MLS-C01 Data Engineering Practice Test 22 — 15 Questions

Question 1

A data engineer runs the AWS CLI command shown in the exhibit to find large log files in S3. The command returns an empty list, but the engineer knows there are files larger than 1 MB in that prefix. What is the MOST likely issue?

Accepted Answer

The command does not paginate through all objects; only the first 1000 are returned. Option A is correct. The list-objects command returns up to 1000 objects per call. If there are more objects, pagination is needed. Option B is incorrect because the query syntax is correct. Option C is incorrect because the prefix is fine. Option D is incorrect because the unit is bytes, so 1000000 is 1 MB.

Answer

The JMESPath query syntax is incorrect

Answer

The prefix is incorrect; there are no objects under that prefix

Answer

The Size value is in kilobytes, not bytes

Question 2

A Lambda function is triggered by S3 events. The event payload shown in the exhibit is received by the Lambda function. The function is supposed to process the CSV file and load it into DynamoDB. However, the function fails because it cannot read the file. What is the MOST likely cause?

Accepted Answer

The Lambda function's IAM role does not have s3:GetObject permission. Option C is correct. The Lambda function needs an IAM role with s3:GetObject permission to read the object. Option A is wrong because the event is valid. Option B is wrong because DynamoDB permissions are separate. Option D is wrong because the bucket exists.

Answer

The Lambda function lacks DynamoDB write permissions

Answer

The S3 bucket does not exist

Answer

The S3 event notification is misconfigured

Question 3

A company is building a data pipeline to process streaming data from IoT devices. The data must be ingested with low latency, transformed in real-time using custom logic, and stored in Amazon S3 partitioned by device ID and timestamp. Which combination of AWS services should the company use to meet these requirements?

Accepted Answer

Amazon Kinesis Data Streams with AWS Lambda and Amazon S3. Option B is correct because Amazon Kinesis Data Streams provides low-latency ingestion, AWS Lambda can apply custom transformation logic in real-time, and Amazon S3 with partitioning can store the data. Option A is wrong because Kinesis Data Firehose does not support custom transformation without Lambda and cannot partition on write. Option C is wrong because Amazon MSK (Managed Streaming for Kafka) is more complex than needed and not as tightly integrated. Option D is wrong because Amazon DynamoDB Streams is not designed for this volume of streaming data.

Answer

Amazon Kinesis Data Firehose with direct S3 delivery

Answer

Amazon Managed Streaming for Apache Kafka (MSK) with Amazon S3 sink connector

Answer

Amazon DynamoDB Streams with AWS Lambda and Amazon S3

Question 4

A data scientist is using Amazon SageMaker to train a model. The training data is stored in Amazon S3 and is approximately 500 GB. The data scientist notices that the training job is taking a long time to start because the data is being copied to the training instance's storage. The data scientist wants to reduce the startup time for subsequent training jobs. Which action should the data scientist take?

Accepted Answer

Use Pipe input mode instead of File input mode for the training job. Option A is correct because using Pipe input mode streams data directly from S3 to the training algorithm without downloading, reducing startup time. Option B is wrong because FSx for Lustre is not needed for simple streaming. Option C is wrong because increasing instance storage does not address the data transfer issue. Option D is wrong because using EBS optimized instances does not change the data loading mechanism.

Answer

Use an EBS-optimized instance type

Answer

Use Amazon FSx for Lustre as a high-performance file system mounted to the training instance

Answer

Increase the size of the training instance's Amazon EBS storage volume

Question 5

A company is designing a data pipeline to process log files from multiple sources. The logs are written to Amazon S3 every hour. The data is then transformed using AWS Glue ETL jobs and loaded into Amazon Redshift for analysis. The company needs to ensure that the data is available for analysis within 30 minutes of being written to S3. Currently, the Glue job is triggered hourly, but the company wants to reduce the latency. Which solution should the company implement?

Accepted Answer

Use Amazon S3 event notifications to invoke an AWS Lambda function that starts the Glue job automatically. Option C is correct because configuring an S3 event notification to invoke AWS Lambda, which starts the Glue job, allows near-real-time processing within minutes. Option A is wrong because hourly triggers do not reduce latency. Option B is wrong because increasing the crawler frequency does not trigger ETL jobs. Option D is wrong because Redshift Spectrum does not transform data.

