20+ practice questions focused on Data Engineering — one of the most tested topics on the AWS Certified Machine Learning Specialty MLS-C01 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Data Engineering PracticeA machine learning team needs to create a training dataset by joining two large datasets (10 TB and 5 TB) stored in S3. The join key is 'user_id'. They want to minimize data movement and cost. Which approach should they use?
Explanation: AWS Glue ETL provides a serverless Spark environment that can read both datasets from S3, perform the join in a distributed manner, and write the result back to S3. This approach minimizes data movement because Glue reads directly from S3 and writes back to S3 without requiring intermediate storage or cluster management. It is cost-effective due to per-second billing and is optimized for large-scale ETL workloads like joining 10 TB and 5 TB datasets. Amazon Athena would scan 15 TB of data, incurring high costs and potential performance issues, while EMR and Redshift require provisioning and managing clusters, leading to higher operational overhead and costs.
A data pipeline uses Amazon Kinesis Data Streams with a Lambda consumer to process clickstream data. The Lambda function sometimes times out because of spikes in traffic. The team wants to buffer the data before processing to handle spikes. Which approach is most effective?
Explanation: Amazon SQS can act as a buffer to handle traffic spikes. By sending data from Kinesis Data Streams to an SQS queue, Lambda can poll from the queue in batches, smoothing out the load and preventing timeouts. This decouples the ingestion from processing, allowing Lambda to process data at its own pace.
A company runs a nightly AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes to Amazon S3. The job fails intermittently with 'ERROR: cannot execute INSERT in a read-only transaction'. What is the most likely cause?
Explanation: This error occurs when the Glue job tries to write to a Redshift table that is in a read-only transaction, often because the Redshift cluster is in a read-only state due to a failover or maintenance. Option A (IAM permissions) would result in an access denied error. Option B (JDBC driver version) would typically cause a connection or compatibility error. Option D (connection pool exhaustion) would produce a different error related to connection limits.
A data engineer is tasked with building a system to process a continuous stream of IoT sensor data. The data must be processed in near real-time, and the results must be stored in Amazon S3 partitioned by hour. Which AWS service is the most cost-effective and simplest to implement?
Explanation: Amazon Kinesis Data Firehose is the simplest and most cost-effective way to ingest streaming data and deliver it to S3 with automatic partitioning by time. Option A (Amazon SQS with AWS Lambda) is for message queuing and decoupling, not for streaming data to S3 with partitioning. Option C (Amazon Kinesis Data Streams with EC2 consumers) requires custom consumers and more management overhead. Option D (AWS DMS) is for database migration, not streaming.
A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are currently failing due to insufficient memory. The data volume varies, with occasional spikes. Which solution should be used to handle the variable memory requirements efficiently?
Explanation: To handle variable memory requirements efficiently, increasing the number of DPUs for all jobs ensures sufficient memory during spikes. AWS Glue Flex execution uses spare capacity and can be preempted, so it does not guarantee resource availability and is not suitable for handling memory insufficiencies. Option C provides a direct way to allocate more resources, preventing job failures due to insufficient memory.
+15 more Data Engineering questions available
Practice all Data Engineering questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Data Engineering. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Data Engineering questions on the MLS-C01 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Data Engineering is tested as part of the AWS Certified Machine Learning Specialty MLS-C01 blueprint. Practicing with targeted Data Engineering questions ensures you can handle any format or difficulty that appears.
Yes. Courseiva provides free MLS-C01 practice questions across all exam topics and domains. The platform includes topic-based practice, mock exams, missed-question review, bookmarked questions, and readiness tracking — no account required.
Difficulty is subjective, but Data Engineering is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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