DEA-C01 Mock Exam 78 — 25 Questions

Question 1

A data engineer is building an AWS Glue ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3. The DynamoDB table has a large number of items, and the engineer needs to ensure the job reads the data efficiently without consuming too much provisioned throughput. Which method should the engineer use to read from DynamoDB?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use the DynamoDB export to S3 feature to export the table to S3, then read from S3 in the Glue job.. DynamoDB export to S3 allows you to export a full table snapshot to S3 without consuming read capacity, making it ideal for large-scale ETL reads. The Glue job can then read the exported data from S3 in parallel, achieving high throughput without impacting the DynamoDB table's performance.

Answer

Use Amazon Kinesis Data Streams to stream changes from DynamoDB to S3, then read from S3.

Answer

Use AWS Glue's native DynamoDB connector with the read throughput parameter set to a high value.

Answer

Use AWS Glue's DynamoDB connector with the 'dynamodb.scan' option to scan the table.

Question 2

A data engineer is configuring an AWS Glue ETL job that reads from an Amazon S3 source with many small JSON files and writes to Amazon S3 in Parquet. The job runs slowly and produces many tiny output files. The engineer wants to improve throughput and reduce the number of output files without changing the source data layout. (Choose two.)

Accepted Answer

Call coalesce or repartition on the DataFrame before writing the Parquet output.. Small input files cause per-object overhead that slows reads, and the output file count equals the number of write-time partitions. Grouping input files with the groupFiles option consolidates reads, while repartitioning or coalescing the DataFrame before writing reduces the number of output partitions. Together these improve throughput and produce fewer, larger Parquet files without altering the source layout.

Answer

Increase the number of Glue DPUs allocated to the job.

Answer

Enable AWS Glue job bookmarks to skip previously processed files.

Answer

Convert the output format from Parquet to CSV to reduce file count.

Question 3

A data engineer maintains an AWS Glue ETL job that processes JSON files from Amazon S3 and writes Parquet to another S3 location. The job has been running successfully for months. Recently, the job started failing intermittently with the error 'Unable to infer schema for JSON'. The engineer confirms the source bucket contains valid JSON files. Which action should the engineer take to resolve the failure?

Accepted Answer

Define an explicit schema using a Glue Data Catalog table or a DynamicFrame with a specified schema, instead of relying on schema inference.. The error 'Unable to infer schema for JSON' occurs when AWS Glue cannot automatically determine a consistent schema from the source files. This often happens when JSON records have varying fields or data types. Defining an explicit schema through the Glue Data Catalog or programmatically ensures the job can parse the data without relying on inference, resolving the failure while maintaining data integrity.

Answer

Enable job bookmarks to track previously processed files and avoid reprocessing.

Answer

Increase the number of AWS Glue DPUs allocated to the job to handle larger file sizes.

Answer

Convert the source JSON files to CSV format before running the job, then update the job to read CSV.

Question 4

A company uses Amazon RDS for PostgreSQL to store customer data. The data engineer needs to ensure that the database can be restored to any point in time within the last 35 days. The engineer also wants to minimize the impact on the production database during backups. What should the engineer do?

Accepted Answer

Enable automated backups and set the backup retention period to 35 days.. Amazon RDS automated backups provide point-in-time recovery to any second within the retention period, which can be set up to 35 days. They are managed by RDS, require no manual intervention, and have minimal performance impact. Manual snapshots, Multi-AZ, and read replicas do not offer the same continuous PITR capability.

Answer

Create a manual snapshot every day and retain them for 35 days.

Answer

Enable Multi-AZ deployment and rely on the standby replica for backups.

Answer

Configure a read replica and take snapshots from the read replica.

