DEA-C01 Practice Test 39 — 15 Questions

Question 1

A data engineering team needs to transform CSV files to Parquet format after they land in an S3 bucket. The transformation should be triggered automatically as soon as a new file arrives. Which AWS service is best suited for this task?

Accepted Answer

AWS Lambda function triggered by S3 event. Option D is correct because AWS Lambda can be triggered by S3 events and convert files to Parquet using libraries like PyArrow. Option A (AWS Glue) is better for scheduled jobs, not event-driven. Option B (Amazon EMR) is overkill. Option C (AWS Batch) is for compute jobs but adds latency.

Answer

AWS Batch job submitted by S3 event

Answer

Amazon EMR cluster running continuously

Answer

AWS Glue ETL job scheduled every 5 minutes

Question 2

A company is designing a data lake on Amazon S3. The data ingestion pipeline must handle both structured and unstructured data. The data must be cataloged for easy discovery. Which THREE services should be included in the solution? (Choose THREE.)

Accepted Answer

Amazon S3. Option A is correct because S3 is the storage layer. Option B is correct because Glue Crawlers can catalog data. Option C is correct because Athena can query the cataloged data. Option D (RDS) is a relational database, not for a data lake. Option E (Redshift) is a data warehouse, not a data lake.

Answer

Amazon RDS

Answer

Amazon Redshift

Question 3

A company is ingesting data from multiple sources into S3 using AWS Glue. The data engineer notices that the Glue job is failing with an OutOfMemory error. Which step should be taken to resolve this issue?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Option C is correct because increasing the number of DPUs provides more memory. Option A is wrong because reducing data volume is not a solution. Option B is wrong because using a smaller file size may help but not directly address memory. Option D is wrong because Glue does not support memory configuration directly.

Answer

Reduce the volume of incoming data

Answer

Configure the job to use a larger memory setting

Answer

Use a smaller file size for input

Question 4

A data engineer needs to transform CSV files to Parquet format using AWS Glue. The source data contains sensitive columns that must be masked. Which Glue feature should be used?

Accepted Answer

AWS Glue DataBrew. Option B is correct because Glue DataBrew provides built-in transformations like masking. Option A is wrong because Glue Studio is for building visual ETL jobs but not specifically for masking. Option C is wrong because Glue crawlers catalog data, not transform. Option D is wrong because Glue Schema Registry manages schemas.

Answer

AWS Glue Studio

Answer

AWS Glue Crawler

Answer

AWS Glue Schema Registry

Question 5

A company uses AWS Glue to run ETL jobs daily. The data is stored in S3 as Parquet files partitioned by date. Recently, jobs have failed with the error 'No such file or directory' for certain partitions. What is the MOST likely cause?

Accepted Answer

A partition folder was deleted or not created by the upstream process.. Option B is correct because if a partition folder is missing, Glue may fail looking for it. Option A is wrong because a lack of permissions would produce an access denied error. Option C is wrong because schema evolution typically causes type mismatches, not missing files. Option D is wrong because compression issues cause read errors, not missing files.

Answer

The schema has changed and Glue cannot parse the data.

Answer

The files are compressed with an unsupported codec.

Answer

The IAM role does not have s3:GetObject permission.

Question 6

A company is building a data lake on Amazon S3. They need to ingest data from multiple sources, including relational databases, streaming data, and log files. Which THREE AWS services can be used to ingest data into the data lake?

Accepted Answer

Amazon Kinesis Data Firehose. Options A, B, and D are correct. Kinesis Firehose for streaming, Glue for batch ETL, and DMS for database migration. Option C is wrong because Athena is a query service, not ingestion. Option E is wrong because Redshift Spectrum is for querying S3 from Redshift.

Answer

Amazon Athena

Answer

Amazon Redshift Spectrum

Question 7

A data engineer is designing a streaming pipeline using Amazon Kinesis Data Streams with a shard count of 10. The incoming data rate is 1 MB/second. The consuming application uses the Kinesis Client Library (KCL) with a single worker. What is the most likely performance bottleneck?

