DEA-C01 Practice Test 43 — 15 Questions

Question 1

A company is using Amazon S3 to store log files. The security team requires that all data be encrypted in transit. Which of the following ensures encryption in transit for S3?

Accepted Answer

Use HTTPS (SSL/TLS) when accessing S3 endpoints.. Encryption in transit for Amazon S3 is ensured by using HTTPS (SSL/TLS) when accessing S3 endpoints. HTTPS encrypts data as it travels between the client and S3, protecting it from eavesdropping. This is the standard method to enforce encryption in transit for S3, and it can be enforced via bucket policies that deny requests not using HTTPS.

Answer

Use Amazon S3 Transfer Acceleration.

Answer

Enable client-side encryption before uploading to S3.

Answer

Use server-side encryption with S3 managed keys (SSE-S3).

Question 2

A data engineer needs to share a dataset stored in an S3 bucket with a partner AWS account. The partner should be able to read the data without needing to authenticate with the engineer's account. The engineer must not share any secret keys. Which approach should be used?

Accepted Answer

Write a bucket policy that grants access to the partner account's IAM role.. A bucket policy granting read access to the partner account's IAM role is the correct approach because it enables cross-account access without sharing credentials. The partner's IAM role assumes its own identity, and the bucket policy explicitly authorizes that principal, so no secret keys are exchanged. This is the standard AWS cross-account access pattern for S3 data sharing.

Answer

Generate presigned URLs and share them with the partner.

Answer

Make the bucket publicly readable.

Answer

Create an IAM user with access keys and share them with the partner.

Question 3

A data engineer is using AWS Glue DataBrew to profile a dataset in Amazon S3. The dataset contains a column 'customer_id' that should be unique. The engineer runs a profile job and notices that the 'customer_id' column has a uniqueness metric of 98%. The engineer needs to identify the duplicate values. Which DataBrew feature should the engineer use to display the duplicate values?

Accepted Answer

Use the 'Value frequencies' view in the profile job results to see the most frequent values.. AWS Glue DataBrew profile jobs include a 'Value frequencies' view that lists the most frequent values in a column with their counts. For a column that should be unique, this view directly reveals which customer_id values are duplicated. The other options provide summary statistics or rules but do not display the specific duplicate values.

Answer

Use the 'Column profile' view to see a histogram of values.

Answer

Use the 'Column statistics' view in the profile job results to see the count of duplicates.

Answer

Use the 'Data quality rules' feature to create a rule that flags duplicates.

Question 4

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. Amazon Kinesis Data Firehose is a fully managed service that automatically delivers streaming data to destinations like Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, making it ideal when you need a fully managed delivery destination without managing the ingestion pipeline. In contrast, Amazon Kinesis Data Streams requires you to build and manage consumers to process and deliver data, so if you need a fully managed destination, Firehose is the correct choice.

Answer

The ability to compress data before storage

Answer

The maximum throughput supported per shard

Question 5

A data engineer is using AWS Step Functions to orchestrate a daily ingestion workflow. The workflow calls an AWS Glue job, then runs an AWS Lambda function to validate output, and finally starts an Amazon Redshift stored procedure. The engineer needs to ensure that if the Glue job fails, the workflow retries the Glue job up to three times with exponential backoff before failing the entire execution. Which Step Functions feature should the engineer configure?

Accepted Answer

Configure a Retry policy on the Glue job task with MaxAttempts and BackoffRate.. Step Functions Retry policies are defined on individual states and accept MaxAttempts, IntervalSeconds, MaxDelaySeconds, and BackoffRate. Applying a Retry to the Glue job task with MaxAttempts of three and a BackoffRate above one produces exponential backoff retries scoped to that task. If all attempts fail, the state then fails and can be handled by a Catch block.

Answer

Enable idempotency on the Glue job so repeated invocations succeed.

Answer

Increase the Step Functions execution timeout to allow the Glue job to rerun.

Answer

Configure a Catch block on the Glue job task to transition to a failure state.

Question 6

A data engineer is building an AWS Glue ETL job that reads from an Amazon S3 bucket containing CSV files and writes to an Amazon Redshift table. The job runs successfully but the Redshift table ends up empty. The engineer checks the AWS Glue job run metrics and sees that the job processed 0 rows. The S3 bucket contains files under the prefix 'data/'. The Glue crawler created a table with the correct schema. What is the MOST likely cause of the empty output?

Accepted Answer

The Glue job's script is filtering out all records because of an incorrect partition predicate or a misconfigured pushdown predicate.. When an AWS Glue job completes successfully but processes zero rows, the issue is typically within the job's transformation logic rather than permissions or connectivity. A pushdown predicate or filter that does not match any data will cause the job to read no records. Checking the script for such filters is the first step.

Answer

The Redshift table is in a different AWS Region than the S3 bucket.