Question 5

A data engineer is building an AWS Glue ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3. The DynamoDB table has a large number of items, and the engineer needs to ensure the job reads the data efficiently without consuming too much provisioned throughput. Which method should the engineer use to read from DynamoDB?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Grant the Glue job role secretsmanager:GetSecretValue on the secret ARN and retrieve the secret in the job using the Glue Secrets Manager connection property.. AWS Glue connections support a secretId property that fetches credentials from Secrets Manager at runtime. Granting the job role GetSecretValue on the specific secret keeps access narrow and avoids plaintext credentials in scripts or parameters, while also enabling rotation and audit through Secrets Manager.

Answer

Embed the password directly in the Glue ETL script and restrict access to the script in Amazon S3.

Answer

Use an IAM database authentication token for the JDBC connection instead of a password.

Answer

Store the password in an AWS Glue job parameter and mark it as encrypted using a KMS key.

Question 6

A data engineer maintains an Amazon Kinesis Data Streams pipeline that feeds an AWS Lambda consumer. During traffic spikes, the Lambda function is throttled and records are reprocessed, causing duplicate entries in the downstream Amazon S3 sink. The engineer needs to reduce duplicates with the LEAST code change. What should the engineer do?

Accepted Answer

Make the Lambda function idempotent by using the Kinesis sequence number as a deduplication key before writing to S3.. Duplicate delivery in Kinesis-to-Lambda pipelines is expected because Lambda retries batches after throttling or errors, and Kinesis guarantees at-least-once delivery. Making the consumer idempotent by keying on the Kinesis sequence number ensures that reprocessed records are detected and not written twice, which requires only a small change inside the function.

Answer

Configure the event source mapping's StartingPosition to LATEST and enable enhanced fan-out on the stream.

Answer

Increase the Lambda function's reserved concurrency and set the event source mapping's MaximumRetryAttempts to a low value.

Answer

Enable the 'ReportBatchItemFailures' feature on the event source mapping and return the failed sequence numbers so only failed records are retried.

Question 7

A data engineer needs to run a one-time AWS Glue ETL job that reads data from an Amazon S3 bucket and writes transformed Parquet files to another S3 bucket. The job does not need a schedule and should be run immediately after creation. Which method should the engineer use to start the job?

Accepted Answer

Use the AWS Glue console or AWS CLI to start the job run directly.. For a one-time, unscheduled AWS Glue job, the simplest way to start it is to invoke StartJobRun through the console, AWS CLI, or SDK. Triggers and workflows are used for scheduling or orchestration and are not needed when the job should run immediately after creation.

Answer

Configure a scheduled trigger with a cron expression that runs once.

Answer

Create a trigger with type ON_DEMAND and activate it.

Answer

Create a workflow and add the job to it, then start the workflow.

Question 8

A company uses AWS Lake Formation to manage fine-grained access to a data lake in Amazon S3. A data analyst needs to query a table in the AWS Glue Data Catalog that contains columns with sensitive data. The analyst must be able to see only non-sensitive columns and only rows where the region column equals 'US'. The analyst uses Amazon Athena for queries. Which Lake Formation permission model should the data engineer implement?

Accepted Answer

Grant the analyst SELECT permission on the table and create a data filter that includes only non-sensitive columns and a row filter for region = 'US'.. Lake Formation data filters provide column-level and row-level security by allowing you to specify which columns and rows a principal can access. By granting SELECT with a data filter that includes only non-sensitive columns and a row filter for region = 'US', the analyst is restricted appropriately. IAM policies, Athena views, and Glue job bookmarks do not enforce the required fine-grained access control.

Answer

Grant the analyst SELECT permission on the table and use AWS Glue job bookmarks to filter sensitive data.

Answer

Create a view in Athena that selects only non-sensitive columns and filters rows for region = 'US', and grant the analyst access to the view.

Answer

Grant the analyst SELECT permission on the table and use an IAM policy to deny access to sensitive columns.

Question 9

A data engineer needs to ingest streaming data from an Amazon Kinesis Data Stream into an Amazon S3 bucket. The data must be delivered in near real-time and stored in Parquet format for efficient querying. The engineer wants to minimize custom code. Which solution should the engineer use?

