DEA-C01 Data Ingestion and Transformation Practice Test 24 — 15 Questions

Question 1

A data engineer needs to ingest data from an Amazon S3 bucket into Amazon Redshift for analytics. The data is in CSV format and the Redshift table already exists. Which service can be used to perform this ingestion with minimal configuration?

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

Accepted Answer

Amazon Redshift COPY command. The Amazon Redshift COPY command is the most direct and minimal-configuration method to load data from an S3 bucket into an existing Redshift table. It is purpose-built for bulk data ingestion from S3, supports CSV format natively, and requires only the table name, S3 path, IAM role, and format options — no additional services or pipelines needed.

Answer

AWS Glue

Answer

Amazon Kinesis Data Firehose

Answer

AWS Database Migration Service (DMS)

Question 2

A streaming application sends data to Amazon Kinesis Data Streams. The data must be enriched with reference data from an Amazon DynamoDB table in real-time. Which AWS service can be used to perform this enrichment with minimal latency?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Analytics for Apache Flink is correct because it allows you to run Apache Flink applications that can read from a Kinesis data stream, perform stateful stream processing, and enrich records in real-time by joining with reference data stored in DynamoDB. Flink's asynchronous I/O and managed state enable sub-second enrichment latency without the cold-start delays or concurrency limits of Lambda-based approaches.

Answer

Amazon Kinesis Data Firehose with Lambda transformation

Answer

AWS Lambda function triggered by Kinesis Data Streams

Answer

AWS Glue streaming ETL

Question 3

A data engineering team is designing a data ingestion pipeline for a social media analytics platform. The pipeline must handle up to 100,000 events per second with less than 1 second processing latency. Which TWO services should be used together to meet these requirements?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Streams (Option E) is designed for real-time data ingestion at scale, supporting up to 1,000 records per second per shard with sub-second latency, making it suitable for 100,000 events per second when provisioned with sufficient shards. Amazon Kinesis Data Analytics for Apache Flink (Option D) can consume data directly from Kinesis Data Streams and perform low-latency stream processing (e.g., aggregations, filtering) with exactly-once semantics, meeting the <1 second processing latency requirement. Together, they form a fully managed, scalable pipeline for high-throughput, low-latency streaming analytics.

Answer

AWS Glue streaming ETL

Answer

Amazon Kinesis Data Firehose

Answer

Amazon SQS

Question 4

A company is migrating its data warehouse from on-premises to Amazon Redshift. The migration involves copying 50 TB of data from an S3 bucket to Redshift. The network bandwidth is limited to 1 Gbps. Which TWO approaches should the team use to complete the transfer within 7 days?

Accepted Answer

Use Amazon S3 Transfer Acceleration. Amazon S3 Transfer Acceleration (option A) uses AWS edge locations to accelerate uploads over the public internet by routing traffic through optimized paths, which can significantly improve transfer speeds for large datasets when bandwidth is limited. With 1 Gbps bandwidth, the theoretical maximum transfer for 50 TB over 7 days is approximately 75.6 TB (1 Gbps * 7 days * 86400 seconds/day / 8 bits/byte), so the raw bandwidth is sufficient, but Transfer Acceleration helps overcome latency and packet loss issues that can reduce effective throughput. This makes it a valid approach to ensure the transfer completes within the time window.

Answer

Use AWS Direct Connect with 10 Gbps

Answer

Use AWS Lambda to copy data in parallel

Answer

Use Amazon Kinesis Data Firehose

Question 5

A data engineer needs to ingest JSON files from an Amazon S3 bucket into an Amazon DynamoDB table. The files are uploaded every hour. Which THREE services can be used together to build this ingestion pipeline?

Accepted Answer

Amazon SQS. Amazon SQS is correct because it decouples the ingestion pipeline, allowing S3 Event Notifications to send messages to an SQS queue when new JSON files arrive. AWS Lambda can then poll the SQS queue to process the files and write to DynamoDB, ensuring reliable, asynchronous ingestion without data loss.

Answer

AWS Step Functions

Answer

Amazon DynamoDB Streams

Question 6

Refer to the exhibit. A data engineer runs an AWS Glue ETL job that writes output to an S3 bucket. The job fails with the error shown. What is the most likely cause?

Accepted Answer

The IAM role used by the Glue job lacks the s3:PutObject permission for the output bucket. The error shown in the exhibit indicates an access denied or permission failure when the AWS Glue ETL job attempts to write its output to the S3 bucket. The most likely cause is that the IAM role assigned to the Glue job does not include the s3:PutObject permission for the target bucket, which is required to upload objects. Without this permission, the job cannot complete the write operation, resulting in the failure.

Answer

The Glue job attempted to write data in an unsupported format

Answer

The S3 bucket does not exist

Answer

The output file name contains invalid characters

Question 7

Refer to the exhibit. A data engineer is troubleshooting an AWS Lambda function that reads from an S3 bucket and writes to a Kinesis Data Stream. The Lambda function fails with an AccessDeniedException when calling the kinesis:PutRecords API. Which change is needed to the IAM policy?

Accepted Answer

Change the resource ARN for Kinesis to include the correct stream name. The Lambda function's IAM policy grants kinesis:PutRecords permission but the resource ARN does not match the target Kinesis Data Stream. The AccessDeniedException occurs because IAM evaluates the resource ARN against the stream's ARN and denies access on mismatch. The correct fix is to change the resource ARN to include the correct stream name (option C). While using a wildcard (option B) would also resolve the error, it is not the recommended approach because it violates the principle of least privilege. Option A is incorrect because the error is from Kinesis, not S3. Option D is incorrect because the kinesis:PutRecords action is already present in the policy.

