20+ practice questions focused on Data Ingestion and Transformation — one of the most tested topics on the AWS Certified Data Engineer Associate DEA-C01 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Data Ingestion and Transformation PracticeA company uses AWS Lambda to process records from an Amazon Kinesis Data Stream. Each record is about 50 KB. The Lambda function transforms the data and writes to Amazon DynamoDB. Recently, the Lambda function has been experiencing throttling and high error rates. The Kinesis stream has 10 shards. What is the most cost-effective solution to improve processing throughput?
Explanation: Increasing the Batch Window (MaximumBatchingWindowInSeconds) allows the Lambda function to accumulate more records from the Kinesis stream before invoking the function, reducing the number of invocations and thus lowering the chance of throttling. This is the most cost-effective solution as it does not require additional shards, memory, or parallelization, and directly addresses the high error rates caused by excessive concurrent executions.
A data engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that transforms each record and writes it to Amazon S3. Recently, the Lambda function started failing with 'ProvisionedThroughputExceededException' errors when writing to S3. The team has already increased the Lambda function's memory and timeout. Which action should the team take to resolve the issue?
Explanation: The 'ProvisionedThroughputExceededException' error indicates that the Lambda function is being throttled by S3 due to exceeding the bucket's request rate limits. Implementing retries with exponential backoff in the Lambda function for S3 put operations is the correct solution because it allows the function to gracefully handle transient throttling errors by waiting progressively longer between retries, which aligns with AWS's guidance for managing S3 request rate limits.
A company is using AWS Glue to process data from Amazon S3. The Glue job reads CSV files and writes Parquet files to a different S3 bucket. The job occasionally fails with 'java.lang.OutOfMemoryError: Java heap space'. The data size varies. Which change should the engineer make to avoid this error?
Explanation: The 'java.lang.OutOfMemoryError: Java heap space' error in AWS Glue indicates that the Spark executors ran out of memory while processing the data. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory available across the cluster, allowing larger datasets to be processed without hitting the heap limit. Each DPU provides 4 vCPUs and 16 GB of memory, so adding more DPUs scales memory linearly.
A data engineering team uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. They notice that the application's checkpointing is failing intermittently, causing data reprocessing. The application uses a large state. Which configuration change should the team make to improve checkpoint reliability?
Explanation: Increasing the checkpointing interval reduces the frequency of checkpoint operations, giving the system more time to complete each checkpoint before the next one starts. This alleviates backpressure and resource contention, which is critical when dealing with large state, as checkpointing large state is I/O and CPU intensive and can fail if intervals are too tight.
A company uses AWS Glue to perform ETL on data stored in Amazon S3. The Glue job reads CSV files, converts them to Parquet, and partitions by date. The job runs daily and processes about 500 GB of data. The team wants to optimize costs and performance. Which three actions should the team take? (Select THREE.)
Explanation: Option B is correct because column pruning (selecting only the needed columns in the Glue/Spark script) reduces the amount of data read from S3 and processed during the shuffle, lowering I/O and DPU cost for a 500 GB daily job. Option C is correct because G.1X and G.2X worker types are the current AWS Glue worker families with more memory and compute per worker than the legacy Standard type, improving Spark performance and cost-efficiency for large ETL workloads. Option D is correct because increasing the number of DPUs (i.e., adding workers) parallelizes the Spark job, which speeds up processing of the large CSV-to-Parquet conversion and can reduce total runtime and cost when the job is not already optimally parallelized. Option A is not correct because blindly setting Spark shuffle partitions to 500 is not a general best practice; the value should be tuned to data size and cluster parallelism (often 2-3x the number of cores), and a fixed 500 may cause excessive small tasks or overhead. Option E is not correct because writing JSON instead of Parquet would increase storage size and read cost, since Parquet is columnar and compressed, and JSON does not avoid compression overhead in a beneficial way.
+15 more Data Ingestion and Transformation questions available
Practice all Data Ingestion and Transformation questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Data Ingestion and Transformation. 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 Ingestion and Transformation questions on the DEA-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 Ingestion and Transformation is tested as part of the AWS Certified Data Engineer Associate DEA-C01 blueprint. Practicing with targeted Data Ingestion and Transformation questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Data Ingestion and Transformation 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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