DEA-C01 Data Ingestion and Transformation — 10 Questions

Question 1

A 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?

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

Accepted Answer

Increase the Batch Window (MaximumBatchingWindowInSeconds) for the event source mapping.. 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.

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Increase the number of shards in the Kinesis stream.

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Increase the Parallelization Factor for the Lambda event source mapping.

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Increase the memory allocated to the Lambda function.

Question 2

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?

Accepted Answer

Implement retries with exponential backoff in the Lambda function for S3 put operations.. 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.

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Use S3 Batch Operations to write data in batches.

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Increase the number of shards in the Kinesis data stream.

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Enable S3 Transfer Acceleration on the destination bucket.

Question 3

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?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. 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.

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Convert the CSV files to JSON format before processing.

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Decrease the Spark shuffle partitions in the job script.

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Increase the job timeout setting.

Question 4

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?

Accepted Answer

Increase the checkpointing interval.. 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.

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Disable checkpointing to avoid failures.

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Switch the state backend from in-memory to RocksDB.

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Increase the parallelism of the application.

Question 5

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.)

Accepted Answer

Use column pruning to read only necessary columns in the Glue script.. 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.

Answer

Increase the Spark shuffle partitions to 500.

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Write the output as JSON instead of Parquet to avoid compression overhead.

Question 6

A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job reads JSON records and writes Parquet to Amazon S3. Recently, the job started failing with 'Out of Memory' errors. Which change is MOST likely to resolve the issue?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. The 'Out of Memory' error in AWS Glue indicates that the job's allocated resources are insufficient for the data volume or processing complexity. Increasing the number of DPUs (Data Processing Units) directly increases the available memory and compute capacity, which is the most straightforward fix for OOM errors in Glue streaming jobs. Option C is correct because it addresses the root cause—resource exhaustion—by scaling the job horizontally.

Answer

Enable compression on the Kinesis stream.

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Change the output format from Parquet to ORC.

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Reduce the streaming batch size in the Glue job configuration.

Question 7

A data engineer is designing a data ingestion pipeline for IoT sensor data. The data arrives as JSON via AWS IoT Core, and must be stored in Amazon S3 in partitioned Parquet format. The pipeline must handle late-arriving data (up to 1 hour) and ensure exactly-once processing. Which combination of services should the engineer use?

Accepted Answer

Amazon Kinesis Data Firehose with data transformation via AWS Lambda, delivering to Amazon S3.. Amazon Kinesis Data Firehose is the correct choice because it can directly ingest streaming data from AWS IoT Core, use a built-in AWS Lambda function to transform JSON to Parquet, and deliver the data to Amazon S3 with automatic partitioning. It also supports buffering and retry logic to handle late-arriving data (up to 1 hour) and provides exactly-once delivery to S3 when configured with the appropriate error handling and idempotent transformations.

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Amazon Kinesis Data Streams with AWS Lambda for transformation and Amazon S3.

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Amazon Simple Queue Service (SQS) with AWS Lambda for transformation and Amazon S3.

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AWS Glue streaming jobs consuming from Amazon Kinesis Data Streams and writing to Amazon S3.

Question 8

A company has a Glue ETL job that reads from an Amazon RDS for MySQL table and writes to Amazon S3. The job runs hourly and processes new records based on a 'last_modified' timestamp column. Recently, the job started missing some records because the timestamp in MySQL is stored with microsecond precision but Glue's job bookmark only tracks second precision. Which solution addresses this issue?

Accepted Answer

Use a job parameter to store the last processed timestamp with millisecond precision and query records greater than that value.. AWS Glue job bookmarks track timestamps with only second precision, so records with microsecond differences within the same second are missed. By using a custom job parameter to store the last processed timestamp with millisecond precision and querying records greater than that value, you bypass Glue's bookmark limitation and capture all new or modified records.

Answer

Increase the job frequency to every 30 minutes.

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Run a full refresh of the table each time instead of incremental.

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Modify the MySQL table to use a DATE data type instead of TIMESTAMP.

Question 9

A data engineer is ingesting CSV files from an Amazon S3 bucket into a Glue Data Catalog table. The files have headers, but some files have extra columns not present in the first file. The engineer wants the Glue crawler to automatically detect the schema. Which crawler configuration option should be used?

Accepted Answer

Configure the crawler to 'Create a single schema for each S3 path' and enable 'Merge tables'.. When CSV files have varying schemas (extra columns), the Glue crawler must be configured to 'Create a single schema for each S3 path' with 'Merge tables' enabled. This configuration instructs the crawler to union the schemas from all files in the S3 path, adding new columns as they appear, rather than creating separate tables for each schema variation.

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Configure the crawler to 'Inherit schema from table' and set the table name.

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Configure the crawler to 'Create a single schema for each S3 path' without enabling 'Merge tables'.

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Configure the crawler to 'Create a single schema for each S3 path' and set 'Each file as a separate table'.

Question 10

A company uses AWS Glue to process CSV files from an S3 bucket. The job fails intermittently with a 'SchemaDetectionError' for files that have inconsistent column counts. What is the most efficient way to handle this?

Accepted Answer

Define a fixed schema in the Glue job using 'apply_mapping' to map columns.. Defining a fixed schema using the `schema` parameter in the DynamicFrame reader forces Glue to apply that schema to all CSV files. With `apply_mapping`, you can map the actual columns present to the fixed schema, handling inconsistent column counts by ignoring extra columns and filling missing columns with nulls. This avoids schema detection errors without extra preprocessing.

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Use the 'mergeSchema' option when reading the DynamicFrame.

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Convert all CSV files to Parquet format using a separate preprocessing job.

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Set the job to 'ignore' schema mismatches in the job parameters.