DEA-C01 Data Ingestion and Transformation Practice Test 36 — 15 Questions

Question 1

A data pipeline uses AWS Glue to read CSV files from an S3 bucket, transform them, and write Parquet back to S3. The pipeline runs daily and processes about 500 GB per run. The team wants to reduce costs without increasing runtime. Which approach is most effective?

Accepted Answer

Optimize the Glue script to select only required columns and filter rows early.. Option D is correct because using column pruning and predicate pushdown reduces the amount of data scanned, lowering costs without affecting runtime. Option A is wrong because increasing DPUs may increase cost and runtime unpredictably. Option B is wrong because converting to Parquet upfront would add an extra processing step and cost. Option C is wrong because job bookmarks track processed data but do not reduce cost per run.

Answer

Pre-convert the CSV files to Parquet in S3 using a separate process.

Answer

Enable job bookmarks to skip already processed data.

Answer

Increase the number of DPUs for the Glue job to improve parallelism.

Question 2

A company needs to ingest data from an on-premises Oracle database into Amazon S3 for analytics. The data volume is about 1 TB initially, with daily incremental updates of about 10 GB. Which TWO services can be combined to achieve this with minimal custom code?

Accepted Answer

Amazon S3. Options A and D are correct because AWS DMS can perform full load and ongoing replication, and S3 is the target. Option B (Kinesis Data Streams) is for real-time streaming, not database migration. Option C (Glue) can be used but often requires more custom code than DMS. Option E (Athena) is a query service, not data movement.

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Answer

Amazon Athena

Question 3

A data engineer is designing a streaming pipeline using Amazon Kinesis Data Streams. The data must be transformed in real-time and then stored in Amazon S3 for long-term retention. Which THREE services can be used together to achieve this?

Accepted Answer

Amazon Kinesis Data Analytics. Options B, C, and D are correct: Kinesis Data Streams ingests, Kinesis Data Analytics transforms, and Kinesis Data Firehose writes to S3. Option A (Glue) is batch-oriented, not real-time. Option E (Athena) is for querying, not streaming.

Answer

AWS Glue

Answer

Amazon Athena

Question 4

A data pipeline uses AWS Glue to process large CSV files. The team notices that some jobs fail with out-of-memory errors. Which TWO configuration changes can help mitigate this issue?

Accepted Answer

Increase the number of DPUs for the Glue job.. Options A and D are correct: increasing DPUs and enabling autoscaling provide more memory. Option B (reducing DPUs) would worsen the problem. Option C (conversion to Parquet) may reduce memory but is not a direct configuration change for the Glue job. Option E (job bookmarks) does not affect memory.

Answer

Reduce the number of DPUs to limit concurrency.

Answer

Convert input files from CSV to Parquet.

Answer

Enable job bookmarks.

Question 5

Refer to the exhibit. A Glue ETL job failed. What is the most likely cause?

Accepted Answer

Some source files have a different schema than others.. Option D is correct because the error 'Cannot merge incompatible schemas' indicates differing schemas in the source data. Option A is wrong because insufficient DPUs cause memory errors, not schema conflicts. Option B is wrong because partition pruning is about filtering, not schema. Option C is wrong because bookmarks track processed files, not schema issues.

Answer

The job bookmarks are misconfigured.

Answer

The source data has inconsistent partitioning.

Answer

The job ran out of memory due to insufficient DPUs.

Question 6

Refer to the exhibit. A Lambda function with this IAM policy is used to process records from a Kinesis stream and write to S3. The function is failing with access denied errors when writing to S3. What is the issue?

Accepted Answer

The S3 bucket name in the policy does not match the actual bucket used by the function.. Option B is correct because the policy allows s3:PutObject on 'my-bucket/*' but the function might be writing to a different bucket or the resource ARN is missing permissions for the bucket itself (e.g., s3:ListBucket). Option A is wrong because the policy includes GetRecords. Option C is wrong because the policy has kinesis actions. Option D is wrong because Kinesis Data Analytics is not mentioned.

Answer

The function needs to use Kinesis Data Analytics for transformation.

Answer

The Kinesis stream ARN is incorrect.

Answer

The Lambda function does not have permission to read from the Kinesis stream.

Question 7

Refer to the exhibit. A data engineer is troubleshooting a Glue job that reads objects from this S3 bucket. The job runs successfully but produces no output. The Glue catalog table points to the same S3 path. What is the most likely cause?

Accepted Answer

The S3 key does not follow Hive-style partitioning (e.g., year=2024/month=01).. Option C is correct because Glue expects a partition structure (e.g., year=2024/month=01/day=01) to automatically discover partitions. The current key has no partition markers. Option A is wrong because Glue can read application/octet-stream. Option B is wrong because storage class is STANDARD. Option D is wrong because metadata is irrelevant to Glue catalog.

Answer

The object metadata is too large.

Answer

The StorageClass is not supported by Glue.

Answer

The ContentType is not supported by Glue.

Question 8

A company wants to move data from an Amazon RDS for MySQL database to Amazon Redshift for analytics. The data needs to be refreshed daily. Which AWS service is best suited for this?

