DEA-C01 Data Ingestion and Transformation Practice Test 32 — 15 Questions

Question 1

A company uses AWS Glue to transform data in an S3 data lake. The transformation logic requires joining two large datasets that are each hundreds of gigabytes. The Glue job runs out of memory. Which configuration change will most likely resolve this issue?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs provides more memory for the join operation. Glue automatically distributes data across workers, so more workers mean more total memory.

Answer

Repartition the data before the join.

Answer

Use a different file format like Parquet with compression.

Answer

Use the 'spark.sql.autoBroadcastJoinThreshold' setting to broadcast the smaller table.

Question 2

A company wants to ingest streaming data from social media feeds into AWS for real-time analytics. Which TWO services can directly ingest streaming data without writing custom code? (Choose TWO.)

Accepted Answer

Amazon AppFlow. Amazon Kinesis Data Firehose can directly ingest streaming data and deliver to destinations. AWS Glue can stream from Kafka but not directly ingest from social media without custom connectors. Kinesis Data Streams requires producers to send data, not direct ingestion. AppFlow can ingest from SaaS applications including social media.

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Answer

Amazon S3 Transfer Acceleration

Question 3

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 4

A company uses Amazon Kinesis Data Firehose to deliver data to an S3 bucket. The data contains personally identifiable information (PII) that must be redacted before storage. Which THREE actions can achieve this requirement? (Choose THREE.)

Accepted Answer

Use AWS Glue ETL to read from the S3 bucket and write redacted data to another S3 bucket.. Option A is correct because AWS Glue ETL can read data from the source S3 bucket, apply transformations (including redacting PII fields using custom scripts or built-in transforms), and write the cleaned data to a target S3 bucket. This decouples the redaction from the ingestion pipeline and allows for complex, schema-aware transformations using Apache Spark or Python.

Answer

Use Amazon Athena to query the data and redact PII on the fly.

Answer

Use AWS Database Migration Service (AWS DMS) to replicate data and apply transformations.

Question 5

A data engineer attached this IAM policy to a Lambda function used to transform data in S3. The function is unable to write output to the bucket. What is the most likely reason?

Accepted Answer

The policy does not allow the s3:ListBucket action on the bucket.. The policy allows GetObject and PutObject on objects, but not the s3:ListBucket action required to check existence or list objects. The function likely needs ListBucket to write or verify.

Answer

The resource ARN is missing the bucket-level ARN.

Answer

The policy does not allow the s3:DeleteObject action.

Answer

The policy does not allow the s3:PutObjectAcl action.

Question 6

A data engineer runs the command shown. The consumer application is unable to read data older than 24 hours. What is the most likely cause?

Accepted Answer

The retention period is set to 24 hours, so data older than 24 hours is deleted.. The stream's retention period is 24 hours, meaning data is automatically deleted after 24 hours. The consumer tries to read data older than 24 hours, which is no longer available.

Answer

The shard has reached its maximum sequence number.

Answer

The stream is encrypted with KMS, preventing access.

Answer

The stream is in ACTIVE status but not processing data.

Question 7

A data engineer reviews the Glue job configuration. The job fails when processing large datasets. The error message indicates out-of-memory in the executors. Which change to the job configuration will most directly address this issue?

Accepted Answer

Change the worker type from Standard to G.2X.. The job uses 5 workers of Standard type. To increase memory, the engineer should use G.1X or G.2X worker types which provide more memory per worker, or increase the number of workers. Among options, changing worker type to G.2X directly increases memory.

Answer

Increase the timeout from 30 to 60 minutes.

Answer

Increase the number of workers from 5 to 10.

Answer

Set MaxRetries to 3.

Question 8

A company uses AWS Glue to catalog data in Amazon S3. The data arrives in Parquet format, but the crawler fails to update the schema when new columns are added. What is the most likely cause?

Accepted Answer

The crawler configuration has 'Update the table definition' set to 'Ignore the change'.. Option C is correct because the crawler's catalog update policy may be set to 'Update the table definition' but not 'Add new columns only', or it might be set to 'Ignore the change'. Option A is wrong because Parquet schema evolves naturally. Option B is wrong because S3 bucket versioning does not affect crawler schema updates. Option D is wrong because the Glue Data Catalog is the target, not the source.

Answer

The Glue Data Catalog is not configured to accept schema changes.

Answer

Parquet files do not support schema evolution.

Answer

The S3 bucket has versioning disabled.

Question 9

A data engineering team needs to ingest streaming data from thousands of IoT devices. The data must be processed in near real-time and stored in Amazon S3 in Apache Parquet format partitioned by device_id and timestamp. Which combination of services should the team use to minimize operational overhead and cost?

