DEA-C01 Practice Test 28 — 15 Questions

Question 1

A data engineer is setting up an Amazon Kinesis Data Firehose delivery stream to load data into Amazon Redshift. The data is coming from an application that produces JSON records. The engineer needs to transform the data to match the Redshift table schema. Which approach is the MOST cost-effective and requires the least operational overhead?

Accepted Answer

Configure a Lambda function in the Firehose delivery stream to transform records before delivery.. Using Firehose's built-in Lambda transformation function is the simplest and most cost-effective approach. Option A is wrong because Firehose cannot directly write to Redshift without transformation. Option B is wrong because Glue adds extra cost and complexity. Option D is wrong because KCL requires custom code and management.

Answer

Use AWS Glue as a transformation step between Firehose and Redshift, with a trigger on S3.

Answer

Use Kinesis Data Firehose with direct PUT to Redshift and rely on Redshift's COPY command to transform.

Answer

Use the Kinesis Client Library (KCL) to consume the stream, transform in an EC2 instance, and then load to Redshift.

Question 2

A data engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that transforms and loads it into Amazon S3. Recently, the team noticed that the Lambda function is failing with throttling errors (HTTP 429) from the Kinesis API. Which configuration change should the team make to resolve this issue?

Accepted Answer

Reduce the batch size and increase the number of shards in the Kinesis stream to increase parallelism.. The correct answer is to reduce the batch size and increase the number of shards. The Lambda function is experiencing throttling because it is trying to process too many records per invocation. Reducing the batch size lowers the number of records per invocation, and increasing shards increases parallelism. Option A is incorrect because increasing the batch size would worsen throttling. Option B is incorrect because using a DynamoDB stream is a different ingestion mechanism and doesn't address the Kinesis throttling. Option D is incorrect because disabling retries would cause data loss. Option C directly addresses the throttling by reducing load per invocation and increasing parallelism.

Answer

Disable retries on the Lambda function and configure a dead-letter queue for failed records.

Answer

Replace Kinesis Data Streams with Amazon DynamoDB Streams for ingestion.

Answer

Increase the batch size in the Lambda event source mapping to reduce the number of invocations.

Question 3

A data engineering team is building a data lake on Amazon S3. They need to ingest data from multiple sources: (1) streaming IoT data, (2) daily CSV exports from an on-premises system via SFTP, and (3) change data capture (CDC) from an Amazon Aurora database. Which THREE services should the team use to ingest these data sources?

Accepted Answer

Amazon Kinesis Data Streams for IoT data ingestion.. The correct answers are A, B, and D. Kinesis Data Streams for streaming IoT, Transfer Family for SFTP and CDC from Aurora via DMS. Option C (Glue ETL) is for transformation, not ingestion. Option E (EMR) is for big data processing, not ingestion.

Answer

AWS Glue ETL for CDC from Aurora.

Answer

Amazon EMR for daily CSV ingestion.

Question 4

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS for MySQL and load it into Amazon S3. The job runs daily and processes incremental changes using the JDBC connection. Recently, the job has been failing with a 'Communications link failure' error. The RDS instance is in a private subnet. Which step should the engineer take first to diagnose the issue?

Accepted Answer

Check the security group and network ACL rules for the RDS instance and the Glue connection.. Option B is correct because network connectivity between Glue and RDS is likely the issue, and checking security groups and subnets is the first step. Option A is wrong because the error is not about authentication. Option C is wrong because S3 permissions are not related to the link failure. Option D is wrong because the job type (Spark vs Python) does not affect connectivity.

Answer

Verify that the IAM role used by Glue has the correct permissions to access RDS.

Answer

Change the Glue job type from Spark to Python shell.

Answer

Check that the JDBC driver is compatible with the Glue version.

Question 5

A company needs to ingest CSV files from an FTP server into Amazon S3 daily. The files are typically 50 MB each, and the process should be fully managed with minimal operational overhead. Which AWS service should be used?

Accepted Answer

AWS Transfer Family. AWS Transfer Family provides fully managed SFTP/FTP to S3 ingestion. Option A is correct. Option B is wrong because DataSync is for server-to-server transfers, not FTP. Option C is wrong because AppFlow is for SaaS applications. Option D is wrong because Lambda is not a managed ingestion service.