Accepted Answer

The single KCL worker cannot process all shards in parallel. Option A is correct because a single KCL worker processes all shards sequentially, limiting throughput. Option B is wrong because the shard count is adequate for 1 MB/s (each shard can ingest 1 MB/s). Option C is wrong because provisioned throughput is not relevant. Option D is wrong because Lambda concurrency would apply if using Lambda, not KCL.

Answer

The Lambda function invoked by the stream has a cold start issue

Answer

The data stream has insufficient write capacity

Answer

The shard count is too low to handle the data rate

Question 8

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed into Parquet format and stored in Amazon S3. Which AWS service can perform the transformation in near real-time with minimal operational overhead?

Accepted Answer

Amazon Kinesis Data Firehose with a transformation Lambda function. Option A is correct because Kinesis Data Firehose can transform data using Lambda functions and deliver to S3 in Parquet format with no servers to manage. Option B (Kinesis Data Analytics) is for SQL queries, not direct S3 delivery. Option C (Glue ETL) is batch-oriented. Option D (EMR) requires cluster management.

Answer

Amazon EMR cluster running Spark Streaming

Answer

AWS Glue ETL job triggered by Kinesis stream

Answer

Amazon Kinesis Data Analytics for Apache Flink

Question 9

A company is building a data lake on S3. They have a large volume of CSV files (hundreds of GB) in a source bucket. They need to convert them to Parquet, partition by date, and ensure the data is encrypted at rest with SSE-KMS. The pipeline must be triggered automatically when new files arrive. Which THREE steps should be part of the solution? (Choose THREE.)

Accepted Answer

Configure S3 Event Notification to send events to an SQS queue. Option A (S3 Event Notification to SQS) can trigger the pipeline. Option C (Glue ETL with partition pruning) can convert to Parquet and partition. Option D (KMS key with s3:PutObject) ensures SSE-KMS. Option B (Athena CTAS) is not triggered by events. Option E (Kinesis) is for streaming.

Answer

Use Amazon Kinesis Data Firehose to ingest new files

Answer

Use Amazon Athena CTAS query to convert files in batch

Question 10

A company ingests IoT data into an S3 bucket using AWS IoT Core rules. The data is in JSON format, and each record is about 500 bytes. The data volume is 5 GB per day. The company wants to convert the data to Parquet format and partition it by year/month/day. Which TWO AWS services can be used together to achieve this with minimal operational overhead?

Accepted Answer

AWS Glue ETL job triggered by S3 event. Options B and E are correct. S3 Events can trigger Lambda, and Lambda can convert JSON to Parquet and write to partitioned paths. AWS Glue can also be triggered by S3 events via a workflow, but the combination of Lambda + Glue (option E) works as Glue can read from the landing zone and write partitioned Parquet. Option A (Kinesis Firehose) can convert to Parquet but cannot partition by year/month/day easily. Option C (EMR) requires cluster management. Option D (Athena) is for querying, not transformation.

Answer

Amazon Athena CTAS query

Answer

Amazon EMR with Spark job

Answer

Amazon Kinesis Data Firehose with Parquet conversion

Question 11

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that is experiencing high error rates when writing to an S3 bucket. The error logs indicate 'AccessDenied' errors. The S3 bucket policy allows access from the Firehose service, but the errors persist. What is the most likely cause?

Accepted Answer

The IAM role assumed by Firehose does not have the s3:PutObject permission. Option C is correct because Firehose uses a service-linked IAM role to write to S3; if the role lacks proper permissions, AccessDenied occurs even if the bucket policy allows it. Option A (encryption) would cause different errors. Option B (KMS key) is needed only if SSE-KMS is used. Option D (S3 event notifications) is unrelated.

