MLS-C01 Practice Exam 54 — 20 Questions

Question 1

Refer to the exhibit. A data engineer runs the AWS CLI command to check an object in an S3 bucket. The bucket is part of a data lake and is configured with versioning enabled. However, the output shows "VersionId": null. What is the most likely reason for this?

Accepted Answer

The object was uploaded before versioning was enabled. Option D is correct because the head-object command does not have the --version-id parameter, so it retrieves the latest version, but if versioning is enabled, the latest version will have a version ID. However, the output shows null, which indicates that the object is not versioned. This happens when the bucket has versioning enabled but the object was uploaded before versioning was enabled. Option A is wrong because the command does not require versioning parameter to show version ID. Option B is wrong because versioning is enabled at the bucket level, not object. Option C is wrong because SSE does not affect versioning.

Answer

The object is encrypted using SSE-S3, which hides the version ID

Answer

The command must include the --version-id parameter to display the version ID

Answer

Versioning is not enabled on the bucket

Question 2

A company uses Amazon Kinesis Data Streams to collect IoT sensor data. The stream has 4 shards. A consumer application reads from the stream using the Kinesis Client Library (KCL). The application processes records and stores them in Amazon DynamoDB. Recently, the data volume has increased, and the consumer is falling behind. Which action should the team take to increase the processing throughput?

Accepted Answer

Increase the number of shards in the Kinesis stream.. Option C is correct because increasing the number of shards increases the stream's throughput and allows more concurrent consumers. Option A is wrong because increasing the retention period does not affect throughput. Option B is wrong because adding more KCL workers without increasing shards will cause them to idle. Option D is wrong because increasing DynamoDB write capacity may reduce throttling but does not increase the consumer's reading throughput.

Answer

Deploy additional consumer instances using the same application name.

Answer

Increase the write capacity of the DynamoDB table.

Answer

Increase the data retention period of the stream to 7 days.

Question 3

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

Use an SQS queue to buffer event notifications and configure Lambda with a batch window. Option B is correct because S3 event notifications can be sent to an SQS queue, and Lambda can process messages in batches with longer timeouts. Option A is wrong because increasing timeout alone doesn't solve the issue of large objects; Option C is wrong because Kinesis is not needed; Option D is wrong because Step Functions adds complexity.

Answer

Increase the Lambda function timeout to 15 minutes

Answer

Stream events to Amazon Kinesis Data Streams and process with Lambda

Answer

Use AWS Step Functions to orchestrate the processing

Question 4

A company is using Amazon Kinesis Data Streams with 10 shards to ingest clickstream data. Each record is approximately 50 KB. The data is consumed by a Lambda function that writes to DynamoDB. The Lambda function is experiencing throttling errors. Which TWO actions should the data engineer take to resolve the issue? (Choose TWO.)

Accepted Answer

Request a limit increase for the Lambda function's concurrent execution limit. Option A (increase shards) increases throughput but may not solve Lambda throttling. Option B (increase Lambda concurrency limit) directly addresses throttling. Option C (increase batch size) reduces number of Lambda invocations. Option D (use Firehose) changes architecture. Option E (increase record size) is irrelevant. The correct answers are B and C because they reduce the number of concurrent Lambda executions and increase efficiency.

Answer

Increase the record size to 1 MB to reduce the number of records

Answer

Switch to Kinesis Data Firehose instead of Data Streams

Answer

Increase the number of shards in the Kinesis stream

Question 5

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 6

A company uses Amazon Redshift to run analytics on sales data. The data is loaded daily from S3 using COPY commands. The team notices that the COPY command performance degrades over time due to table bloat. The team needs to maintain query performance and reduce storage costs. Which combination of maintenance operations should the team perform regularly? (Choose THREE.)

Accepted Answer

Run the VACUUM command to reclaim space and re-sort rows.. Option A, C, and D are correct. VACUUM reclaims space and re-sorts rows (if sort keys defined). ANALYZE updates statistics for query planning. DEEP COPY recreates the table to eliminate bloat completely. Option B is wrong because UNLOAD exports data, not maintenance. Option E is wrong because changing distribution style is a schema change, not regular maintenance.

Answer

Run the UNLOAD command to export data to S3 and then reload.

Answer

Change the distribution style of the table to KEY.

