MLS-C01 Practice Exam 52 — 20 Questions

Question 1

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. Glue DataBrew provides data masking and cleansing capabilities. Glue Studio is for building ETL jobs, but masking requires custom code. Lake Formation is for fine-grained access control, not masking. Macie is for discovering sensitive data, not masking.

Answer

AWS Glue Studio

Answer

AWS Lake Formation

Answer

Amazon Macie

Question 2

A data engineer needs to schedule an AWS Glue ETL job to run every hour. The job reads from an Amazon DynamoDB table and writes to Amazon S3. Which AWS service should the engineer use to trigger the Glue job on schedule?

Accepted Answer

Amazon EventBridge (CloudWatch Events). Option A is correct. Amazon CloudWatch Events (now Amazon EventBridge) can trigger Glue jobs on a schedule. Option B is wrong because AWS Lambda is not a scheduler. Option C is wrong because Amazon Kinesis is for streaming. Option D is wrong because AWS Step Functions is for orchestrating workflows, but scheduling is typically done via EventBridge.

Answer

Amazon Kinesis Data Streams

Answer

AWS Step Functions

Answer

AWS Lambda

Question 3

A data scientist is building a training dataset from data stored in Amazon S3. The data consists of JSON files each containing a 'timestamp' field. The scientist wants to use AWS Glue to catalog the data and enable querying via Amazon Athena. However, Athena queries are returning zero results for time-range filters. What is the most likely cause?

Accepted Answer

The 'timestamp' field is not defined as a partition column in the Glue table.. Athena uses the partition columns derived from the Glue catalog. If the timestamp column is not used as a partition key, queries that filter on it will scan all data. Option B (timestamp is not a partition column) is correct because Glue can automatically partition by date, but the user must set it. Option A (wrong file format) is unlikely if JSON is supported. Option C (Athena cannot query nested JSON) is false; Athena supports JSON. Option D (insufficient permissions) would cause a different error.

Answer

The AWS Glue crawler does not have permissions to read the S3 bucket.

Answer

Athena cannot query nested JSON objects.

Answer

The JSON files are not in the correct format for Athena.

Question 4

Refer to the exhibit. A data engineer is troubleshooting an AWS Glue job that fails with an 'AccessDenied' error when trying to write to the S3 bucket 'my-data-lake'. The IAM policy attached to the Glue service role is shown. What is the missing permission?

Accepted Answer

s3:ListBucket. The policy allows s3:GetObject and s3:PutObject on the bucket's objects, but it does not allow s3:ListBucket on the bucket itself. Many Glue operations require ListBucket to discover objects. Option D (s3:ListBucket) is missing. Option A (s3:DeleteObject) is not needed. Option B (s3:GetBucketLocation) is not required. Option C (s3:PutObjectAcl) is not needed.

Answer

s3:PutObjectAcl

Answer

s3:GetBucketLocation

Answer

s3:DeleteObject

Question 5

A company has a large number of small CSV files (hundreds of thousands) in an S3 bucket. A data engineer needs to run a SQL query on this data using Amazon Athena. The queries are currently slow and expensive. Which two actions will improve query performance and reduce cost?

Accepted Answer

Partition the data by a commonly filtered column (e.g., date).. Option A and D are correct. Partitioning the data reduces the amount of data scanned by Athena, improving performance and reducing cost. Converting to columnar format (Parquet) further reduces scanned data and improves compression. Option B (compression) helps but is less impactful than partitioning and columnar format. Option C (more partitions) could help but too many small partitions may hurt performance. Option E (increasing S3 request rate) is not a direct action for Athena.

Answer

Increase the S3 request rate per prefix to improve read throughput.

Answer

Compress the CSV files using gzip.

Answer

Increase the number of partitions by splitting files into smaller ones.

Question 6

A company uses Amazon Kinesis Data Firehose to ingest streaming data and deliver it to an S3 bucket. The data is in JSON format with a timestamp field. The data science team wants to query the data using Athena with partitioning by year/month/day. How should the S3 data be organized?

Accepted Answer

Configure Firehose to use dynamic partitioning with custom prefix. Kinesis Firehose can partition data using custom prefixes like 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/'. This creates Hive-style partitions that Athena can automatically discover.

