon Redshift COPY command. Redshift COPY command loads from S3 efficiently. Glue and Data Pipeline also work but are more complex. Athena is for querying, not loading.

Answer

AWS Glue ETL job with JDBC connection

Answer

AWS Data Pipeline

Answer

Amazon Athena CREATE TABLE AS SELECT

Question 12

A data engineer is tasked with transforming JSON data from an S3 bucket into Parquet format for efficient querying. The transformation should run on a schedule every hour. Which AWS service is best suited for this task?

Accepted Answer

AWS Glue. Option C (AWS Glue) is correct because it offers scheduled ETL jobs that can read from S3, transform data, and write back in Parquet format. Option A (Amazon Athena) is a query service, not for scheduled transformations. Option B (Amazon EMR) is more complex and typically used for large-scale processing. Option D (AWS Lambda) has a 15-minute timeout and is not ideal for hourly batch jobs.

Answer

AWS Lambda

Answer

Amazon Athena

Answer

Amazon EMR

Question 13

A company is using AWS Glue to run ETL jobs that transform data from S3 to Redshift. The jobs are failing intermittently with out-of-memory errors. Which THREE actions can help resolve this issue? (Choose THREE.)

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Options A, B, and D are correct. A: Increasing the number of DPUs provides more memory. B: Using a larger worker type (e.g., G.1X or G.2X) increases memory per worker. D: Optimizing the transformation logic to reduce memory usage helps. C: Using Spark's 'coalesce' reduces partitions but may not solve memory issues. E: Using S3 Select pushes down filtering but does not address memory.

Answer

Use S3 Select to filter data before reading into the Glue job

Answer

Use Spark's 'coalesce' function to reduce the number of partitions

Question 14

A data engineer is building a pipeline to ingest JSON files from Amazon S3 into Amazon Redshift. The files are 100 MB each and arrive every 5 minutes. Which service is BEST suited for this ingestion?

Accepted Answer

Amazon Redshift COPY command. Amazon Redshift COPY command efficiently loads large files from S3 in parallel. It is the most direct and performant approach for bulk loading into Redshift.

Answer

AWS Glue ETL job

Answer

AWS Lambda with Redshift Data API

Answer

Amazon Kinesis Data Firehose with Redshift destination

Question 15

A company uses AWS Glue to transform data stored in Amazon S3. During a run, the job fails with a 'OutOfMemoryError' in the Spark executor. The job processes 2 TB of parquet files using 10 DPUs. The data is evenly distributed across partitions. Which action would MOST likely resolve the issue without impacting the job logic?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Option A is correct because increasing DPUs provides more memory and cores. Option B is wrong because repartitioning may not reduce memory per executor if total DPUs unchanged. Option C is wrong because compressing reduces input size, but Spark uses memory for processing, not just storage. Option D is wrong because increasing S3 request rate does not help with executor memory.

Answer

Enable S3 request rate increase to speed up data reading.

Answer

Repartition the data to a larger number of partitions.

Answer

Change the input format from Parquet to Snappy-compressed CSV.