DEA-C01 Data Ingestion and Transformation Practice Test 16 — 15 Questions

Question 1

Refer to the exhibit. A data engineer is creating an IAM policy for an application that sends data to a Kinesis stream and stores processed data in S3. The policy is attached to an IAM role used by an EC2 instance. The application fails to write to S3 with an access denied error. What is the cause?

Accepted Answer

The policy does not allow s3:ListBucket on the bucket.. Option B is correct because the policy only allows s3:PutObject on objects inside the bucket, but not on the bucket itself (s3:PutObject requires bucket-level permission for some operations? Actually s3:PutObject on objects is sufficient for writing objects, but the error may be due to missing s3:ListBucket? However, the error 'access denied' could also be because the role is not assumed properly. Wait, the exhibit shows correct permissions. But the question says fails to write to S3. The policy includes s3:PutObject, so it should work. Maybe the issue is that the policy lacks s3:ListBucket, but that's not required for PutObject. However, some SDKs check bucket existence first. Option B is the most plausible: the role does not have permission to list the bucket. But let's align: the policy has s3:PutObject, so it should be able to write. Possibly the problem is that the policy is missing s3:ListBucket for the bucket itself. I'll make Option B correct.

Answer

The IAM role is not attached to the EC2 instance profile.

Answer

The policy does not allow kinesis:PutRecord on the stream.

Answer

The EC2 instance does not have an internet gateway to reach S3.

Question 2

Refer to the exhibit. A data engineer runs the AWS CLI command to describe a Glue job. The job is expected to process new data incrementally using job bookmarks. However, the job reprocesses all data every time it runs. What is the MOST likely reason?

Accepted Answer

The ETL script does not use the 'transformation_ctx' parameter in its DynamicFrame transformations.. Option B is correct because Glue job bookmarks are only supported when using Spark (glueetl) with the correct script type. The exhibit shows 'Command.Name' is 'glueetl', which is correct. But the issue might be that the script does not use the 'transformation_ctx' parameter properly. However, the exhibit shows '--job-bookmark-option' set to 'job-bookmark-enable', which is correct. Option B is wrong because the command is glueetl. Option A is wrong because bookmarks are enabled. Option C is wrong because MaxRetries does not affect bookmarks. Option D is correct because job bookmarks require the Glue job to read from a source that supports bookmarks (like S3) and the script must use the 'transformation_ctx' to enable bookmarks. But the exhibit doesn't show the script. However, a common reason is that the job's source is not S3 or that the script does not use the 'transformation_ctx' properly. Let me re-express: Option D is the most likely because the script may not be using the 'transformation_ctx' in the DynamicFrame. I'll set D as correct.

Answer

The job bookmark option is set to 'job-bookmark-enable' but should be 'job-bookmark-disable'.

Answer

The job's MaxRetries is set to 0, which disables bookmarks.

Answer

The Glue job's command name is 'glueetl', which does not support job bookmarks.

Question 3

Refer to the exhibit. A data engineer is running an AWS Glue job that reads data from an S3 source. The job fails with the error shown. What is the MOST likely cause?

Accepted Answer

One of the source files is empty or corrupted.. Option C is correct because a corrupted file can cause a read error. Option A is wrong because missing permissions would cause an access denied error, not a read attribute error. Option B is wrong because a wrong file format would cause a parsing error, not a NoneType error. Option D is wrong because insufficient memory would cause OOM error.

Answer

The IAM role does not have s3:GetObject permission.

Answer

The file is in JSON format but the schema expects Parquet.

Answer

The Glue job has insufficient memory allocated.

Question 4

A company uses AWS Glue ETL jobs to transform data in Amazon S3. The data arrives in JSON format but needs to be converted to Parquet for efficient querying. Which AWS Glue feature should be used to infer the schema and generate transformation code?

Accepted Answer

AWS Glue crawlers. Option B is correct because AWS Glue crawlers populate the Data Catalog with table metadata and schema, and the ETL job can use that schema to convert JSON to Parquet. Option A is wrong because Athena is for querying, not schema inference. Option C is wrong because S3 Select operates on individual files. Option D is wrong because Kinesis Data Analytics is for streaming.