Answer

Store data in a single prefix and use Athena's 'partition projection' feature

Answer

Use AWS Glue crawler to partition the data after delivery

Answer

Use Amazon EMR to partition the data after delivery

Question 7

The Glue job my-glue-job fails after a few successful runs. The error log shows 'Job run exceeds max concurrent runs limit'. The CloudFormation template is shown in the exhibit. What change should be made to allow multiple runs to execute concurrently?

Accepted Answer

Set the MaxConcurrentRuns property to 3. The 'MaxConcurrentRuns' is set to 1, which prevents parallel executions. Setting it to a higher value (e.g., 3) allows concurrent runs. MaxRetries is for retry count, not concurrency. Role and TempDir are not relevant.

Answer

Change the IAM role to one with more permissions

Answer

Increase the MaxRetries property to 3

Answer

Remove the --job-bookmark-option argument

Question 8

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.. Using Amazon CloudWatch to monitor disk space and automatically resize the cluster is the best automated solution. Reserving nodes does not address space. Using RA3 nodes with managed storage is a good proactive step but does not automate resizing. The correct answer is to monitor and auto-resize.

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 9

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. Option A is correct because Kinesis Data Firehose supports built-in data format conversion to Parquet. Option B is wrong because AWS Glue is an ETL service, not a real-time transformation; Option C is wrong because Lambda can transform but must output to Firehose again; Option D is wrong because Kinesis Data Analytics is for streaming analytics, not format conversion.

Answer

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

Answer

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

Answer

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

Question 10

A data engineering team is designing a data pipeline that processes streaming data from Amazon Kinesis Data Streams using AWS Lambda. The team notices that some records are being processed multiple times (duplicates). Which TWO steps should the team take to ensure exactly-once processing?

Accepted Answer

Design the Lambda function to be idempotent.. Options A and C are correct. Making the Lambda function idempotent ensures that processing the same record multiple times does not cause duplicates downstream. Using a unique identifier per record and checking against a DynamoDB table allows deduplication. Option B is wrong because increasing batch size increases duplicates. Option D is wrong because disabling retries may cause data loss. Option E is wrong because Kinesis does not guarantee exactly-once delivery; deduplication is needed.

Answer

Increase the batch size to reduce the number of invocations.

Answer

Use Kinesis Producer Library (KPL) to guarantee exactly-once delivery.

Answer

Disable retries on the Lambda function.

Question 11

A company is streaming data from IoT devices to Amazon Kinesis Data Firehose, which writes to an Amazon S3 bucket. The data is then processed by an AWS Glue ETL job and loaded into Amazon Redshift. The team notices that some records are missing in Redshift. They suspect data loss during the Firehose delivery. Which configuration parameter should be checked first?

Accepted Answer

The buffer interval (e.g., 60 seconds) and buffer size.. Firehose can buffer data before writing to S3. If the buffer interval is too long and the stream ends, data may be lost if the buffer is not flushed. Option C (buffer interval) is the most likely cause. Option A (compression) does not cause loss. Option B (KMS key) is for encryption. Option D (error logging) only logs errors, does not prevent loss.

Answer

The AWS KMS key used for encryption.

Answer

The CloudWatch error logging configuration.

Answer

The compression format (GZIP, Snappy, etc.).

Question 12

Refer to the exhibit. A team deploys this CloudFormation stack. The Kinesis stream is created, but the Firehose delivery stream fails to create with a 'Resource handler returned message: Unable to assume role' error. What is the most likely cause?

Accepted Answer

The IAM role 'firehose-role' does not exist in the AWS account.. The Firehose role 'firehose-role' is specified with a static ARN that includes an account ID. If the stack is deployed in a different account, or if the role does not exist, the assumption fails. Option A (role does not exist in the account) is the most likely. Option B (stream encryption) is not related. Option C (retention period) is fine. Option D (shard count) is fine.

Answer

The shard count of 2 is too low for Firehose to read from.

Answer

The Kinesis stream is encrypted with KMS, but Firehose does not have permission to decrypt.

Answer

The retention period of 168 hours is too long for Firehose.