Answer

The Glue job's output is written to a temporary S3 location instead of Redshift because the connection is misconfigured.

Answer

The Glue job's IAM role lacks s3:GetObject permission on the bucket.

Question 7

A company uses Amazon Kinesis Data Streams to ingest real-time logs from thousands of applications. The data must be transformed and enriched with reference data from Amazon S3 before being stored in Amazon S3 in Parquet format. The transformation logic is stateful and requires exactly-once processing. Which AWS service should the data engineer use to perform the transformation?

Accepted Answer

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) with a Flink application.. The need for stateful processing, exactly-once semantics, and enrichment with reference data from S3 points to a stream processing framework. Amazon Managed Service for Apache Flink provides these capabilities with native support for state, exactly-once processing, and connectors to Kinesis and S3. Other services either lack stateful processing, exactly-once guarantees, or both.

Answer

Amazon Kinesis Data Firehose with AWS Lambda transformation.

Answer

AWS Glue streaming ETL job.

Answer

AWS Lambda with Kinesis Data Streams event source mapping.

Question 8

A data engineer is investigating intermittent failures in an AWS Step Functions state machine that orchestrates a nightly ETL workflow. The state machine invokes an AWS Glue job, then an Amazon EMR step, then an AWS Lambda function. Occasionally a task fails transiently and the entire workflow stops instead of retrying. The engineer needs the workflow to automatically retry failed tasks with exponential backoff before alerting. What should the engineer do?

Accepted Answer

Add a Retry field to the relevant state with ErrorEquals, IntervalSeconds, MaxAttempts, and BackoffRate values.. Step Functions state machines are defined in Amazon States Language, and each state can include a Retry field that matches specific error names and retries with an interval that grows by a BackoffRate multiplier. This declarative approach centralizes retry logic in the orchestrator, so transient failures in Glue, EMR, or Lambda are retried automatically before any alert is raised.

Answer

Set the state machine's execution role to include the 'states:Retry' IAM action and re-run the workflow.

Answer

Configure the Glue job, EMR step, and Lambda function each with their own internal retry logic and remove error handling from the state machine.

Answer

Enable the state machine's 'Retry on failure' setting in the Amazon CloudWatch console.

Question 9

A data engineer runs an AWS Glue job that reads from an Amazon Kinesis Data Stream and writes to Amazon S3. The job must process records in order per shard and must checkpoint progress so it can resume after a failure without reprocessing all records. Which Glue configuration BEST supports this?

Accepted Answer

Use the Glue streaming ETL job type with `--starting_position` set to `TRIM_HORIZON` and rely on Glue's built-in checkpointing in the job bookmarks.. Glue streaming ETL jobs natively support Kinesis Data Streams and use job bookmarks to track progress per shard. Setting `--starting_position` to `TRIM_HORIZON` establishes the initial read point, and bookmarks checkpoint processed records so a failed job resumes without reprocessing everything. Batch jobs, disabled bookmarks, and custom checkpoint tables either lose data, reprocess, or add unnecessary overhead.

Answer

Use a Glue streaming ETL job with a custom checkpoint table in Amazon DynamoDB and set `--starting_position` to `TRIM_HORIZON`.

Answer

Use a Glue streaming ETL job with `--starting_position` set to `LATEST` and disable job bookmarks to always read new records.

Answer

Use a standard Glue batch job with the Kinesis connector and set `--starting_position` to `LATEST` for each run.

Question 10

A data engineer maintains an AWS Glue job that incrementally processes new files in Amazon S3 using job bookmarks. After a schema change in the source data added a new column, the engineer updated the Glue Data Catalog table. Subsequent job runs still process only previously seen files and ignore newly arrived objects. The engineer verifies that new files exist in the prefix and that the bookmark state was not reset. Which factor most likely explains why new files are being skipped?

Accepted Answer

Job bookmarks use the source path and a transformation context key; changing the job script or the catalog table can invalidate the bookmark state.. Job bookmarks persist state keyed to the source path, transformation context, and job parameters. Modifying the job script or the referenced Data Catalog table alters that context, which can cause the bookmark to skip newly arrived objects despite their presence. Resetting the bookmark or recreating the job restores expected incremental behavior. Format, IAM, and S3 consistency are not the cause here.

Answer

Amazon S3 eventual consistency delays object visibility, so bookmarks never see files written within the same hour.

Answer

Job bookmarks only support Amazon S3 sources when the files are in Parquet format, so JSON files are silently skipped.

Answer

The Glue job's IAM role lacks s3:ListBucket permission, so the job cannot detect newly arrived objects.

Question 11

A data engineer is building an AWS Glue ETL job that reads from an AWS Glue Data Catalog table backed by Amazon S3. The job must process only records added since the last successful run to reduce cost and runtime. The source data is partitioned by year, month, and day. Which approach should the engineer use?