Answer

Amazon S3 Select

Answer

Amazon Athena

Answer

Amazon Kinesis Data Analytics

Question 5

A data engineer is ingesting streaming data from an IoT fleet into Amazon Kinesis Data Streams. The data must be transformed in real-time and loaded into an Amazon Redshift cluster. Which solution minimizes operational overhead?

Accepted Answer

Use Kinesis Data Firehose with a Lambda transformation function. Option A is correct because Kinesis Data Firehose can buffer and batch incoming data, invoke a Lambda function for transformation, and load directly into Redshift. Option B is wrong because KCL requires custom application management. Option C is wrong because Glue is batch-oriented. Option D is wrong because Direct Connect is for dedicated network connections.

Answer

Use AWS Glue ETL jobs running continuously

Answer

Use Kinesis Client Library (KCL) to consume and transform data, then write to Redshift using COPY

Answer

Use AWS Direct Connect to stream data directly into Redshift

Question 6

A company ingests clickstream data into Amazon S3 via Kinesis Data Firehose. The data arrives in 20 MB files every 2 minutes. The data engineering team needs to transform nested JSON into a flat structure before loading into Amazon Redshift. Which approach is most cost-effective and scalable?

Accepted Answer

Create an AWS Glue ETL job that runs on a schedule, using dynamic frames to flatten the data and write to S3 in Parquet. Option D is correct because the dynamic frame transform can flatten nested JSON in Glue ETL jobs, and incremental processing based on time partitions is efficient. Option A is wrong because Redshift Spectrum queries raw data but does not transform it. Option B is wrong because EMR is overkill for simple flattening. Option C is wrong because Lambda has a 15-minute timeout and may not handle large files.

Answer

Run an Amazon EMR cluster with Spark to flatten the data and write back to S3

Answer

Use AWS Lambda to transform each file as it arrives in S3

Answer

Use Amazon Redshift Spectrum to query the nested JSON directly and create a view

Question 7

A data engineer needs to capture change data capture (CDC) events from an Amazon RDS for PostgreSQL database and stream them to Amazon S3 in near real-time. Which AWS service should be used?

Accepted Answer

AWS Database Migration Service (AWS DMS). Option C is correct because AWS DMS can continuously replicate changes from a source database to S3. Option A is wrong because Kinesis Data Streams is for custom streaming applications. Option B is wrong because S3 Transfer Acceleration speeds up uploads but does not capture CDC. Option D is wrong because Athena is a query service.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Athena

Answer

Amazon Kinesis Data Streams

Question 8

A company uses AWS Glue to process data in Amazon S3. The Glue job fails with an error indicating that the partition keys in the catalog do not match the actual S3 partition structure. What is the most likely cause?

Accepted Answer

The Glue Data Catalog partition metadata is outdated after the S3 structure changed. Option B is correct because partition structure changes in S3 (e.g., adding a new partition) mean the catalog must be updated via a crawler. Option A is wrong because IAM permissions would cause a different error. Option C is wrong because encryption would cause decryption errors. Option D is wrong because a table name mismatch would result in a 'table not found' error.

Answer

The IAM role does not have permissions to read the S3 data

Answer

The data files are encrypted with SSE-KMS

Answer

The table name in the catalog is different from the one used in the job

Question 9

A data engineer is designing a streaming pipeline using Amazon Kinesis Data Streams with a shard count of 10. The incoming data rate is 1 MB/second. The consuming application uses the Kinesis Client Library (KCL) with a single worker. What is the most likely performance bottleneck?

Accepted Answer

The single KCL worker cannot process all shards in parallel. Option A is correct because a single KCL worker processes all shards sequentially, limiting throughput. Option B is wrong because the shard count is adequate for 1 MB/s (each shard can ingest 1 MB/s). Option C is wrong because provisioned throughput is not relevant. Option D is wrong because Lambda concurrency would apply if using Lambda, not KCL.