Question 13

A data engineer needs to design a data ingestion pipeline that ingests data from a MySQL database hosted on-premises into Amazon S3 for analytics. The pipeline must capture change data (CDC) and run continuously with low latency. Which two services should the data engineer use?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication.. Option A and D are correct. AWS DMS can capture ongoing changes from MySQL using CDC and replicate them to S3. Option B (Kinesis Data Streams) can receive CDC data from DMS but is not directly needed if DMS writes to S3. Option C (Glue ETL) is batch-oriented. Option E (AppFlow) is for SaaS applications, not for on-premises databases.

Answer

Amazon AppFlow.

Answer

AWS Glue ETL jobs scheduled at regular intervals.

Answer

Amazon Kinesis Data Streams.

Question 14

A Glue job fails with an AccessDenied error when trying to write to the S3 bucket my-data-lake. The IAM policy attached to the job role is shown in the exhibit. What is the MOST likely reason for the failure?

Accepted Answer

The job role does not have permissions to decrypt the KMS key used for server-side encryption. The policy allows s3:PutObject on the bucket, so write access seems granted. However, if the bucket is encrypted with SSE-KMS, the job also needs kms:Decrypt and kms:GenerateDataKey permissions. The policy does not include KMS actions. The bucket policy might also deny, but the most common issue is KMS encryption.

Answer

The s3:ListBucket action is missing on the bucket level

Answer

The s3:PutObject action is not sufficient; the job needs s3:PutObjectAcl

Answer

The resource ARN for s3:PutObject should include a specific prefix

Question 15

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

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics and AWS Lambda can process streams in real-time. AWS Glue is batch-oriented, Amazon EMR can process streams but is more batch, and Amazon Athena is for ad-hoc SQL queries on S3.

Answer

Amazon Athena

Answer

AWS Glue

Answer

Amazon EMR

Question 16

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. Option D is correct because enabling EMR Managed Scaling automatically adjusts cluster resources based on workload, which can reduce runtime. Option A is wrong because using spot instances may cause interruptions; Option B is wrong because more memory per node may not help if the bottleneck is parallelism; Option C is wrong because consolidating data into fewer files can reduce overhead but may not be the main issue.

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 17

A data engineer needs to transform large CSV files stored in Amazon S3 into Parquet format before loading into Amazon Redshift. The transformation logic is complex and requires custom Python code. Which AWS service should be used to perform this transformation with minimal operational overhead?

Accepted Answer

AWS Glue. Option B is correct because AWS Glue provides a serverless Spark environment with built-in support for ETL jobs, including converting CSV to Parquet. Option A (AWS Lambda) has a 15-minute timeout and is not suitable for large files. Option C (Amazon EMR) requires managing clusters. Option D (AWS Data Pipeline) is a legacy service with less flexibility.

Answer

AWS Lambda

Answer

Amazon EMR

Answer

AWS Data Pipeline

Question 18

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 policy allows GetTable and GetDatabase actions, but not CreateTable. The job needs glue:CreateTable permission. The S3 actions are sufficient. The error is specifically about creating a table.

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 19

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

Enable DynamoDB Accelerator (DAX) for caching. Option A is correct because enabling DynamoDB Accelerator (DAX) provides a caching layer that reduces read/write latency. Option B is wrong because increasing write capacity units helps throughput but not latency; Option C is wrong because S3 is object storage, not a low-latency store; Option D is wrong because using a VPC endpoint does not reduce latency significantly.

Answer

Use Amazon S3 instead of DynamoDB

Answer

Configure a VPC gateway endpoint for DynamoDB

Answer

Increase the DynamoDB write capacity units

Question 20

A data scientist wants to run a one-time SQL query on a large dataset stored in Amazon S3 (CSV format, 2 TB) using Amazon Athena. The query involves joining this dataset with a smaller table stored in Amazon RDS. What is the MOST cost-effective and performant approach?

Accepted Answer

Export the RDS table to S3 in Parquet format, then use Athena to join the two S3 datasets. Option A is correct. Exporting the RDS table to S3 as Parquet and running the join in Athena avoids data transfer costs and leverages Athena's fast query engine. Option B (federated query) adds complexity and may be slower. Option C (Redshift Spectrum) requires a Redshift cluster. Option D (Glue ETL) is overkill for a one-time query.

Answer

Use Amazon Redshift Spectrum to query both S3 and RDS

Answer

Use Athena Federated Query to query RDS directly

Answer

Use AWS Glue ETL to join the data and write results back to S3, then query with Athena