DEA-C01 Data Operations and Support Practice Test 27 — 15 Questions

Question 1

A data engineer is setting up a data pipeline to ingest streaming data from an IoT fleet. The data must be processed in near real-time and stored in Amazon S3 for analytics. Which THREE AWS services should the engineer consider using?

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

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose (B) is correct because it is a fully managed service designed to reliably load streaming data directly into Amazon S3 (and other destinations) with near real-time delivery, requiring no server management. Amazon Kinesis Data Streams (E) is correct because it ingests and buffers high-throughput streaming data from IoT fleets in real time, allowing custom consumers to process the data before it is stored in S3. AWS Lambda (C) is correct because it can be invoked by Kinesis to process streaming records in near real-time, enabling serverless transformation or enrichment of the IoT data before it lands in S3. Amazon EMR (A) is not the best fit here because it is a batch-oriented big data processing platform (Hadoop/Spark) rather than a streaming ingestion service, and AWS Glue (D) is primarily a serverless ETL and data catalog service for batch and some streaming jobs, not a dedicated real-time ingestion pipeline component for this scenario.

Answer

Amazon EMR

Answer

AWS Glue

Question 2

A data pipeline uses AWS Glue to process data from Amazon S3 and write results to Amazon Redshift. The pipeline fails intermittently with the error 'S3ServiceException: Access Denied'. The IAM role used by Glue has permissions to read from the S3 bucket. What is the most likely cause of this error?

Accepted Answer

The S3 bucket policy denies access to the Glue job's IAM role. S3 bucket policies can explicitly deny access even if the IAM role allows it. Option A is wrong because S3 bucket region does not cause access denied errors. Option B is wrong because S3 Server Access Logging does not affect access permissions. Option D is wrong because S3 Transfer Acceleration is not related to access denied errors.

Answer

The S3 bucket is in a different AWS Region than the Glue job

Answer

S3 Server Access Logging is enabled and blocking requests

Answer

The S3 bucket has S3 Transfer Acceleration enabled

Question 3

A data engineer is designing a data lake on Amazon S3. The data includes sensitive personally identifiable information (PII). Which combination of services would provide the most comprehensive data protection?

Accepted Answer

Enable S3 Block Public Access and use Amazon Macie to discover and classify PII. S3 Block Public Access prevents accidental public exposure of buckets and objects, which is the leading cause of S3 data leaks, while Amazon Macie uses machine learning and pattern matching to discover, classify, and alert on PII stored in S3. Together they address both the access-control and data-discovery dimensions of protecting sensitive PII, making this the most comprehensive combination.

Answer

Use S3 Transfer Acceleration and enable versioning

Answer

Enable S3 server-side encryption with AWS KMS

Answer

Use Amazon CloudWatch Logs to monitor access and enable MFA Delete

Question 4

A company uses Amazon DynamoDB as the primary data store for a high-traffic application. Recently, read latency has increased significantly. The DynamoDB table has on-demand capacity mode. Which action is MOST effective to reduce read latency?

Accepted Answer

Add a DynamoDB Accelerator (DAX) cluster in front of the table. DynamoDB Accelerator (DAX) is an in-memory cache purpose-built for DynamoDB that reduces read latency from single-digit milliseconds to microseconds for eventually consistent reads. For a high-traffic, read-heavy application on on-demand mode, adding a DAX cluster in front of the table is the most direct and effective way to cut read latency without changing capacity mode or table architecture.

Answer

Switch the table to provisioned capacity mode with higher read capacity

Answer

Increase the read capacity units in the table's auto scaling settings

Answer

Enable DynamoDB Global Tables to distribute reads across regions

Question 5

A data engineer needs to monitor the number of records processed by an AWS Glue ETL job. Which CloudWatch metric should the engineer use?

Accepted Answer

glue.driver.aggregate.numRecords. Glue emits a 'glue.driver.aggregate.numRecords' metric for the number of records processed. Option A is wrong because 'glue.driver.aggregate.elapsedTime' is for time. Option C is wrong because 'glue.driver.aggregate.bytesRead' is for bytes. Option D is wrong because 'glue.driver.aggregate.recordsRead' is not a standard metric.

Answer

glue.driver.aggregate.elapsedTime

Answer

glue.driver.aggregate.bytesRead

Answer

glue.driver.aggregate.recordsRead

Question 6

A company stores sensitive data in Amazon S3 and requires that all data be encrypted at rest. The data is accessed by multiple AWS services. Which solution meets the encryption requirement with the LEAST operational overhead?

