MLS-C01 Data Engineering Practice Test 21 — 15 Questions

Question 1

A data engineer needs to store streaming data from thousands of IoT devices for real-time analytics. Which AWS service is most suitable for ingesting and storing this data for subsequent processing by Amazon Kinesis Data Analytics?

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

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the most suitable service for ingesting and storing streaming data from thousands of IoT devices because it is designed for real-time data ingestion, can handle high throughput from many sources, and integrates natively with Amazon Kinesis Data Analytics for real-time analytics without needing additional storage layers. Its shard-based architecture allows for scalable, durable storage of streaming records for up to 365 days, enabling immediate processing by Kinesis Data Analytics.

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Amazon S3

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Amazon RDS

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Amazon DynamoDB

Question 2

A company is using AWS Glue to run ETL jobs that process data in an S3 data lake. The jobs are failing with out-of-memory errors when processing large files. Which configuration change should be made to resolve this issue?

Accepted Answer

Increase the number of DPUs allocated to the job. Out-of-memory errors in AWS Glue ETL jobs indicate that the allocated memory (DPUs) is insufficient for the data being processed. Increasing the number of DPUs allocates more memory and compute capacity to the job, directly resolving the memory constraint. This is the standard approach for scaling Glue jobs handling large datasets.

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Change the worker type to G.1X

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Partition the input data into smaller files

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Enable job bookmark to process only new data

Question 3

A data scientist is training a deep learning model on a GPU instance. The training data is stored in S3 and is 50 GB. To reduce I/O bottlenecks, which storage option should be used to cache the data locally on the instance?

Accepted Answer

Mount an Amazon FSx for Lustre file system linked to the S3 bucket. Amazon FSx for Lustre is a high-performance file system designed for HPC and machine learning workloads. By linking it to the S3 bucket, it automatically caches data locally on the Lustre file system attached to the GPU instance, providing low-latency access and reducing I/O bottlenecks. Option A is incorrect because Amazon EFS is a shared file system with lower throughput compared to Lustre, and it is not optimized for the high throughput needed for deep learning training. Option C is incorrect because while EBS io2 volumes provide high IOPS, copying data using AWS DataSync introduces an extra step and does not provide the seamless caching and high aggregate throughput that FSx for Lustre offers. Option D is incorrect because instance store volumes are ephemeral and not persistent; they would require re-copying the data each time the instance is stopped, and they lack the integration with S3 that FSx for Lustre provides.

Answer

Attach an Amazon EFS file system to the instance and copy data from S3

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Provision an Amazon EBS io2 volume and copy data from S3 using AWS DataSync

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Use instance store volumes to cache the data from S3

Question 4

A company is using Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be transformed into Parquet format before delivery. Which approach should the data engineer use?

Accepted Answer

Configure Kinesis Data Firehose to convert the record format to Parquet using a schema from AWS Glue Data Catalog. Amazon Kinesis Data Firehose can directly convert incoming JSON records to Parquet format by referencing a schema stored in the AWS Glue Data Catalog. This is a built-in feature of Firehose that does not require additional services for the conversion. Option A is wrong because Kinesis Data Analytics is for real-time analytics, not format conversion. Option C is wrong because while Lambda can transform data, using it for Parquet conversion adds latency and complexity; Firehose's native conversion is simpler. Option D is wrong because an AWS Glue ETL job is for batch processing, not real-time streaming transformation.

Answer

Send the data to Amazon Kinesis Data Analytics to convert to Parquet

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Use an AWS Lambda function to transform JSON to Parquet and write to S3

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Use an AWS Glue ETL job to read from Firehose and write Parquet to S3

Question 5

A company is running a data pipeline that uses Amazon EMR with Spark to process 100 TB of data daily. The pipeline must complete within 6 hours. Currently, it takes 8 hours. Which optimization will most likely reduce the runtime?