Accepted Answer

Use Amazon Kinesis Data Firehose to deliver the stream to S3, and enable record format conversion to Parquet using an AWS Glue table.. Kinesis Data Firehose provides a fully managed way to deliver streaming data from Kinesis Data Streams to S3. It supports automatic conversion to Parquet using a schema from the AWS Glue Data Catalog, requiring no custom code. This meets the near real-time and Parquet requirements while minimizing development effort, unlike solutions that require writing Lambda functions, Flink applications, or Glue scripts.

Answer

Use Amazon Kinesis Data Analytics to process the stream and write Parquet to S3 using a Flink application.

Answer

Use AWS Lambda to read from the Kinesis Data Stream and write Parquet files to S3 using the AWS SDK for pandas (awswrangler).

Answer

Use AWS Glue streaming ETL job to read from the Kinesis Data Stream and write Parquet to S3.

Question 10

A data engineer must load data from an Amazon DynamoDB table into an Amazon S3 data lake nightly. The table is approximately 800 GB and the nightly window is tight. The engineer wants a fully managed, serverless option that exports the table to S3 without consuming DynamoDB read capacity or writing custom code. Which solution meets these requirements?

Accepted Answer

Use the native Amazon DynamoDB export to Amazon S3 feature, which exports a point-in-time snapshot without consuming read capacity.. Amazon DynamoDB's native export to Amazon S3 creates a full point-in-time snapshot of the table directly in an S3 bucket without consuming read capacity and without requiring custom code or replication infrastructure. It is serverless and scales to large tables, which fits the tight nightly window and the no-capacity-impact constraint.

Answer

Run an AWS Database Migration Service task with DynamoDB as the source and Amazon S3 as the target on a nightly schedule.

Answer

Use AWS Glue with a DynamoDB connection and the dynamodb.read.throughput.percent option set to 100.

Answer

Enable DynamoDB Streams, attach an AWS Lambda function, and write each stream record to Amazon S3 as it arrives.

Question 11

A data engineer is building an AWS Glue ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3. The DynamoDB table has a large number of items, and the engineer needs to ensure the job reads the data efficiently without consuming too much provisioned throughput. Which method should the engineer use to read from DynamoDB?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use the AWS Glue Studio visual editor and add a Flatten transformation to the job.. The Flatten transformation in AWS Glue Studio is purpose-built to un-nest arrays and structs into rows without custom code, directly satisfying the need for an efficient, code-free flattening step in a Glue ETL job on nested JSON.

Answer

Configure the Glue crawler to automatically flatten nested JSON during cataloging.

Answer

Write a custom Python shell script in Glue to recursively flatten the JSON.

Answer

Use an Apache Spark filter transformation to explode the nested arrays.

Question 12

A data engineer is configuring an AWS Glue ETL job that reads from an Amazon S3 bucket and writes transformed records to an Amazon Redshift cluster. The job must load data in parallel and use an Amazon Redshift IAM role for authentication. Which connection option should the engineer configure in the Glue job to enable parallel loading and IAM-based authentication?

Accepted Answer

Create an AWS Glue connection of type Amazon Redshift, specify the cluster, database, and an IAM role with permissions to access both S3 and Redshift, and enable the 'Use Redshift COPY' option.. The correct configuration uses an AWS Glue connection of type Amazon Redshift with an IAM role and the Redshift COPY option enabled. This lets Glue stage data in Amazon S3 and issue a COPY command, which parallelizes loading across Redshift slices and authenticates via IAM instead of database credentials. Other connection types may provide connectivity but do not combine parallel COPY-based loading with IAM role authentication in the way Glue's native Redshift integration does.

Answer

Use a JDBC connection with the Redshift cluster endpoint, database name, and a database user credential stored in AWS Secrets Manager.

Answer

Configure an AWS Glue connection of type JDBC with the Redshift JDBC URL and set the 'redshift_tmp_dir' parameter to an S3 path, using a database password stored in AWS Glue.

Answer

Use an AWS Glue connection of type Amazon S3 and configure the Redshift cluster endpoint in the job's script using boto3.