Answer

The S3 bucket has a lifecycle policy that is deleting objects too quickly

Answer

The S3 bucket has default encryption enabled

Answer

The S3 bucket uses an AWS KMS key for encryption and Firehose does not have kms:Decrypt permission

Question 12

Refer to the exhibit. A data engineer is configuring an IAM policy for a Lambda function that writes transformed data to S3. The function writes to both 'example-bucket/data/' and 'example-bucket/public/'. The policy is intended to enforce server-side encryption with SSE-S3 for all objects written to the 'public/' prefix, while allowing all operations on other prefixes. However, the Lambda function is failing with an AccessDenied error when writing to 'example-bucket/public/'. What is the most likely cause?

Accepted Answer

The Lambda function is not setting the 'x-amz-server-side-encryption' header to 'AES256' when writing to 'public/'.. Option A is correct because the Deny statement uses a condition that denies PutObject if the encryption is not AES256. If the Lambda function does not set SSE-S3, the condition fails and the request is denied. Option B is wrong because the policy allows PutObject on all resources. Option C is wrong because DeleteObject is not denied. Option D is wrong because GetObject is allowed.

Answer

The policy denies DeleteObject on 'public/'.

Answer

The policy denies PutObject on 'public/' unconditionally.

Answer

The policy does not allow GetObject for 'public/'.

Question 13

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon RDS for PostgreSQL. The migration is ongoing with continuous replication. The data engineer notices that some changes are not being captured in the target database. What is the MOST likely cause?

Accepted Answer

The source Oracle database is not configured to retain archived redo logs for a sufficient period.. Option A is correct because DMS uses the source database's transaction logs (redo logs) for CDC; if they are not retained, DMS cannot read changes. Option B is wrong because VPC peering affects network connectivity, not CDC. Option C is wrong because DMS uses its own task logs, not CloudTrail. Option D is wrong because table mapping would cause missing tables, not missing changes.

Answer

The VPC peering connection between on-premises and AWS is down.

Answer

The DMS task's table mapping is incorrectly configured.

Answer

DMS is not publishing task logs to CloudWatch Logs.

Question 14

A company ingests clickstream data into Amazon S3 via Kinesis Data Firehose. The data arrives in 20 MB files every 2 minutes. The data engineering team needs to transform nested JSON into a flat structure before loading into Amazon Redshift. Which approach is most cost-effective and scalable?

Accepted Answer

Create an AWS Glue ETL job that runs on a schedule, using dynamic frames to flatten the data and write to S3 in Parquet. Option D is correct because the dynamic frame transform can flatten nested JSON in Glue ETL jobs, and incremental processing based on time partitions is efficient. Option A is wrong because Redshift Spectrum queries raw data but does not transform it. Option B is wrong because EMR is overkill for simple flattening. Option C is wrong because Lambda has a 15-minute timeout and may not handle large files.

Answer

Run an Amazon EMR cluster with Spark to flatten the data and write back to S3

Answer

Use AWS Lambda to transform each file as it arrives in S3

Answer

Use Amazon Redshift Spectrum to query the nested JSON directly and create a view

Question 15

Refer to the exhibit. A data engineer has attached this IAM policy to an AWS Glue job role. The Glue job fails when trying to write transformed data to an S3 bucket located in a different AWS account. What is the most likely reason?

Accepted Answer

The policy does not grant s3:ListBucket, and the bucket policy may not allow cross-account access. Option C is correct because the policy only allows s3:GetObject and s3:PutObject on the specified bucket ARN, but cross-account access typically requires additional permissions like s3:ListBucket and the bucket policy must also grant access. Option A is wrong because Kinesis permissions are present. Option B is wrong because Lambda invoke is allowed. Option D is wrong because the job role does have S3 permissions; the issue is cross-account.

Answer

The policy does not allow lambda:InvokeAsync

Answer

The Glue job role does not have permissions to write to S3

Answer

The policy does not include kinesis:DescribeStream