Accepted Answer

Enable EMR Managed Scaling. EMR Managed Scaling automatically adjusts the number of core and task nodes based on workload, increasing parallelism and reducing runtime for large data pipelines. This is the most effective optimization when the current cluster is under-provisioned for the 100 TB workload. Option A is incorrect because consolidating input files reduces small file overhead but does not address a compute capacity bottleneck. Option C is incorrect because increasing memory per node (e.g., r5 instances) helps memory-bound tasks, but the primary bottleneck here is likely throughput and parallelism, not memory. Option D is incorrect because Spot Instances can be interrupted, which may cause delays and increase runtime, making them unsuitable for a time-sensitive pipeline.

Answer

Consolidate small input files into fewer larger files

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Increase the memory of each node by using r5 instances

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Use Spot Instances for all core nodes

Question 6

A data engineer needs to schedule an AWS Glue ETL job to run every hour. Which service should be used to trigger the job?

Accepted Answer

Amazon CloudWatch Events (EventBridge). Amazon CloudWatch Events (now part of Amazon EventBridge) is the correct service for scheduling AWS Glue ETL jobs on a recurring basis, such as every hour. It allows you to create a time-based rule using a cron or rate expression that triggers an AWS Glue job directly as a target, without needing additional compute or orchestration logic.

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AWS Lambda

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AWS Step Functions

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AWS Data Pipeline

Question 7

A company is using Amazon Athena to query a data lake in S3. Queries are slow and expensive. The data is stored as JSON. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format. Converting JSON data to Parquet format significantly improves Athena query performance and reduces cost. Parquet is a columnar storage format that allows Athena to scan only the columns needed for a query, drastically reducing the amount of data read from S3. This minimizes I/O and compute costs, as Athena charges based on the amount of data scanned. In contrast, JSON is row-based and requires scanning entire files even for queries that only touch a few columns.

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Compress the JSON files using gzip

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Partition the data by date

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Increase the number of Athena workers

Question 8

A data engineer is building a data pipeline that uses Amazon S3 to store raw data, AWS Lambda for transformation, and Amazon DynamoDB for serving. The Lambda function experiences high latency when writing to DynamoDB. Which action will most effectively reduce the latency?

Accepted Answer

Increase the DynamoDB write capacity units. Increasing write capacity units for DynamoDB directly reduces write latency by minimizing throttling. DAX (Option A) is a read cache and does not improve write latency. Option B is incorrect because using S3 would increase latency due to its different access pattern. Option C is incorrect because VPC gateway endpoint improves network connectivity but does not reduce write latency.

Answer

Enable DynamoDB Accelerator (DAX) for caching

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Use Amazon S3 instead of DynamoDB

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Configure a VPC gateway endpoint for DynamoDB

Question 9

A company needs to move 10 TB of data from an on-premises NAS to Amazon S3 over a 100 Mbps internet connection. The transfer must complete within 3 days. Which solution is the most appropriate?

Accepted Answer

Use AWS Snowball Edge to transfer the data. AWS Snowball Edge is a physical device that can transfer large data volumes much faster than over the internet. Option A is wrong: AWS DataSync over the internet at 100 Mbps would take approximately 10 days to transfer 10 TB, exceeding the 3-day requirement. Option B is wrong: S3 Transfer Acceleration optimizes the network path but still relies on internet bandwidth; even with a 200% speed improvement, it would take over 4.6 days. Option C is wrong: using AWS CLI to copy directly over the internet has the same bandwidth limitation and would also take about 10 days.

Answer

Use AWS DataSync to transfer over the internet

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Enable S3 Transfer Acceleration on the bucket

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Use AWS CLI to copy data directly over the internet

Question 10

A data engineering team is designing a data lake on AWS. They need to store raw data in S3 and allow multiple analytics services to query the data. Which service can be used to catalog and provide schema information for the data?

Accepted Answer

AWS Glue Data Catalog. AWS Glue Data Catalog is a fully managed metadata repository that stores table definitions, schema information, and partition details for data in S3. Amazon Athena, while it can query data in S3 using SQL, does not provide its own catalog; it relies on the Glue Data Catalog for schema information. Therefore, only AWS Glue Data Catalog directly catalogs and provides schema information.