Question 7

A company is streaming clickstream data from a website to Amazon Kinesis Data Streams. The data is consumed by a Lambda function that enriches each record with geolocation information before writing to an S3 bucket. Recently, the Lambda function has been failing with throttling errors. What is the MOST likely cause?

Accepted Answer

The Lambda function's concurrent execution limit has been reached. Option D is correct because the default Lambda concurrent execution limit (1000) can be reached if the stream has many shards or high throughput. Option A (shard limit) is a Kinesis limit, not Lambda. Option B (buffer size) is not a Lambda issue. Option C (record size) would cause a different error.

Answer

The Lambda function's payload size exceeds the 6 MB limit

Answer

The Lambda function's reserved concurrency is set too high

Answer

The Kinesis stream has exceeded the default shard limit of 500

Question 8

A company stores IoT sensor data in Amazon S3 and uses Amazon Athena for ad-hoc queries. The data is partitioned by date, but queries are still slow and expensive. Which TWO actions can improve query performance and reduce cost? (Choose TWO.)

Accepted Answer

Use S3 lifecycle policies to compact small files into larger ones. Option A (convert to Parquet) reduces data scanned. Option C (compact small files) reduces overhead. Option B (increase partitions) can create many small files. Option D (use Glue instead) changes service. Option E (disable encryption) is not related to performance.

Answer

Disable server-side encryption on the S3 bucket

Answer

Use AWS Glue instead of Athena for querying

Answer

Increase the number of partitions to hour-level granularity

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 data engineer needs to load data from a MySQL database to Amazon S3 daily. The database is 500 GB and the load window is 2 hours. The data must be extracted without impacting the source database performance. Which AWS service should be used to perform the extraction?

Accepted Answer

AWS Database Migration Service (AWS DMS) with a full-load task to S3.. Option D is correct because AWS Database Migration Service (DMS) can perform continuous or scheduled full-load and change-data-capture tasks from MySQL to S3 without impacting source performance when using appropriate task settings and replication instance. Option A is wrong because Amazon Athena is a query service, not an extraction tool. Option B is wrong because AWS Glue can connect to JDBC sources but may cause higher overhead on the source; DMS is purpose-built. Option C is wrong because Amazon EMR is for big data processing, not direct extraction from MySQL to S3.

Answer

AWS Glue ETL job using a JDBC connection to read the full table.

Answer

Amazon Athena with the MySQL federated query connector.

Answer

Amazon EMR with a Spark job reading from MySQL via JDBC.

Question 11

A company uses Amazon Athena to analyze data stored in S3. The data is in CSV format and is partitioned by year/month/day. Queries that filter on a specific day are slow. The team wants to improve query performance without changing the data format. Which action should the team take?

Accepted Answer

Ensure that the S3 folder structure follows the Hive-style partition naming convention (e.g., year=2023/month=01/day=01).. Option D is correct because partitioning pruning is most effective when the partition columns are in a Hive-style format (e.g., year=2023/month=01/day=01). If the folders are not in that format, Athena may not prune partitions properly. Option A is wrong because increasing the number of partitions would further slow queries. Option B is wrong because converting to Parquet changes the format. Option C is wrong because using a large number of small files hurts performance.

Answer

Use a larger number of small files to increase parallelism.

Answer

Increase the number of partitions by adding hour and minute levels.

Answer

Convert the data to Parquet format.

Question 12

A data engineering team is building a real-time clickstream analytics pipeline on AWS. They need to ingest millions of events per second from mobile apps and websites, process them with low latency, and store the results in Amazon S3 for downstream analysis. Which combination of AWS services should the team use to minimize operational overhead while meeting these requirements?

Accepted Answer

Use Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose to deliver results to Amazon S3.. Option C is correct because Amazon Kinesis Data Streams can handle high-throughput ingestion, Kinesis Data Analytics processes streaming data with low latency, and Kinesis Data Firehose delivers processed data to S3 with minimal overhead. Option A is wrong because AWS Glue is a batch ETL service, not suitable for real-time processing. Option B is wrong because Amazon EMR is a managed Hadoop cluster that requires more operational overhead. Option D is wrong because Amazon MQ is a message broker for standard messaging protocols, not optimized for real-time analytics.