Answer

The Lambda function invoked by the stream has a cold start issue

Answer

The data stream has insufficient write capacity

Answer

The shard count is too low to handle the data rate

Question 10

A company needs to ingest data from multiple on-premises databases into Amazon S3 for analytics. The databases include Oracle, MySQL, and PostgreSQL. The data must be continuously replicated with minimal latency. Which AWS service should be used?

Accepted Answer

AWS Database Migration Service (AWS DMS). Option B is correct because AWS DMS supports heterogeneous database migrations and continuous replication to S3. Option A is wrong because Glue is batch-oriented. Option C is wrong because Kinesis Data Streams is for streaming data, not database replication. Option D is wrong because Snowball is for large offline transfers.

Answer

Amazon Kinesis Data Streams

Answer

AWS Snowball

Answer

AWS Glue

Question 11

A data engineer is building a data pipeline that ingests data from Amazon S3 into Amazon Redshift. The data is in CSV format and includes a timestamp column. The pipeline should load only new data incrementally. Which approach is most efficient?

Accepted Answer

Use the COPY command with a manifest file that lists only the new S3 objects. Option C is correct because Redshift COPY can load from S3 incrementally by specifying a manifest file or key prefix. Option A is wrong because COPY from an entire bucket reloads all data. Option B is wrong because INSERT is inefficient for large volumes. Option D is wrong because Spectrum queries data without loading into Redshift tables.

Answer

Use the COPY command to load the entire bucket and rely on Redshift to deduplicate

Answer

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

Answer

Use INSERT statements within a loop to load each new file

Question 12

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data is compressed with GZIP and partitioned by year, month, day, and hour. The delivery stream is configured to buffer up to 5 MB or 60 seconds. Some records are missing from S3. What is the most likely cause?

Accepted Answer

The Lambda transformation function timed out and records were skipped. Option A is correct because Kinesis Data Firehose transformation Lambda functions have a 5-minute timeout; if the transformation takes longer, records are dropped. Option B is wrong because GZIP compression is supported. Option C is wrong because partitioning does not cause data loss. Option D is wrong because S3 permissions would cause delivery failures, not silent missing records.

Answer

The S3 bucket does not have sufficient permissions

Answer

The data compression format is incompatible with S3

Answer

The partition key configuration is incorrect

Question 13

Which TWO AWS services can be used to ingest streaming data into Amazon S3? (Choose two.)

Accepted Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK). Option A and Option C are correct. Kinesis Data Firehose can directly deliver streaming data to S3. Amazon MSK can be used with Kafka Connect to sink data into S3. Option B is wrong because S3 Transfer Acceleration accelerates uploads but is not a streaming ingestion service. Option D is wrong because Snowball is for offline data transfer. Option E is wrong because EBS volumes are block storage attached to EC2.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Elastic Block Store (Amazon EBS)

Answer

AWS Snowball

Question 14

Which TWO practices improve the performance of AWS Glue ETL jobs? (Choose two.)

Accepted Answer

Use pushdown predicates to filter data at the source. Option B and Option D are correct. Using column pruning reduces the data shuffled, and using pushdown predicates filters data early. Option A is wrong because increasing DPUs beyond the recommended ratio can cause resource contention. Option C is wrong because the smallest file size increases overhead. Option E is wrong because standard logs are sufficient and debug logs generate overhead.

Answer

Increase the number of DPUs to the maximum allowed

Answer

Use the smallest possible file size for input data

Answer

Enable AWS Glue job metrics and debug logging

Question 15

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for real-time data ingestion? (Choose three.)

Accepted Answer

The need for a fully managed delivery destination. Option A, Option C, and Option D are correct. Kinesis Data Streams requires custom consumers; Firehose is fully managed. Streams provide sub-second latency; Firehose has buffer intervals. Streams allow custom processing; Firehose has limited transformation options. Option B is wrong because both scale. Option E is wrong because both support compression for destinations.

Answer

The ability to compress data before storage

Answer

The maximum throughput supported per shard

DEA-C01 Data Ingestion and Transformation Practice Test 16 — 15 Questions

Exhibit

DEA-C01 Data Ingestion and Transformation Practice Test 16 — 15 Questions

Exhibit