DEA-C01 Practice Test 50 — 15 Questions

Question 1

A company is streaming data from an application to Amazon Kinesis Data Streams. The data must be transformed in real time and then stored in Amazon S3 in Parquet format. Which AWS service should be used for the transformation step?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink.. Option D is correct because Amazon Kinesis Data Analytics for Apache Flink is a serverless service that can run Apache Flink applications to perform real-time transformations on streaming data. Option A (AWS Lambda) can be used but is limited by execution time and is not optimized for complex streaming transformations. Option B (Amazon Kinesis Data Firehose) can transform data using Lambda functions but is more suited for loading data. Option C (Amazon EMR) is for batch processing, not real-time streaming.

Answer

Amazon Kinesis Data Firehose with a Lambda transformation.

Answer

Amazon EMR running Apache Spark Streaming.

Answer

AWS Lambda with a Kinesis trigger.

Question 2

A company is using AWS Glue to catalog data in Amazon S3. The data is stored in CSV format, but the schema is not consistent across all files. Which TWO actions can the company take to handle schema evolution and ensure the Glue Data Catalog is up to date? (Choose TWO.)

Accepted Answer

Configure the Glue crawler to update the table schema on each run.. Options B and D are correct. Enabling schema update in the Glue crawler (B) allows the crawler to update the existing table schema when new columns are found. Running the crawler on a schedule (D) ensures that changes are captured periodically. Option A (manual schema update) is not automated. Option C (using a single schema) is not practical for evolving data. Option E (disabling schema update) would not update the catalog.

Answer

Manually update the Glue Data Catalog tables whenever the schema changes.

Answer

Disable schema update in the crawler and add partitions manually.

Answer

Require all data producers to use a single fixed schema.

Question 3

A company is using Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The data is delivered in 5-minute intervals. The company wants to reduce the delivery frequency to 1 minute to get data faster. Which parameter should be changed in the Firehose delivery stream configuration?

Accepted Answer

Reduce the buffer interval from 300 seconds to 60 seconds.. Option A is correct because the buffer interval determines how often data is delivered to the destination. Changing it from 300 seconds to 60 seconds will deliver data every minute. Option B (buffer size) affects delivery based on data volume, not time. Option C (compression) does not affect frequency. Option D (partitioning) does not affect delivery frequency.

Answer

Increase the buffer size to trigger delivery sooner.

Answer

Enable dynamic partitioning to deliver data more frequently.

Answer

Enable compression to reduce data size and speed up delivery.

Question 4

A company needs to ingest data from a relational database into Amazon S3 for analytics. The database is an Amazon RDS MySQL instance. Which AWS service should be used for a one-time historical data load?

Accepted Answer

AWS Database Migration Service (DMS). Option C is correct because AWS Database Migration Service (DMS) can perform a one-time full load from RDS MySQL to S3. Option A is incorrect because Glue ETL can do it but DMS is specialized for databases. Option B is incorrect because Kinesis Data Firehose is for streaming, not database. Option D is incorrect because Athena queries data, not ingest.

Answer

AWS Glue ETL

Answer

Amazon Athena

Answer

Amazon Kinesis Data Firehose

Question 5

A company is using AWS Glue ETL to transform and load data from Amazon S3 to Amazon Redshift. The data engineer notices that the job is taking longer than expected. Which TWO actions can improve the job performance?

Accepted Answer

Partition the source data in S3.. Options B and D are correct because increasing the number of DPUs and using partitioning in S3 improve parallelism and reduce data scanned. Option A is incorrect because S3 Transfer Acceleration is for uploads, not ETL. Option C is incorrect because Redshift Spectrum is for querying, not Glue ETL. Option E is incorrect because a larger instance type for Redshift does not affect Glue job performance.

Answer

Use Amazon Redshift Spectrum to query data directly.

Answer

Enable S3 Transfer Acceleration.

Answer

Use a larger Redshift node type.

Question 6

A company is using Amazon MSK (Managed Streaming for Apache Kafka) to ingest real-time data. They need to transform the data using custom Java code before writing to Amazon S3. The transformation must be fault-tolerant and exactly-once semantics are required. Which AWS service should be used?

Accepted Answer

Kafka Connect with S3 Sink Connector. Option C is correct because Kafka Connect with S3 Sink Connector supports exactly-once semantics and custom transformations via Single Message Transforms (SMTs) or custom connectors. Option A (Kinesis Data Analytics for Apache Flink) is for Flink, not Kafka. Option B (AWS Lambda) does not provide exactly-once semantics from MSK. Option D (Amazon EMR) is for batch processing.

Answer

Amazon EMR with Spark Streaming

Answer

AWS Lambda consumer for MSK

Answer

Kinesis Data Analytics for Apache Flink

Question 7

A data engineer is designing a data ingestion pipeline for IoT sensor data. The sensors send JSON messages every second. The data must be available in Amazon S3 within 5 minutes and must be transformed (JSON to Parquet) before storage. Which combination of services meets these requirements?

Accepted Answer

Amazon Kinesis Data Firehose with data transformation and Parquet conversion. Option C is correct because Kinesis Firehose can ingest streaming data and transform it to Parquet before delivering to S3. Option A is wrong because Lambda from S3 would cause delay. Option B is wrong because Glue streaming jobs add complexity. Option D is wrong because Kinesis Data Analytics does not output to S3 directly.

Answer

Amazon Kinesis Data Streams with AWS Glue streaming ETL

Answer

Amazon Kinesis Data Analytics with output to S3

Answer

Amazon S3 with S3 Event Notifications to AWS Lambda for transformation

Question 8

A data engineer needs to ingest streaming data from thousands of IoT devices and immediately process each record with minimal latency. Which AWS service should be used as the ingestion point?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed for real-time streaming data ingestion with low latency. AWS Glue is for batch ETL, S3 is object storage, and Lambda is compute but not an ingestion endpoint itself.