Answer

Add s3:PutObject permission to the policy

Answer

Change the resource ARN for Kinesis to a wildcard

Answer

Add kinesis:PutRecords permission to the policy

Question 8

Refer to the exhibit. A data engineer is using a Kinesis Data Stream with 2 shards. The producer uses a partition key that is the user ID (a UUID). The consumer is falling behind. Which change would improve throughput?

Accepted Answer

Increase the number of shards. The consumer is falling behind because the total throughput of the stream (1 MB/s or 1,000 records/s per shard for writes, and 2 MB/s per shard for reads) is insufficient for the incoming data volume. Increasing the number of shards scales both the write and read capacity linearly, allowing the consumer to process records faster and catch up. Changing the partition key or retention period does not increase throughput, and switching to Firehose changes the delivery model but does not inherently solve the consumer lag.

Answer

Switch to Kinesis Data Firehose

Answer

Increase the retention period

Answer

Change the partition key to a constant value

Question 9

A company receives streaming clickstream data from its website. The data must be ingested with low latency and transformed in real time before being stored in Amazon S3. Which AWS service combination is most suitable for this use case?

Accepted Answer

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics. Amazon Kinesis Data Streams ingests streaming clickstream data with low latency, and Amazon Kinesis Data Analytics performs real-time transformations using SQL or Apache Flink. The processed data can then be stored in Amazon S3 via a Kinesis Data Firehose delivery stream, meeting the requirement for low-latency ingestion and real-time transformation.

Answer

Amazon S3 with S3 Object Lambda

Answer

Amazon Kinesis Data Firehose with AWS Lambda for transformation

Answer

AWS Glue jobs triggered by Amazon S3 events

Question 10

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. AWS DataSync is the most suitable option because it is designed to efficiently transfer large volumes of data from on-premises storage (including FTP servers) to AWS, handling millions of small files with minimal operational overhead. It automates data transfer, retries, and validation, and it is cost-effective as you pay only for the data transferred, with no need for additional infrastructure or complex scripting.

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 11

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. Increasing the Lambda function timeout directly addresses the root cause of transformation errors (timeouts), and configuring a backup S3 bucket for failed records ensures no data loss. Kinesis Data Firehose can be configured to send failed records to a separate S3 bucket as a dead-letter queue, which preserves the data for later reprocessing while the primary transformation pipeline is fixed.

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 12

A data engineer needs to ingest data from an Amazon S3 bucket into Amazon Redshift for analytics. The data is in CSV format and the Redshift table already exists. Which service can be used to perform this ingestion with minimal configuration?

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

Accepted Answer

AWS Glue with a scheduled ETL job. AWS Glue with a scheduled ETL job is the most appropriate choice because it provides a fully managed, serverless ETL service that can efficiently extract 500 GB of data daily from Amazon RDS for PostgreSQL and write it to Amazon S3. Glue can handle large volumes via its distributed Spark-based execution, and scheduling ensures the daily cadence without manual intervention.

Answer

AWS Database Migration Service (DMS) with continuous replication

Answer

Amazon Athena with federated query to RDS

Answer

Amazon EMR with Spark job

Question 13

A company uses AWS Glue to transform data in Amazon S3. The transformation logic is complex and involves multiple steps. The data engineer wants to implement a workflow that handles dependencies and retries on failure. Which AWS service should be used to orchestrate the Glue jobs?

Accepted Answer

AWS Step Functions. AWS Step Functions is the best choice for orchestrating Glue jobs with dependencies and retries.

Answer

AWS Lambda

Answer

Amazon Managed Workflows for Apache Airflow (MWAA)

Answer

Amazon CloudWatch Events

Question 14

A data engineer is building a streaming pipeline using Amazon Kinesis Data Streams and AWS Lambda. The Lambda function processes records and writes to Amazon DynamoDB. The engineer notices that the Lambda function is throttled during high traffic. Which action should the engineer take to reduce throttling?

Accepted Answer

Increase the number of shards in the Kinesis data stream. Increasing the number of shards in the Kinesis data stream increases the overall throughput of the stream, which allows the Lambda event source mapping to poll more shards concurrently. Each shard is processed by one Lambda invocation at a time, so more shards mean more concurrent Lambda executions, reducing the per-invocation load and the likelihood of throttling.

Answer

Increase the Lambda function timeout

Answer

Disable retries on the Lambda function

Answer

Use an Amazon SQS queue as an intermediate buffer

Question 15

A company needs to ingest data from multiple SaaS applications into Amazon S3. The data sources provide REST APIs. Which AWS service can be used to build a fully managed data ingestion pipeline without writing custom code?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is a fully managed service designed to transfer data from SaaS applications to AWS services like Amazon S3 without writing custom code. Option B is wrong because Amazon Kinesis Data Streams is primarily for real-time streaming data, not for directly ingesting from SaaS APIs. Option C is wrong because AWS Lambda requires custom code to integrate with SaaS APIs. Option D is wrong because AWS Glue with Python shell is for ETL transformations and also requires custom scripting.

Answer

Amazon Kinesis Data Streams

Answer

AWS Lambda with custom code

Answer

AWS Glue with Python shell