Accepted Answer

Enable job bookmarks in the AWS Glue job and use the default transformation_ctx on the source DynamicFrame.. AWS Glue job bookmarks persist state across runs and, when a transformation context is set on the source, cause the job to read only data that has not been processed before. For partitioned S3 sources, bookmarks track new partitions and files, which precisely matches the requirement to process only records added since the last successful run while reducing cost and runtime.

Answer

Use the AWS Glue Data Catalog's update time and a custom script that queries the catalog for recently changed partitions before reading S3.

Answer

Set the job's maximum concurrency to 1 and rely on the S3 object's last modified timestamp in the script to filter records.

Answer

Configure the job to use a pushdown predicate that filters on the year, month, and day partition columns using the current date.

Question 12

A data engineer is troubleshooting an Amazon Redshift cluster where nightly COPY loads from Amazon S3 are intermittently slow and sometimes fail with 'S3ServiceException' errors. The engineer suspects the cluster's network configuration and load design are contributing. Which TWO actions should the engineer take to improve load performance and reliability? (Choose two.)

Accepted Answer

Split large input files into multiple files sized roughly between 1 MB and 1 GB and load them in parallel.. Redshift COPY performance depends on parallelizing reads across slices and on a stable network path to S3. Splitting input into many files sized between 1 MB and 1 GB lets slices read concurrently, while enhanced VPC routing keeps S3 traffic inside the VPC and avoids public internet variability that produces intermittent S3ServiceException errors.

Answer

Convert all input files to a single large gzip file to minimize the number of S3 GET requests during COPY.

Answer

Increase the cluster's node count and change the distribution style of all tables to EVEN before every load.

Answer

Disable compression on the input files so Redshift can parse records faster.

Question 13

A data engineer is using AWS Step Functions to orchestrate a series of AWS Glue jobs and Amazon EMR steps. The state machine has a task that starts a Glue job and waits for completion using the 'StartJobRun' API with the 'sync' integration. Occasionally, the Step Functions execution fails with the error: 'States.TaskFailed: Glue job failed with error: ResourceNumberLimitExceededException'. The engineer confirms that the Glue job itself runs successfully when triggered manually. What is the most likely cause of this intermittent failure?

Accepted Answer

The AWS Glue job is hitting the concurrent job run limit for the account.. The error 'ResourceNumberLimitExceededException' from AWS Glue indicates that the account has reached the maximum number of concurrent job runs. Step Functions may trigger the job multiple times concurrently, or other processes may be running Glue jobs. The engineer should check the Glue service quotas and either increase the concurrent runs limit or implement a queueing mechanism to serialize job starts.

Answer

The Glue job's script has a bug that causes it to fail only when run in parallel.

Answer

The IAM role used by Step Functions lacks permissions to start the Glue job.

Answer

The Step Functions state machine is exceeding the maximum number of concurrent executions.

Question 14

A data engineer manages an AWS Glue ETL job that processes millions of small JSON files in Amazon S3. The job is slow and often fails with an OutOfMemory error on the driver. The engineer wants to improve performance without changing the output format. Which solution should the engineer implement?

Accepted Answer

Use AWS Glue's groupFiles and groupSize options to combine small files into larger groups.. Grouping small files with groupFiles and groupSize is the most effective way to handle millions of small files in AWS Glue. It reduces the number of input splits, lowers driver memory pressure, and improves overall job performance. Other options either do not address the driver memory issue or require changing the output format.

Answer

Enable job bookmarks to track processed files and skip already-processed data.

Answer

Convert the JSON files to Parquet format using an AWS Glue crawler before running the ETL job.

Answer

Increase the number of DPUs allocated to the job to provide more memory per executor.

Question 15

A data engineer is using AWS Step Functions to orchestrate a series of AWS Glue jobs. During a recent run, a Glue job failed due to a transient network issue, and the Step Functions execution stopped. The engineer needs to ensure that the workflow can automatically retry the failed Glue job up to three times before considering the step failed. Which Step Functions state configuration should the engineer implement?

Accepted Answer

Add a Retry field to the Task state with ErrorEquals set to States.ALL and MaxAttempts set to 3.. The Retry field in Step Functions is designed to automatically retry a task when specified errors occur. By setting ErrorEquals to States.ALL and MaxAttempts to 3, the Glue job will be retried up to three times on any error, including transient network issues. The Catch field is for fallback, not retry. Glue job definitions do not have a retry count, and timeout/heartbeat fields are for monitoring, not retry logic.

Answer

Set the TimeoutSeconds and HeartbeatSeconds fields in the Task state to trigger a retry on failure.

Answer

Add a Catch field to the Task state with ErrorEquals set to States.ALL and Next set to a retry state.

Answer

Configure the Glue job itself to have a maximum retry count of 3 in its job definition.