Answer

AWS Lambda

Answer

Amazon S3

Answer

AWS Glue

Question 9

A data engineer needs to schedule a nightly ETL job that reads from an Amazon RDS database and writes to Amazon S3 in Parquet format. The solution must be serverless and minimize cost. Which TWO AWS services should be used? (Choose TWO.)

Accepted Answer

Amazon S3. AWS Glue can run serverless ETL jobs. Amazon S3 is the destination. Lambda could trigger but not run the ETL itself; Data Pipeline is not serverless; Athena is query-only.

Answer

AWS Data Pipeline

Answer

AWS Lambda

Answer

Amazon Athena

Question 10

A company needs to ingest data from an on-premises Oracle database into Amazon S3 on a daily basis. The data volume is about 100 GB per day. Which AWS service is BEST suited for this task?

Accepted Answer

Use AWS Database Migration Service (DMS) to replicate data to S3.. Option C is correct because AWS Database Migration Service (DMS) can continuously replicate data from Oracle to S3, and it supports full load and change data capture (CDC). Option A (AWS DataSync) is for file-based transfers, not database replication. Option B (Amazon Kinesis Data Firehose) is for streaming data, not database pull. Option D (AWS Glue) is for ETL but does not natively support continuous CDC from Oracle.

Answer

Use AWS DataSync to copy the database files to S3.

Answer

Use Amazon Kinesis Data Firehose with a database connector.

Answer

Use AWS Glue to extract data from Oracle and write to S3.

Question 11

A company is using Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is delivered in JSON format. The company wants to convert the data to Apache Parquet format before delivery to reduce storage costs and improve query performance. How can this be achieved?

Accepted Answer

Use the AWS Glue Data Catalog to define a schema and configure Firehose to use it for Parquet conversion.. Option B is correct because Kinesis Data Firehose can convert the input data to Parquet or ORC format using a schema from the AWS Glue Data Catalog. Option A is incorrect because Firehose does not support a built-in transformation to Parquet without a schema. Option C is incorrect because Lambda can be used for custom transformations, but Firehose natively supports Parquet conversion using Glue. Option D is incorrect because Athena is a query service, not a transformation service.

Answer

Deliver data to S3 as JSON, then use Amazon Athena to convert to Parquet.

Answer

Write an AWS Lambda function to transform the data to Parquet and deliver it to S3.

Answer

Configure the Firehose stream to convert data to Parquet automatically without any additional setup.

Question 12

A company is using AWS Glue to process data from an Amazon S3 data lake. The Glue job runs daily and transforms data into multiple output formats. Which TWO actions can the company take to optimize the Glue job's performance and reduce costs? (Choose TWO.)

Accepted Answer

Increase the number of DPUs allocated to the job.. Options A and D are correct. Using job bookmarking (A) ensures that only new data is processed, reducing processing time and cost. Using a larger number of DPUs (D) can improve performance for data-intensive jobs, though it increases cost per job, but if the job runs faster, overall cost may be lower. Option B (using a smaller number of DPUs) would reduce performance. Option C (increasing the job timeout) does not optimize performance or cost. Option E (disabling job bookmarking) would reprocess all data, increasing cost.

Answer

Reduce the number of DPUs (Data Processing Units) allocated to the job.

Answer

Disable job bookmarking to force full reprocessing every run.

Answer

Increase the job timeout to allow more time for processing.

Question 13

A company uses Amazon Kinesis Data Firehose to ingest application logs into an Amazon S3 bucket. The logs are in JSON format. The data engineering team wants to convert the logs from JSON to Parquet format before landing in S3. What is the most cost-effective way to achieve this?

Accepted Answer

Configure the Firehose delivery stream to convert the data to Parquet using a schema from AWS Glue.. Option B is correct because Kinesis Data Firehose can convert the input data format to Parquet using a schema from AWS Glue. Option A is incorrect because Lambda can do this but incurring additional compute cost. Option C is incorrect because Athena queries raw data and would not help with ingestion. Option D is incorrect because Glue ETL would add cost and latency.

Answer

Use Amazon Athena to query the JSON data and write results in Parquet format.

Answer

Use an AWS Lambda function to transform each record to Parquet and send to Firehose.

Answer

Use an AWS Glue ETL job to run on a schedule and convert JSON to Parquet in S3.

Question 14

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time using custom Python code before being stored in Amazon S3. Which AWS service should be used to perform this transformation?

Accepted Answer

Kinesis Data Analytics for Apache Flink. Option C is correct because Kinesis Data Analytics for Apache Flink allows running custom Apache Flink applications for real-time data processing. Option A (Kinesis Data Firehose) does not support custom Python code; it uses built-in transformations. Option B (AWS Lambda) can be used but requires custom code and is typically used for simple transformations. Option D (Amazon EMR) is for large-scale batch processing, not real-time.

Answer

Amazon EMR with Spark Streaming

Answer

AWS Lambda function triggered by Kinesis Data Streams

Answer

Kinesis Data Firehose with custom data transformation

Question 15

A company wants to ingest data from multiple SaaS applications into Amazon S3 using a fully managed service that supports schema discovery and transformation. Which AWS service should they use?

Accepted Answer

Amazon AppFlow. Option B is correct because Amazon AppFlow is a fully managed integration service that supports SaaS sources, schema discovery, and data transformation. Option A (AWS Glue) is for ETL but not for SaaS ingestion directly. Option C (Amazon Kinesis) is for streaming data. Option D (AWS Data Pipeline) is not fully managed for SaaS.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue

Answer

AWS Data Pipeline