Accepted Answer

Enable S3 default encryption with SSE-S3. Enabling S3 default encryption with SSE-S3 applies AES-256 encryption automatically to all objects at rest with zero key management overhead. AWS manages the keys, and no per-request KMS calls are needed, making it the lowest operational overhead solution that meets the encryption-at-rest requirement.

Answer

Use server-side encryption with AWS KMS (SSE-KMS)

Answer

Use client-side encryption with AWS KMS

Answer

Use server-side encryption with customer-provided keys (SSE-C)

Question 7

A data engineer needs to transform a large dataset stored in Amazon S3 using Apache Spark. The engineer wants to minimize startup time and use a serverless approach. Which AWS service should the engineer use?

Accepted Answer

AWS Glue. AWS Glue provides a serverless Spark environment with fast startup. Option A is wrong because Amazon Redshift is a data warehouse, not a Spark environment. Option B is wrong because Amazon EMR requires cluster provisioning, which increases startup time. Option D is wrong because Amazon Athena is for querying data, not for transforming with Spark.

Answer

Amazon Redshift

Answer

Amazon EMR

Answer

Amazon Athena

Question 8

A data engineer is responsible for a data pipeline that uses Amazon S3 as a data lake, AWS Glue for ETL, and Amazon Athena for ad-hoc queries. The pipeline ingests CSV files from an external partner via SFTP into an S3 bucket. The files are then processed by a Glue job that converts them to Parquet and writes to a separate S3 bucket partitioned by date. The Glue job runs daily and is triggered by a scheduled CloudWatch Events rule. Recently, the data engineer noticed that some days the Glue job fails because of memory errors, and on those days the Athena queries that rely on the data return incomplete results. The engineer needs to ensure that the pipeline is resilient and that Athena queries always see a complete view of the data, even if the Glue job fails mid-run. The engineer also needs to minimize re-processing of data. Which course of action should the engineer take?

Accepted Answer

Modify the Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully.. Writing Parquet output to a temporary location and only copying it into the final partitioned path after the job succeeds ensures Athena never sees partial data from a failed run. Job bookmarks enable incremental processing so already-processed files aren't reprocessed, minimizing re-work. This combination directly addresses both resilience (atomic visibility) and efficiency (no duplicate processing).

Answer

Increase the number of workers and the worker type to G.2X to handle the memory errors, and enable job retries.

Answer

Replace the Glue job with an AWS Lambda function that processes the CSV files and writes Parquet to S3, and use S3 Event Notifications to trigger the function.

Answer

Use Athena partition projection to automatically discover partitions and set up a retry mechanism using AWS Step Functions.

Question 9

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Aurora MySQL. After the migration, the data in Aurora is inconsistent with the source. The engineer needs to ensure ongoing replication with minimal downtime. Which solution should the engineer implement?

Accepted Answer

Configure a DMS task with change data capture (CDC). Configuring a DMS task with Change Data Capture (CDC) enables ongoing replication of changes from the source Oracle database to the target Aurora MySQL database with minimal downtime, ensuring consistency. Option A is incorrect because AWS Schema Conversion Tool (SCT) only converts schema and does not handle data replication. Option B is incorrect because mysqldump provides a one-time export/import, not ongoing replication. Option D is incorrect because performing a full load migration again would disrupt operations and would not capture ongoing changes.

Answer

Use AWS Schema Conversion Tool (SCT) to convert the schema

Answer

Export the data from Oracle and import into Aurora using mysqldump

Answer

Perform a full load migration again

Question 10

A data engineering team notices that an AWS Glue ETL job, which processes hourly data from an S3 bucket, is taking progressively longer to run. The job reads Parquet files partitioned by date and hour. Which action is MOST likely to improve the job's performance?

Accepted Answer

Enable pushdown predicate filtering on the job's data source.. Pushdown predicate filtering allows AWS Glue to push filter conditions down to the data source, so only the relevant partitions and rows are read from S3. Since the data is partitioned by date and hour, predicate pushdown minimizes the amount of data scanned, reducing I/O and improving job performance. This is especially effective for Parquet, which supports columnar pruning and predicate pushdown.

Answer

Convert Parquet files to CSV to improve read performance.

Answer

Increase the number of DPUs for the job.

Answer

Switch from Spark to Python shell for simpler processing.