Answer

Increase the frequency of the Glue crawler to run every 5 minutes

Answer

Use Amazon Redshift Spectrum to query the data directly from S3 without transformation

Answer

Reduce the Glue job trigger frequency to every 15 minutes

Question 6

A company stores sensitive customer data in Amazon S3. The company must ensure that data is encrypted at rest. The company also needs to manage the encryption keys using an AWS service that allows automatic rotation of keys. Which solution meets these requirements?

Accepted Answer

Use Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS) and enable automatic key rotation. Option B is correct because AWS KMS with automatic key rotation meets the encryption and key management requirements. Option A is wrong because SSE-S3 uses S3-managed keys that cannot be rotated manually. Option C is wrong because SSE-C requires the customer to manage keys. Option D is wrong because CloudHSM is a hardware security module but does not automatically rotate keys.

Answer

Use client-side encryption with AWS CloudHSM

Answer

Use Server-Side Encryption with Customer-Provided Keys (SSE-C)

Answer

Use Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3)

Question 7

A company is building a data pipeline that ingests data from on-premises databases into Amazon S3 using AWS Database Migration Service (AWS DMS). The company wants to capture continuous changes from the source database and replicate them to S3 in near-real time. Which AWS DMS configuration should the company use?

Accepted Answer

Create a CDC-only task with S3 as the target endpoint. Option D is correct because using a CDC-only task with S3 as the target endpoint replicates continuous changes to S3. Option A is wrong because a full-load task only migrates existing data. Option B is wrong because a full-load plus CDC task includes both, but the requirement is only changes. Option C is wrong because a validation task is for data validation, not replication.

Answer

Create a full-load task to copy the existing data

Answer

Create a full-load plus CDC task with S3 target

Answer

Create a validation task to compare source and target

Question 8

A data engineering team is designing a data lake on Amazon S3. The data is ingested from multiple sources in JSON, CSV, and Parquet formats. The team needs to make the data available for analysis using Amazon Athena and Amazon Redshift Spectrum. The team wants to minimize data transformation costs and storage overhead. Which data storage approach should the team use?

Accepted Answer

Convert all data to Apache Parquet before storing in S3. Option B is correct because storing data in columnar formats like Parquet reduces storage and improves query performance. Option A is wrong because storing all data as raw JSON inflates storage. Option C is wrong because converting data to a single format increases transformation costs. Option D is wrong because using a relational database is not a data lake approach.

Answer

Load the data into Amazon Redshift cluster and then unload to S3 in Parquet

Answer

Store the data in its original format in S3 and use Athena to query directly

Answer

Store the data in its original format and use AWS Glue to convert to Parquet when queried

Question 9

A company uses Amazon Kinesis Data Streams to ingest clickstream data from a website. The data is consumed by a custom application that runs on Amazon EC2 instances. The company notices that the consumer application is falling behind the producer, causing data to be throttled. Which action should the company take to improve the consumer's throughput?

Accepted Answer

Increase the number of shards in the Kinesis data stream. Option A is correct because increasing the number of shards increases the stream's capacity and allows more consumers to read in parallel. Option B is wrong because Lambda concurrency is not directly related to Kinesis shard throughput. Option C is wrong because Kinesis Data Firehose is a different service. Option D is wrong because reducing the retention period does not increase throughput.

Answer

Reduce the data retention period of the stream

Answer

Increase the maximum concurrency of the AWS Lambda function that processes the stream

Answer

Use Amazon Kinesis Data Firehose to deliver data to Amazon S3

Question 10

A company is using AWS Glue to run ETL jobs that process data from Amazon RDS to Amazon S3. The ETL jobs are failing intermittently with write timeout errors when writing to S3. The company wants to implement a retry mechanism for transient errors. What should the company do?

Accepted Answer

Configure the AWS Glue job to retry on failure by setting the 'Max retries' parameter. Option D is correct because configuring job retry in AWS Glue automatically retries failed jobs. Option A is wrong because CloudWatch alarms do not retry. Option B is wrong because SQS is not integrated with Glue jobs. Option C is wrong because increasing EBS volume does not address S3 write errors.

Answer

Increase the size of the Amazon EBS volumes attached to the Glue job

Answer

Use Amazon CloudWatch to monitor the job and manually restart on failure

Answer

Place the failed job messages in an Amazon SQS queue and reprocess them

Question 11

A company is migrating its on-premises Apache Hadoop cluster to AWS. The cluster processes large datasets using Spark jobs. The company wants to minimize operational overhead and use native AWS services. Which combination of services should the company use?

