MLS-C01 Data Engineering Practice Test 11 — 15 Questions

Question 1

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. They notice that the data is delivered in 5-minute intervals even though they set the buffer interval to 60 seconds. What could be the cause?

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

Accepted Answer

The buffer size is set to a value larger than the incoming data rate.. B is correct because Kinesis Data Firehose delivers data based on whichever condition is met first: the buffer interval (60 seconds) or the buffer size (e.g., 5 MB). If the incoming data rate is very low, the buffer size threshold may never be reached within 60 seconds, causing Firehose to wait longer—up to the maximum buffer interval of 900 seconds—before delivering. In this case, the data rate is so low that it takes 5 minutes to fill the buffer, overriding the 60-second interval setting.

Answer

The source Kinesis stream has insufficient shards.

Answer

The S3 bucket is in a different region.

Answer

The IAM role does not have permission to write to S3.

Question 2

A company needs to process sensitive data from multiple sources. They want to use AWS Glue to catalog and transform the data. Which feature should they use to ensure that sensitive columns are masked before the data is available for querying?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is a visual data preparation tool that provides built-in transformations for masking, encrypting, or removing sensitive data. It allows you to create recipes that apply these transformations to specific columns, ensuring that sensitive information is obfuscated before the data is cataloged and made available for querying. DataBrew integrates with AWS Glue Data Catalog, so the masked data can be used downstream without exposing sensitive values.

Answer

AWS Glue Studio

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AWS Lake Formation

Answer

Amazon Macie

Question 3

A company runs a critical ETL job using AWS Glue that writes to an Amazon Redshift cluster. The job occasionally fails due to insufficient disk space on the Redshift cluster. How can the company automate the process to prevent this failure?

Accepted Answer

Use a CloudWatch alarm to trigger a Lambda function that resizes the cluster.. A CloudWatch alarm monitoring the Redshift cluster's DiskSpaceUsed metric can trigger a Lambda function that programmatically resizes the cluster (via the Redshift ModifyCluster API) when free disk space drops below a threshold. This automates remediation before the ETL job fails, directly addressing the root cause (insufficient disk space) without manual intervention.

Answer

Use RA3 node types with managed storage.

Answer

Increase the number of slices in the Redshift cluster.

Answer

Reserve additional nodes for the Redshift cluster.

Question 4

Which TWO AWS services are suitable for real-time stream processing?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics (option D) is correct because it is purpose-built for real-time stream processing, allowing you to run SQL or Apache Flink applications continuously over streaming data from sources like Kinesis Data Streams and Amazon MSK with sub-second latency. AWS Lambda (option E) is correct because it can process streaming events in real time by being invoked as an event source mapping consumer for Kinesis Data Streams or DynamoDB Streams, executing code per batch of records as they arrive. Amazon Athena (option A) is an interactive query service for analyzing data in S3 with standard SQL, which is batch-oriented rather than real-time stream processing. AWS Glue (option B) is a serverless ETL service primarily used for batch data integration and cataloging, not continuous stream processing. Amazon EMR (option C) is a managed big-data platform for batch processing frameworks like Hadoop and Spark, and while Spark Streaming can run on it, it is not the primary purpose-built real-time stream processing service in this context.

Answer

Amazon Athena

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AWS Glue

Answer

Amazon EMR

Question 5

An IAM policy attached to an AWS Glue job allows reading and writing to an S3 bucket and accessing Glue Data Catalog. The job fails with an access denied error when trying to create a table in the Data Catalog. What is the likely issue?

Accepted Answer

The policy does not include the glue:CreateTable action.. The error occurs specifically when creating a table in the Glue Data Catalog, which is a control-plane operation requiring the glue:CreateTable IAM action. Even though the policy grants access to the Data Catalog, it likely omits this specific action or uses a wildcard that doesn't cover it. IAM policies are deny-by-default, so any action not explicitly allowed is implicitly denied, causing the AccessDeniedException.

Answer

The Glue Data Catalog is not enabled for the account.

Answer

The job does not have permission to write to the S3 bucket.

Answer

The S3 bucket is encrypted with a KMS key that the job cannot access.

Question 6

A data engineer runs the AWS CLI command shown and notices a zero-byte file in the results. What is the most likely cause of this zero-byte file?

Accepted Answer

The file was created by a failed Spark task that wrote no data.. A zero-byte file in S3 output from a Spark job is most likely caused by a failed or empty Spark task that wrote no data but still created the file object. Spark writes one file per partition, and if a task produces no records or fails after creating the file, a zero-byte object remains.

Answer

The S3 bucket has a lifecycle policy that deleted the content.

Answer

The file was written with the wrong prefix.

Answer

The file was compressed, reducing size to zero.