Answer

AWS Lambda with FTP library

Answer

AWS DataSync

Answer

Amazon AppFlow

Question 6

A data engineering team needs to load data from an on-premises Oracle database to Amazon S3 daily. The data volume is about 50 GB per day, and the network bandwidth is 100 Mbps. The team wants to minimize operational overhead and use AWS managed services. Which solution should they choose?

Accepted Answer

Use AWS Database Migration Service (DMS) to migrate the data to S3.. Option C is correct because AWS Database Migration Service (DMS) can continuously replicate or schedule data migration from on-premises databases to S3 with minimal setup. Option A is wrong because AWS DataSync is for file transfers, not database tables. Option B is wrong because AWS Glue can connect to JDBC sources but requires more configuration for scheduled loads. Option D is wrong because Amazon Kinesis Firehose is for streaming data, not for batch database loads.

Answer

Use AWS DataSync to copy the database files directly to S3.

Answer

Use AWS Glue with a JDBC connection and schedule a crawler to load data into S3.

Answer

Use Amazon Kinesis Data Firehose to stream data from Oracle to S3.

Question 7

A company is building a data lake on Amazon S3 and wants to ingest data from multiple AWS services (CloudTrail, VPC Flow Logs, and ALB logs). The data should be stored in a central S3 bucket with a common partitioning scheme. Which service can be used to collect and centralize this data with minimal configuration?

Accepted Answer

Configure each source service to deliver logs directly to the central S3 bucket.. Amazon S3 can be configured as a destination for CloudTrail, VPC Flow Logs, and ALB logs directly, but centralizing requires custom logic. AWS Glue can crawl and catalog, but not ingest. AWS Data Pipeline can copy data but requires setup. However, the best answer is to use S3 replication or a simple Lambda function. But among the options, the most suitable is to use AWS Glue with a custom script? Actually, the question expects S3 cross-region replication? Let's see. The correct answer is to use S3 as a central bucket and configure each service to deliver logs to that bucket. But the options may include 'Use Amazon S3 event notifications to trigger a Lambda function that copies logs to a central bucket.' That is plausible. However, the simplest is to configure each service to deliver to the same bucket. But the question likely expects using S3 replication? I'll go with: Configure each service to deliver logs to a common S3 bucket prefix.

Answer

Use AWS Data Pipeline to copy logs from each source S3 bucket to the central bucket.

Answer

Use AWS Glue to crawl the logs from each source and write to a central S3 bucket.

Answer

Set up Amazon Kinesis Data Firehose to ingest logs from each service and write to S3.

Question 8

A company is using Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an Amazon S3 bucket. The team notices that the application is experiencing high latency during peak hours. The stream has 8 shards, and the application is configured with a parallelism of 4. Which action would most likely reduce the latency?

Accepted Answer

Increase the parallelism of the Flink application to 8.. The correct answer is to increase the parallelism of the Flink application to match the number of shards (8). When parallelism is lower than the number of shards, some shards are underutilized, causing backpressure and latency. Option A (increasing checkpointing interval) would reduce overhead but not address the parallelism mismatch. Option C (using a larger instance type) could help but is less effective than matching parallelism. Option D (decreasing batch size) is not applicable to Flink. Option B directly fixes the bottleneck.

Answer

Decrease the batch size in the S3 sink.

Answer

Use a larger Kinesis Data Analytics application instance type.

Answer

Increase the checkpointing interval to reduce overhead.

Question 9

A company uses AWS Glue ETL to process data from Amazon S3 and write results to Amazon Redshift. The job fails with a memory error when processing large files. Which action should the data engineer take 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 for processing. Option A is wrong because reducing parallelism may increase memory per worker but typically reduces overall throughput. Option B is wrong because S3 Select filters data server-side but does not reduce memory usage in Glue. Option D is wrong because using a smaller instance type would exacerbate the memory issue.

Answer

Reduce the number of partitions in the Glue job.

Answer

Switch to a smaller instance type in the Glue job configuration.

Answer

Use S3 Select to filter columns before reading into Glue.

Question 10

A data engineer is troubleshooting a slow-running AWS Glue ETL job that reads from Amazon S3 and writes to Amazon Redshift. The job processes 500 GB of CSV data daily. The engineer wants to improve performance. Which THREE actions should the engineer take? (Choose three.)