Question 11

A company runs a time-series forecasting model that writes results to an S3 bucket every 5 minutes. A downstream ETL job reads this data, but sometimes fails because it encounters incomplete files (zero bytes). What is the MOST reliable way to ensure the ETL job only processes complete files?

Accepted Answer

Use S3 Event Notifications to trigger a Lambda function that checks file size and then moves the file to a 'ready' prefix.. S3 Event Notifications can trigger a Lambda function upon object creation, which can check the file size (e.g., > zero bytes) and then copy the file to a 'ready' prefix, ensuring the ETL job only processes complete files. Option A is wrong because an S3 Lifecycle policy can delete small files but does not prevent the ETL from reading incomplete files. Option B is wrong because S3 Copy does not verify completeness. Option C is wrong because S3 Select still reads the file even if it is incomplete; it doesn't guarantee completeness.

Answer

Set an S3 Lifecycle policy to delete files smaller than 1 MB.

Answer

Use S3 Copy to move files to a 'processed' folder after the ETL job reads them.

Answer

Configure S3 Select to query the files and only return rows if the file is complete.

Question 12

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error 'java.lang.OutOfMemoryError: Java heap space'. The job processes a large number of small files in Amazon S3. Which action would MOST effectively resolve the issue?

Accepted Answer

Enable S3 groupFiles option in the Glue job. The 'java.lang.OutOfMemoryError: Java heap space' in AWS Glue when processing many small files is typically caused by the driver or executor accumulating too many file metadata objects. Enabling the S3 groupFiles option (with groupSize and groupFiles parameters) consolidates small files into larger groups, reducing the number of objects processed and alleviating heap pressure. This directly addresses the root cause of the memory issue.

Answer

Change the worker type to G.1X

Answer

Increase the number of workers in the Glue job

Answer

Use a G.2X worker type with more memory

Question 13

A data engineer needs to troubleshoot why an AWS Glue job is failing with a 'Insufficient Memory' error. The job processes a 10 GB dataset. Which step should the engineer take FIRST?

Accepted Answer

Increase the number of DPUs allocated to the job.. The FIRST step when a Glue job fails with 'Insufficient Memory' is to increase the number of DPUs (Data Processing Units) allocated to the job. More DPUs provide more executors and memory, directly addressing the memory constraint. This is the most direct, low-risk remediation before considering code or job-type changes.

Answer

Switch from using Apache Spark to Python shell.

Answer

Repartition the data into more partitions within the job.

Answer

Change the job type from Python to Java.

Question 14

A data engineer needs to set up a disaster recovery solution for an Amazon RDS for MySQL database. The database must be available in another AWS Region with minimal data loss. What is the simplest approach?

Accepted Answer

Create a cross-Region read replica of the database.. A cross-Region read replica of an RDS for MySQL database asynchronously replicates data to another AWS Region, providing a warm standby with minimal data loss (typically seconds of lag) and the ability to promote the replica during a regional failure. It is the simplest native RDS feature for cross-Region DR without building custom replication pipelines.

Answer

Enable Multi-AZ deployment in the same Region.

Answer

Set up AWS Database Migration Service (DMS) for continuous replication.

Answer

Take a manual snapshot and copy it to the other Region daily.

Question 15

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed in real-time using a custom Lambda function. Which TWO steps are required to enable this? (Choose TWO)

Accepted Answer

Configure Kinesis Data Firehose to use a Lambda function for data transformation. Option A is correct because enabling real-time transformation in Kinesis Data Firehose requires configuring the delivery stream to invoke a Lambda function as its processing step, which Firehose then calls synchronously for each batch of records. Option B is correct because the Lambda function must return the transformed records in the exact format Firehose expects — a JSON object containing the processed records with fields such as recordId, result (Ok, Dropped, or ProcessingFailed), and data (base64-encoded) — otherwise Firehose cannot continue delivery. Option C is wrong because Kinesis Data Analytics is a separate service for SQL/Flink stream analytics, not the mechanism Firehose uses for Lambda-based transformation. Option D is wrong because Firehose does not execute inline transformation logic; it only supports invoking a Lambda function for that purpose. Option E is wrong because using Kinesis Data Streams as a source is unrelated to enabling Lambda transformation and is not a required step.

Answer

Create a Kinesis Data Analytics application to transform the data

Answer

Write the transformation logic directly in the Firehose delivery stream configuration

Answer

Use Kinesis Data Streams as the source for Firehose