Question 13

A data engineer needs to ensure that all objects written to an S3 bucket are encrypted with SSE-KMS using a specific customer managed key, and that any upload without that encryption is rejected. The engineer has created the bucket and the KMS key. Which approach will enforce this requirement at the bucket level?

Accepted Answer

Add a bucket policy that denies s3:PutObject requests where the s3:x-amz-server-side-encryption header is not aws:kms or the s3:x-amz-server-side-encryption-aws-kms-key-id does not match the specified key.. To enforce that every object is uploaded with a specific SSE-KMS key, a bucket policy must explicitly deny s3:PutObject requests that do not carry the required encryption headers. Default encryption only applies when no encryption is specified, and it cannot reject requests that specify a different method. The policy approach is the only one that guarantees non-compliant uploads are denied.

Answer

Enable S3 Block Public Access on the bucket.

Answer

Use an S3 Lifecycle rule to transition objects to S3 Glacier with encryption.

Answer

Configure the bucket's default encryption to use SSE-KMS with the customer managed key.

Question 14

A data engineer manages an Amazon DynamoDB table used for a high-traffic gaming leaderboard. The table uses on-demand capacity mode and has a partition key of UserId (string) with no sort key. The leaderboard must retrieve the top 100 scores across all users. Currently, the engineer scans the entire table and sorts the results in application code, which takes several seconds and consumes large amounts of read capacity. What should the engineer do to improve the performance of retrieving the top scores?

Accepted Answer

Create a global secondary index (GSI) with a constant partition key and Score as the sort key, then query the index in descending order with a limit of 100.. The leaderboard requires a global, sorted view of scores. A GSI with a constant partition key and Score as sort key allows efficient Query operations that return the highest scores first using ScanIndexForward=false and a Limit. This design avoids full table scans and scales well.

Answer

Increase the table's read capacity by switching to provisioned mode with a high RCU value.

Answer

Add a local secondary index (LSI) on the Score attribute and query it with a limit of 100.

Answer

Enable DynamoDB Streams and use AWS Lambda to maintain a separate sorted list in Amazon ElastiCache for Redis.

Question 15

A data engineer needs to move 50 TB of existing data from an on-premises data center into Amazon S3 as a one-time migration. The data center has a 1 Gbps internet connection that is shared with production traffic, and the migration must complete within two weeks without disrupting production. Which approach should the engineer use?

Accepted Answer

Use AWS Snowball Edge Storage Optimized devices to ship the data to AWS.. A 50 TB one-time migration over a shared 1 Gbps link cannot reliably finish in two weeks without harming production traffic. AWS Snowball Edge Storage Optimized devices move the data physically, sidestepping the bandwidth constraint entirely. Network-based options such as DataSync, Transfer Family, or Transfer Acceleration all depend on the same limited connection and therefore do not meet the stated requirements.

Answer

Use Amazon S3 Transfer Acceleration with multipart uploads from the data center.

Answer

Use AWS Transfer Family with SFTP to upload the files to Amazon S3.

Answer

Use AWS DataSync over the internet to copy the data directly to Amazon S3.

Question 16

A data engineer is using AWS Glue Studio to build a job that reads from Amazon S3, applies a filter, and writes to Amazon Redshift. The engineer needs to ensure the job can be rerun safely without creating duplicate rows in Redshift if a previous run partially succeeded. Which design choice best meets this requirement?

Accepted Answer

Write the output to a staging table in Redshift and use a MERGE or upsert operation keyed on a business key to apply changes idempotently.. Loading into a staging table and then merging into the target on a business key is the canonical idempotent pattern for Redshift. Reruns re-execute the same merge deterministically, so partial failures do not leave duplicates. This decouples the risky load from the final apply step and supports safe retries.

Answer

Enable AWS Glue job bookmarks and rely on them to prevent duplicate writes to Redshift on rerun.

Answer

Configure the Redshift write to use a pre-action and post-action that truncate the target table before each load.

