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 data science team uses Amazon SageMaker to train models on a large dataset stored in S3. The dataset is 500 GB in CSV format and is updated daily. The team wants to optimize data loading for training jobs to reduce I/O wait time. Which data ingestion strategy is MOST effective?
Explanation: Option B is correct because SageMaker Pipe input mode streams data directly from S3 to the training algorithm without writing to the instance's EBS volume, eliminating disk I/O bottlenecks. This is especially effective for large datasets (500 GB) that are updated daily, as it reduces startup time and avoids the need to download the entire dataset before training begins.
A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data from a website. The data is consumed by a Lambda function that writes records to an S3 bucket. Recently, the number of shards was increased from 2 to 4 to handle higher throughput. After the change, the Lambda function started processing records with increased latency and some records were being written out of order. What is the MOST likely cause?
Explanation: Option D is correct because after resharding from 2 to 4 shards, the mapping of partition keys to shards changes. If the producer does not use a partition key that ensures related records (e.g., same user session) are routed to the same shard, records that were previously ordered within a shard may now be split across multiple shards. Since the Lambda consumer processes shards independently, records from the same logical sequence can arrive out of order, and the increased shard count can also cause higher latency if the consumer is not properly parallelized.
A data engineer needs to transform large CSV files stored in S3 into Parquet format and load them into a data warehouse for analysis. The transformation must be cost-effective and serverless. Which AWS service should be used?
Explanation: AWS Glue is the correct choice because it provides a fully managed, serverless ETL service that can automatically convert CSV files from S3 into Parquet format using its built-in Spark engine. It is cost-effective as you only pay for the resources consumed during the job execution, and it integrates directly with data warehouses like Amazon Redshift for loading transformed data.
A company uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be partitioned by year, month, and day. The delivery stream is configured with a buffer interval of 60 seconds and buffer size of 5 MB. The data producer sends about 1 MB per second. The data is arriving in S3 but the partitions are not being created as expected. What is the MOST likely reason?
Explanation: Option B is correct because Kinesis Data Firehose requires dynamic partitioning to be explicitly enabled and configured with a custom prefix (e.g., 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/') to automatically partition data by year, month, and day. Without this setting, Firehose writes all data to a single S3 prefix, ignoring the desired partition structure.
An ML team is building a recommendation system. The training data includes user-item interactions stored in Amazon DynamoDB. The team wants to export this data to S3 in Parquet format for use with Amazon SageMaker. The export should be incremental (only new or changed records) and run daily. Which approach meets these requirements with MINIMAL operational overhead?
Explanation: Option B is correct because DynamoDB Streams capture every change (insert, update, delete) in near real-time, and AWS Lambda can process these events to write only the changed records to S3 in Parquet format. This approach provides incremental, daily exports with minimal operational overhead, as it is fully serverless and requires no infrastructure management.
+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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