Question 7

A company uses AWS Glue jobs with job bookmarks enabled to process incremental data. They notice that the job processes all data each time instead of only new data. What is the most likely reason?

Accepted Answer

The MaxConcurrentRuns is set to 3, which can cause bookmark conflicts.. Setting MaxConcurrentRuns to a value greater than 1 can cause job bookmark conflicts. When multiple concurrent runs of the same AWS Glue job attempt to update the bookmark state simultaneously, they can overwrite each other's progress, leading to inconsistent bookmark tracking. This results in the job reprocessing all data instead of only incremental data, as the bookmark fails to correctly record the last processed position.

Answer

The TempDir is not configured correctly.

Answer

The job bookmark option is set to 'job-bookmark-enable' but should be 'job-bookmark-disable'.

Answer

The source data does not have a column that can be used as a bookmark key.

Question 8

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?

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.

Answer

Amazon S3

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Amazon RDS

Answer

Amazon DynamoDB

Question 9

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.

Answer

Change the worker type to G.1X

Answer

Partition the input data into smaller files

Answer

Enable job bookmark to process only new data

Question 10

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 cluster capacity based on workload metrics (YARN memory, HDFS utilization), adding task nodes when Spark stages need more parallelism and removing them when idle. For a job that must finish in 6 hours but currently takes 8, dynamic scaling of task nodes is the most direct lever to increase throughput without manual tuning.

Answer

Consolidate small input files into fewer larger files

Answer

Increase the memory of each node by using r5 instances

Answer

Use Spot Instances for all core nodes

Question 11

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 DynamoDB write capacity units (WCUs) directly addresses throttling-induced latency by provisioning more write throughput. If the table is under-provisioned, Lambda writes experience throttling and retries, causing high latency. Scaling WCUs (or switching to on-demand) removes this bottleneck most effectively.

Answer

Enable DynamoDB Accelerator (DAX) for caching

Answer

Use Amazon S3 instead of DynamoDB

Answer

Configure a VPC gateway endpoint for DynamoDB

Question 12

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 (A) is correct because it can consume records directly from a Kinesis Data Stream, run SQL or Apache Flink applications to transform and analyze the clickstream data in near real-time, and then write the processed output to a destination such as Kinesis Data Firehose. Amazon Kinesis Data Firehose (B) is correct because it is the managed delivery service that reliably loads streaming data into Amazon S3 with configurable buffering (for example, 60–900 seconds or 1–128 MB), providing the near real-time storage layer required by the scenario. AWS Lambda (D) is correct because it can be invoked by the Kinesis Data Stream as an event source, process each batch of clickstream records, and then either write results to S3 directly or forward them to Firehose for delivery. AWS Glue ETL (C) is not appropriate here because it is a batch-oriented Apache Spark ETL service that runs on schedules or triggers, not a continuous stream processor for near real-time ingestion. Amazon EMR (E) is also not the intended answer because, while Spark Streaming or Flink can run on EMR, it is a self-managed big-data cluster platform rather than a purpose-built serverless streaming and delivery combination for this use case.

Answer

AWS Glue ETL

Answer

Amazon EMR

Question 13

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. Option A is correct because configuring reserved concurrency on the Lambda function guarantees a dedicated pool of concurrent executions, preventing throttling from other functions and ensuring the pipeline can sustain high throughput with predictable low latency. Option E is correct because S3 Event Notifications can invoke Lambda directly (via the s3:ObjectCreated:* event), which is the native, lowest-latency, serverless trigger path for reacting to new objects without any intermediary hop. Option B is not appropriate here because inserting an SQS queue adds polling delay and buffering, increasing latency rather than minimizing it. Option C is not appropriate because placing Lambda in a VPC adds ENI attachment and cold-start overhead and does not reduce latency for S3 event processing. Option D is not appropriate because Kinesis Data Streams introduces an extra intermediary with its own latency and management overhead, which is unnecessary when direct S3-to-Lambda invocation meets the requirement.

Answer

Use an SQS queue between S3 and Lambda

Answer

Place Lambda in a VPC to reduce network latency

Answer

Use Amazon Kinesis Data Streams as an intermediary

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 AWS Glue job. With AllocatedCapacity: 5 and WorkerType: Standard, the job is using 5 standard workers. To resolve, you can increase the number of workers (Option C) or change to a larger worker type like G.2X (Option D), which has more memory and compute. Both are valid solutions. Increasing timeout (Option B) does not address memory. Deleting and recreating (Option A) is unnecessary. Since the question asks which change should be made, and both C and D are correct, but typically in such questions, multiple answers may be correct; however, the prompt marks both C and D as [CORRECT]. So both are valid.

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

Answer

Stream events to Amazon Kinesis Data Streams and process with Lambda

Answer

Use AWS Step Functions to orchestrate the processing