Accepted Answer

AWS Glue. Option C is correct because AWS Glue can connect to RDS and Redshift, and schedule jobs. Option A (DMS) is for ongoing replication, not necessarily for daily batch. Option B (Athena) queries S3. Option D (EMR) is for big data processing but overkill for simple transfer.

Answer

AWS Database Migration Service (DMS)

Answer

Amazon EMR

Answer

Amazon Athena

Question 9

A data pipeline ingests JSON data from an S3 bucket using AWS Glue. The JSON files contain nested structures, and the team wants to flatten them for analysis in Amazon Athena. Which Glue transformation is most appropriate?

Accepted Answer

Relationalize. Option D is correct because Relationalize is specifically designed to flatten nested JSON into relational tables. Option A (Map) applies a function to each record. Option B (Filter) removes records. Option C (Join) combines datasets.

Answer

Filter

Answer

Join

Answer

Map

Question 10

A company uses Amazon Kinesis Data Firehose to ingest data into an S3 bucket. The data is in JSON format and the team wants to convert it to Parquet before storage. Which TWO configurations are required?

Accepted Answer

Create a Glue table with the schema of the data.. Options A and B are correct: Firehose can convert to Parquet if you specify a data format conversion and a Glue table (schema). Option C (Kinesis Data Analytics) is not needed. Option D (Athena) is for querying. Option E (Lambda) can be used but is not required.

Answer

Use Kinesis Data Analytics to transform data to Parquet.

Answer

Configure a Lambda function to convert data on the fly.

Answer

Set up an Athena table to read the data.

Question 11

A data engineering team is designing a batch processing workflow using AWS Glue. The job reads from an S3 bucket, transforms data, and writes to another S3 bucket. The job runs daily and processes new data incrementally. Which THREE features should they use to optimize performance and cost?

Accepted Answer

Enable Glue job autoscaling.. Options B, C, and D are correct: job bookmarks for incremental processing, autoscaling for dynamic resource allocation, and predicate pushdown for reducing data scanned. Option A (increasing DPUs manually) is not optimal. Option E (converting to Parquet) may help but is not a Glue feature.

Answer

Convert all input data to Parquet format before processing.

Answer

Manually increase the number of DPUs for each run.

Question 12

A company wants to ingest data from thousands of IoT devices into AWS for real-time analytics. The data is in JSON format and each device sends about 1 KB every second. Which service should be used as the primary ingestion point?

Accepted Answer

Amazon Kinesis Data Streams. Option B is correct because Kinesis Data Streams can handle high throughput real-time data from many sources. Option A (SQS) is for message queuing, not streaming analytics. Option C (Firehose) could be used but lacks the ability to have multiple consumers for real-time processing. Option D (IoT Core) is specifically for IoT but not as general-purpose.

Answer

AWS IoT Core

Answer

Amazon Kinesis Data Firehose

Answer

Amazon SQS

Question 13

A data engineer runs an AWS Glue job that reads from a JDBC connection to a PostgreSQL database. The job fails with a 'Connection timed out' error. The Glue job runs in a VPC with the appropriate security group. What is the most likely cause?

Accepted Answer

The network ACL associated with the Glue job's subnet is blocking outbound traffic.. Option C is correct because a network ACL can block outbound traffic from the VPC to the database. Option A is wrong because the error is network, not authentication. Option B is wrong because the security group allows inbound, but need outbound. Option D is wrong because it's a network issue, not schema.

Answer

The Glue job does not have permission to access the database.

Answer

The security group does not allow inbound traffic from the Glue job.

Answer

The database credentials are incorrect.

Question 14

A data pipeline ingests streaming data from thousands of IoT devices into Kinesis Data Streams. The data must be transformed using a simple field mapping before being stored in S3. Which service should be used to perform the transformation with minimal operational overhead?

Accepted Answer

Kinesis Data Firehose with a Lambda transformation. Option C is correct because Kinesis Data Firehose can perform simple transformations using Lambda functions before delivering data to S3. Option A is wrong because Glue ETL is better for complex batch transformations. Option B is wrong because Kinesis Data Analytics is for real-time analytics, not simple field mapping. Option D is wrong because Lambda alone would require managing the delivery to S3.

Answer

AWS Lambda function invoked by the Kinesis stream

Answer

AWS Glue ETL job

Answer

Kinesis Data Analytics

Question 15

A company uses S3 as a data lake. They want to ingest on-premises relational database data daily with full-load snapshots. The data volume is 500 GB per day. The database is accessible over the internet. Which service should they use for this ingestion?

Accepted Answer

AWS Database Migration Service (DMS). Option D is correct because AWS Database Migration Service (DMS) can perform full-load migrations from on-premises databases to S3. Option A is wrong because Kinesis Data Firehose is for streaming data, not database snapshots. Option B is wrong because Glue ETL can read from JDBC but is not optimized for large full-load snapshots. Option C is wrong because Transfer Family is for file transfers over SFTP, not database connections.

Answer

Kinesis Data Firehose

Answer

AWS Glue ETL job reading from JDBC

Answer

AWS Transfer Family