Accepted Answer

Amazon EMR with Spark and Amazon S3. Option B is correct because Amazon EMR is a managed Hadoop framework that supports Spark, and S3 is a scalable storage layer. Option A is wrong because EC2 would require managing the cluster manually. Option C is wrong because Redshift is a data warehouse, not a Hadoop replacement. Option D is wrong because Athena is for querying, not for running Spark jobs.

Answer

Amazon Redshift with Spectrum and Amazon S3

Answer

Amazon Athena and AWS Glue

Answer

Amazon EC2 instances with Apache Spark installed and Amazon S3

Question 12

A company is building a data pipeline that uses Amazon Kinesis Data Streams to ingest real-time events. The pipeline then uses AWS Lambda to process the events and store results in Amazon DynamoDB. The company wants to ensure that the Lambda function can process all events without data loss and without duplicating processing. Which TWO configuration steps should the company take?

Accepted Answer

Set the Lambda function's batch window to a small value (e.g., 1 second) to reduce processing latency. Options A and C are correct. Setting the Lambda batch window to a small value ensures low latency, and enabling the iterator age metric helps monitor lag. Option B is wrong because increasing the retention period does not prevent duplication. Option D is wrong because using a single shard limits throughput. Option E is wrong because disabling retries could cause data loss.

Answer

Increase the data retention period of the Kinesis stream to 7 days to allow reprocessing

Answer

Use a single shard for the Kinesis stream to ensure order and avoid parallel processing issues

Answer

Configure the Lambda function to disable retries on failure to avoid duplicate processing

Question 13

A company is using AWS Glue to catalog data stored in Amazon S3. The data is partitioned by year, month, day, and hour. The company runs hourly ETL jobs that add new partitions. The Glue crawler is scheduled to run every hour to update the Data Catalog. However, the crawler is taking longer than expected and is not completing before the next crawler run starts. Which THREE actions could the company take to resolve this issue?

Accepted Answer

Use multiple crawlers, each configured to crawl a different path (e.g., one for year=2023, one for year=2024). Options A, C, and D are correct. Increasing the crawler's S3 throughput improves speed. Partition indexing speeds up queries but does not affect crawler speed. Adding more crawlers allows parallel processing. Option B is wrong because partition indexing is for Athena/Redshift, not crawler performance. Option E is wrong because decreasing schedule frequency would increase the backlog.

Answer

Increase the throughput of the crawler by configuring the 'Schema updates' option

Answer

Enable partition indexing on the table to speed up the crawler

Answer

Decrease the crawler schedule frequency to every 2 hours to avoid overlapping runs

Question 14

A company needs to move 50 TB of data from an on-premises data center to Amazon S3. The company has a limited internet bandwidth of 100 Mbps. The data transfer must be completed within 10 days. Which TWO services should the company use together to meet these requirements?

Accepted Answer

Amazon S3 as the destination. Options A and E are correct. AWS Snowball Edge is a physical device for large data transfer, and S3 is the destination. Option B is wrong because internet transfer at 100 Mbps would take more than 50 days. Option C is wrong because Direct Connect is for dedicated network, not for physical transfer. Option D is wrong because VPN is not designed for bulk data transfer.

Answer

AWS Direct Connect

Answer

AWS Site-to-Site VPN

Answer

AWS DataSync over the internet

Question 15

Refer to the exhibit. A data engineer is creating an IAM policy for an AWS Glue ETL job that reads encrypted objects from an S3 bucket, transforms them, and writes the results back to the same bucket. The bucket uses SSE-KMS encryption with the KMS key specified. The ETL job is failing with an "Access Denied" error when trying to write data. What is the likely cause?

Accepted Answer

The policy is missing the kms:Encrypt permission. Option C is correct because the policy grants s3:PutObject, which is needed to write, but the KMS permissions include kms:Decrypt and kms:GenerateDataKey, which are sufficient for reading and writing with SSE-KMS. The issue is that the job role must also have kms:Encrypt to write encrypted objects. Option A is wrong because the policy includes s3:PutObject. Option B is wrong because the policy includes both KMS actions needed for reading. Option D is wrong because there is no s3:PutObject condition missing.

Answer

The policy is missing the kms:Decrypt permission

Answer

The policy is missing the s3:PutObjectAcl permission

Answer

The policy is missing the s3:PutObject permission