Accepted Answer

Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, and optionally AWS Lambda.. Option D is correct. Kinesis Data Streams ingests streaming data, Kinesis Data Firehose delivers data to S3 with built-in Parquet conversion and partitioning, and optionally uses Lambda for lightweight transformations. Option A is wrong because EC2-based ingestion is high overhead. Option B is wrong because S3 Transfer Acceleration is for large file transfers, not streaming. Option C is wrong because Kafka and Flink require more operational overhead.

Answer

Amazon Kinesis Data Streams, Amazon EC2 for processing, and Amazon S3 with lifecycle policies.

Answer

Amazon MSK (Kafka), AWS Glue Streaming, and Amazon S3.

Answer

Amazon S3 Transfer Acceleration and AWS Lambda for event-driven transformation.

Question 10

A company uses AWS Glue ETL jobs to transform data and load it into Amazon Redshift. The jobs are failing with 'Out of Memory' errors. What is the most cost-effective way to resolve this issue without changing the transformation logic?

Accepted Answer

Increase the number of G.1X workers in the Glue job configuration.. Option A is correct. Increasing the number of workers (DPUs) adds parallelism without changing logic. Option B is wrong because increasing worker type is more expensive than adding workers. Option C is wrong because a different engine may not be compatible. Option D is wrong because Redshift Spectrum is for querying S3, not for ETL memory.

Answer

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

Answer

Change the worker type to G.2X and keep the same number of workers.

Answer

Switch the job from Python to Scala.

Question 11

A company needs to transfer 50 TB of historical data from an on-premises HDFS cluster to Amazon S3. The network bandwidth is limited to 1 Gbps, and the transfer must complete within 10 days. The data is compressible. Which solution is MOST appropriate?

Accepted Answer

Use AWS Snowball Edge to physically ship the data.. Option D is correct. AWS Snowball Edge is a physical device that can transfer large volumes quickly, bypassing network limitations. Option A is wrong because 1 Gbps would take ~111 hours for 50 TB, and with overhead it may exceed 10 days. Option B is wrong because DataSync also uses network and would be too slow. Option C is wrong because S3 Transfer Acceleration is for existing internet transfers, not bulk offline.

Answer

Use AWS DataSync over a Direct Connect connection.

Answer

Use Amazon S3 Transfer Acceleration with multipart uploads.

Answer

Use multiple parallel AWS CLI sync commands over the internet.

Question 12

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The delivery occasionally fails due to 'ThrottlingException' from S3. What should the team do to resolve this issue without losing data?

Accepted Answer

Increase the Firehose buffer size and buffer interval to reduce the number of S3 PUT requests.. Option C is correct. Kinesis Firehose can buffer failed records and retry; you can also increase the buffer size or interval to reduce S3 PUT frequency. Option A is wrong because disabling error logging is not a solution. Option B is wrong because S3 Transfer Acceleration is for speed, not throttling. Option D is wrong because DynamoDB is not the target.

Answer

Enable S3 Transfer Acceleration on the destination bucket.

Answer

Disable error logging in Firehose to reduce API calls.

Answer

Configure Firehose to deliver data to Amazon DynamoDB instead.

Question 13

A data engineer needs to transform JSON data into CSV format using AWS Glue. The transformation is simple and must be executed on a schedule. Which Glue component is MOST suitable?

Accepted Answer

Glue ETL job. Option B is correct. A Glue ETL job can be scheduled to run a script that transforms data. Option A is wrong because crawlers only catalog data. Option C is wrong because the Data Catalog is a metadata repository. Option D is wrong because a development endpoint is for interactive development, not production scheduling.

Answer

Glue Crawler

Answer

Glue Data Catalog

Answer

Glue Development Endpoint

Question 14

A company uses AWS DMS to migrate an on-premises Oracle database to Amazon RDS for PostgreSQL. The migration completes, but the target table has more rows than the source. Which is the MOST likely cause?

Accepted Answer

Target tables lack unique constraints, causing DMS to insert duplicate rows.. Option C is correct. DMS may re-apply changes from the transaction log if the target tables have no unique constraints, leading to duplicate rows. Option A is wrong because DMS uses CDC, not a full load. Option B is wrong because PostgreSQL is case-sensitive, but that would cause missing rows, not extras. Option D is wrong because binary replication is not relevant.

Answer

DMS used binary replication which included extra metadata rows.

Answer

Oracle and PostgreSQL handle case sensitivity differently.

Answer

DMS performed a full load instead of ongoing replication.

Question 15

A company wants to ingest data from SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate and updates occur frequently. Which AWS service is BEST suited for this task?

Accepted Answer

Amazon AppFlow. Option A is correct. AppFlow is designed to transfer data from SaaS applications to AWS services like S3. Option B is wrong because Glue is for ETL, not direct SaaS ingestion. Option C is wrong because Kinesis is for streaming, not batch from SaaS. Option D is wrong because DMS is for database migrations.

Answer

Amazon Kinesis Data Streams

Answer

AWS Database Migration Service (DMS)

Answer

AWS Glue