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Amazon Kinesis Data Streams

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Amazon RDS

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Amazon DynamoDB

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Amazon Athena

Question 11

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be processed and stored in S3 in near real-time. Which THREE services can be used together to achieve this?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is correct because it can process streaming clickstream data in real-time using SQL or Apache Flink, enabling transformations, aggregations, and filtering before the data is delivered downstream. It integrates directly with Kinesis Data Streams as a source and can output processed records to Kinesis Data Firehose for storage in Amazon S3, achieving near real-time processing and storage.

Answer

AWS Glue ETL

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Amazon EMR

Question 12

A data engineer is designing a data pipeline that uses Amazon S3 events to trigger an AWS Lambda function for processing. The pipeline must handle high throughput with low latency. Which TWO configurations should be applied?

Accepted Answer

Configure Lambda with reserved concurrency. Reserved concurrency ensures that the Lambda function always has a guaranteed number of concurrent executions available, preventing it from being throttled by other functions in the same AWS account. This is critical for high-throughput, low-latency pipelines because S3 event notifications can burst many invocations simultaneously, and without reserved concurrency, the function might hit the account-level concurrency limit and drop events.

Answer

Use an SQS queue between S3 and Lambda

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Place Lambda in a VPC to reduce network latency

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Use Amazon Kinesis Data Streams as an intermediary

Question 13

A data engineer is configuring an IAM policy to allow users to upload objects to an S3 bucket only if the objects are encrypted using SSE-S3. However, users are getting AccessDenied errors when uploading objects without specifying encryption. What is the most likely cause?

Accepted Answer

The condition requires encryption to be specified, but the upload does not specify it. The policy allows PutObject only when encryption is AES256, but denies when no encryption is specified because the condition is not met. Option A is wrong because it's not a service control policy; Option C is wrong because the bucket policy is not shown; Option D is wrong because the condition checks for AES256, not KMS.

Answer

The condition should check for aws:SourceIp instead of encryption

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The policy is attached to the wrong IAM user

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The bucket policy denies all PutObject without encryption

Question 14

An AWS Glue ETL job failed with the error 'Insufficient memory allocated for the job'. The job run details show AllocatedCapacity: 5, WorkerType: Standard, NumberOfWorkers: 5. Which change should be made to resolve the issue?

Accepted Answer

Increase the number of workers to 10. The error indicates insufficient memory for the job. Both increasing the number of workers (Option C) and changing the worker type to G.2X (Option D) increase the total allocated memory. Option C scales out by adding more Standard workers, each with 16 GB memory and 1 DPU. Option D scales up by switching to G.2X workers, each with 32 GB memory and 2 DPUs, effectively doubling the DPU and total memory for the same number of workers. Either change can resolve the insufficient memory error. Option A is incorrect because deleting and recreating the job does not change resource allocation. Option B is incorrect because job timeout does not affect memory.

Answer

Delete and recreate the job with a different name

Answer

Increase the job timeout to 3600 minutes

Question 15

An S3 event notification triggers an AWS Lambda function when a new object is created. The Lambda function parses the event and processes the object. The function is failing with a timeout error for large objects. Which approach should be used to handle large objects efficiently?

Accepted Answer

Increase the Lambda function timeout to 15 minutes. Increasing the Lambda function timeout to the maximum of 15 minutes directly addresses the timeout error for large objects. This allows the function to complete processing within the allotted time. Option B (SQS queue) does not solve the timeout issue because the processing time itself is too long; buffering events does not reduce processing time. Option C (Kinesis) adds unnecessary streaming complexity. Option D (Step Functions) adds orchestration overhead without addressing the timeout.

Answer

Use an SQS queue to buffer event notifications and configure Lambda with a batch window

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Stream events to Amazon Kinesis Data Streams and process with Lambda

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Use AWS Step Functions to orchestrate the processing