Answer

Set the Glue job's write mode to append and add a unique constraint on the Redshift target table to reject duplicates.

Question 17

A data engineer is using AWS Step Functions to orchestrate a data pipeline that includes an AWS Glue job, an Amazon EMR step, and an Amazon Redshift stored procedure. The engineer needs to ensure that if the Glue job fails, the pipeline stops and does not proceed to the EMR step. Which Step Functions state type should be used to handle the error and stop the execution?

Accepted Answer

A Fail state that transitions to a terminal state and stops the execution.. In AWS Step Functions, the Fail state is used to stop an execution and mark it as failed. When a Glue job fails, a Catch block can transition to a Fail state, ensuring the pipeline terminates without executing the EMR step. This is the standard way to handle errors and prevent downstream processing.

Answer

A Wait state that delays the next step until the Glue job is manually restarted.

Answer

A Parallel state that runs the EMR step concurrently with the Glue job to reduce overall time.

Answer

A Choice state that evaluates the output of the Glue job and branches to an end state if it fails.

Question 18

A data engineer runs an AWS Glue job that reads a large partitioned Parquet dataset from Amazon S3 and writes aggregated results to another S3 prefix. The job runs daily and currently reprocesses the entire dataset each time, which is becoming expensive. The engineer wants subsequent runs to process only data added since the last successful run, based on the job's state. Which feature should the engineer enable?

Accepted Answer

AWS Glue job bookmarks. AWS Glue job bookmarks maintain state across runs, recording which S3 objects, partitions, or JDBC rows were processed. When enabled, a subsequent run reads only new or modified data, avoiding full reprocessing. This is the built-in mechanism for incremental Glue jobs, unlike partition indexes, S3 Inventory, or retry settings, which do not track processed data across runs.

Answer

AWS Glue job retry with a smaller max retry count

Answer

AWS Glue Data Catalog partition indexes

Answer

Amazon S3 Inventory reports on the source prefix

Question 19

A data engineer is configuring an Amazon Redshift cluster for a workload that runs large nightly ELT jobs loading data from Amazon S3 and then executes complex analytical queries. The team wants to improve query performance and reduce the time the cluster spends on data loading. Which TWO configuration choices should the engineer make? (Choose two.)

Accepted Answer

Use the COPY command with a manifest file and parallelism to load data from multiple S3 objects.. Bulk loading with COPY and a manifest, combined with parallelism, uses Redshift's MPP architecture to shorten load time. Choosing an appropriate distribution key on large fact tables reduces inter-slice data movement during complex joins, improving query performance. Row-by-row inserts, restricting maintenance to the load window, and applying ALL distribution to every table all degrade performance or increase load time.

Answer

Enable automatic vacuum and analyze only during the nightly load window.

Answer

Load data using single-row INSERT statements in a loop from the application.

Answer

Set the cluster's distribution style to ALL for every table to avoid joins.

Question 20

A data engineer is designing an ingestion pipeline that uses Amazon Kinesis Data Firehose to deliver streaming records into an Amazon S3 bucket. The records arrive as JSON, and downstream consumers require Parquet with a stable schema. The engineer must configure the Firehose delivery stream so records are converted to Parquet before landing in S3. (Choose two.)

Accepted Answer

Enable record format conversion on the Firehose delivery stream and select Apache Parquet as the output format.. Firehose record format conversion converts JSON to Parquet in flight, and it requires a schema reference. The supported schema source is an AWS Glue table in the Data Catalog that describes the incoming JSON structure. Enabling conversion with Apache Parquet output and pointing to a matching Glue table together produce Parquet files in S3 for downstream analytics.

Answer

Enable Amazon S3 Object Lambda access points on the destination bucket to convert objects to Parquet on read.

Answer

Configure an AWS Lambda function as the delivery stream's transformation to rewrite each record into Parquet bytes.

Answer

Set the S3 destination prefix to include a .parquet file extension so Firehose writes columnar files.