Accepted Answer

Use a JDBC connection with a higher batch size for writing to Redshift.. Options A, C, and D are correct. Increasing the number of DPUs provides more parallelism. Partitioning the input data improves read performance. Using a JDBC connection with appropriate batch size improves write performance. Option B is wrong because reducing the number of files may reduce parallelism. Option E is wrong because switching to a single node cluster reduces parallelism.

Answer

Switch to a single-node Redshift cluster to reduce latency.

Answer

Reduce the number of input files by combining them into larger files.

Question 11

A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The source files are in a bucket with thousands of small files. What is the best practice to optimize the Glue job performance?

Accepted Answer

Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.. Grouping small files into fewer partitions reduces overhead. Option A is correct. Option B is wrong because increasing DPUs may not help with small files. Option C is wrong because converting to CSV adds extra step. Option D is wrong because Parquet is more efficient for Glue.

Answer

Convert the JSON files to CSV before processing with Glue.

Answer

Use an AWS Lambda function to pre-process the files.

Answer

Increase the number of DPUs to the maximum.

Question 12

Which TWO AWS services can be used to ingest data from an on-premise relational database into Amazon S3 on a one-time basis?

Accepted Answer

AWS Database Migration Service (DMS). Options A and C are correct. AWS DMS can perform one-time full load from databases to S3. AWS Glue can connect to JDBC sources and write to S3. Option B is wrong because Kinesis Data Streams is for streaming. Option D is wrong because SQS is a queue. Option E is wrong because Data Pipeline is a legacy service.

Answer

AWS Data Pipeline

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams

Question 13

A company is running a critical application that generates millions of small JSON files every hour in an S3 bucket. A data engineer needs to process these files in near real-time using AWS Glue. The engineer wants to minimize the latency between file arrival and Glue job start. Which TWO actions should the engineer take?

Accepted Answer

Pre-process the files to consolidate them into larger files before the Glue job runs.. Using S3 event notifications to trigger a Lambda that starts the Glue job reduces latency compared to scheduled jobs. Also, grouping small files into larger ones before Glue processing reduces the overhead. Option A is wrong because increasing DPUs does not reduce start latency. Option C is wrong because batch window increases latency.

Answer

Increase the Glue job's batch window to 600 seconds.

Answer

Increase the number of DPUs for the Glue job to accelerate processing.

Question 14

A team is designing a data ingestion pipeline to load JSON files from an Amazon S3 bucket into Amazon Redshift. The files arrive every 5 minutes, and each file is between 10 MB and 50 MB. The team wants to minimize the time between file arrival and data availability in Redshift. Which approach should the team use?

Accepted Answer

Use S3 Event Notifications to trigger an AWS Lambda function that runs the COPY command to load data into Redshift.. The correct answer is to use S3 Event Notifications to invoke a Lambda function that calls the Redshift COPY command. This provides near-real-time ingestion with minimal latency. Option B (Redshift Spectrum) queries data in S3 without loading, but data is not in Redshift tables for fast querying. Option C (Kinesis Firehose) can load into Redshift but adds streaming overhead and is not optimized for batch files. Option D (AWS DMS) is for database migration, not file loading.

Answer

Schedule an AWS Glue job to run every 5 minutes to load the data.

Answer

Use Amazon Redshift Spectrum to query the data directly from S3 without loading.

Answer

Configure Amazon Kinesis Data Firehose to stream data from S3 to Redshift.

Question 15

A data engineer is designing a real-time analytics pipeline for clickstream data. The source is Amazon Kinesis Data Streams, and the data must be stored in Amazon S3 in partitioned Parquet format with near-real-time latency. The engineer must also handle late-arriving data (up to 1 hour). Which combination of services meets these requirements?

Accepted Answer

Use Kinesis Data Firehose with a Lambda transformation to write to S3, and a separate Lambda consumer to reprocess late data from the stream.. Option D is correct because Kinesis Data Firehose can buffer data and deliver to S3 with custom partitioning, and a separate Lambda function can reprocess late data from the stream. Option A is wrong because Kinesis Data Analytics does not partition data by time. Option B is wrong because S3 Batch Operations are for batch, not streaming. Option C is wrong because Glue streaming ETL lacks built-in support for late data handling with Firehose partitioning.

Answer

Use AWS Glue streaming ETL to write to S3 in real-time.

Answer

Use Kinesis Data Analytics with a tumbling window to write to S3.

Answer

Use Kinesis Data Firehose to deliver to S3, and use S3 Batch Operations to process late data.