Answer

Use Amazon MQ to ingest streaming data, AWS Lambda to process each message, and save output to Amazon S3.

Answer

Use Amazon Kinesis Data Streams to ingest data, Amazon EMR to process with Spark Streaming, and save output to Amazon S3.

Answer

Use AWS Glue to ingest data into Amazon RDS, then use AWS Glue ETL jobs to transform and load into Amazon S3.

Question 13

A data engineer is designing a data pipeline that uses Amazon Kinesis Data Streams to ingest real-time transaction data. The data must be processed in near real-time and stored in Amazon S3 for long-term analytics. The engineer wants to ensure data durability and exactly-once processing semantics. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Use the Kinesis Producer Library (KPL) with exactly-once delivery.. Correct options: C and D. Using the Kinesis Producer Library (KPL) with exactly-once delivery ensures no duplicates. Enabling Kinesis Data Firehose's exactly-once delivery to S3 ensures data is written exactly once. Option A (SQS) is not part of Kinesis. Option B (Glue) does not provide exactly-once for streaming. Option E (Lambda) can process records but does not guarantee exactly-once semantics without additional logic.

Answer

Use AWS Glue streaming ETL with checkpointing.

Answer

Use AWS Lambda with the Kinesis trigger and enable event source mapping with RetryAttempts set to 0.

Answer

Use Amazon SQS as the event source for downstream processing.

Question 14

A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time sensor data. The application reads from a Kinesis data stream, performs windowed aggregations, and writes results to an S3 bucket. Recently, the application has been experiencing high latency and checkpoint failures. What is the MOST likely cause?

Accepted Answer

The S3 destination bucket is located in a different AWS Region than the Kinesis application. Option C is correct. If the S3 bucket is in a different region, cross-region data transfer can introduce latency and checkpoint failures. Option A (parallelism) would cause resource issues, not necessarily checkpoint failures. Option B (shard count) would cause throttling. Option D (record size) is limited to 1 MB.

Answer

The number of shards in the Kinesis stream is insufficient for the data volume

Answer

The record size in the Kinesis stream exceeds the 1 MB limit

Answer

The parallelism of the Flink application is set too low for the number of shards

Question 15

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 16

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 17

A company uses Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The data is organized by year/month/day/hour. The team needs to ensure that all data is encrypted at rest in S3 using an AWS KMS customer managed key (CMK). Which configuration should the team implement?

Accepted Answer

In the Firehose delivery stream configuration, enable S3 destination encryption and select the customer managed KMS key.. Option A is correct because Kinesis Data Firehose can be configured to use server-side encryption with AWS KMS (SSE-KMS) for the S3 destination. Option B is wrong because S3 default encryption is applied at the bucket level, but Firehose can override it; the recommended approach is to configure encryption in Firehose. Option C is wrong because the bucket policy with Deny is not needed if encryption is configured properly. Option D is wrong because client-side encryption is not suitable for this pipeline.

Answer

Configure the S3 bucket's default encryption to use the customer managed KMS key.

Answer

Use an AWS Lambda function to encrypt the data after it is delivered to S3.

Answer

Add a bucket policy that denies PutObject unless the request includes the correct KMS key.

Question 18

Refer to the exhibit. A data engineer has attached this IAM policy to an IAM role used by an AWS Glue ETL job. The job reads from an S3 bucket (data-bucket) that is encrypted with SSE-KMS using the key arn:aws:kms:us-east-1:123456789012:key/abc123, transforms the data, and writes the result to a different S3 bucket (output-bucket) encrypted with a different KMS key (arn:aws:kms:us-east-1:123456789012:key/xyz789). When the job runs, it fails with an access denied error. What is the cause?

Accepted Answer

The policy does not grant kms:Encrypt permission for the output bucket's KMS key.. Option C is correct because the policy grants kms:Decrypt and kms:GenerateDataKey for the input bucket's key, but does not grant the equivalent permissions for the output bucket's key (xyz789). The job needs to encrypt the output, so it needs kms:Encrypt (or GenerateDataKey) for the output key. Option A is wrong because the policy includes s3:PutObject. Option B is wrong because the Glue catalog permissions are sufficient. Option D is wrong because the policy includes s3:GetObject.

Answer

The policy does not include s3:GetObject permission for the output bucket.

Answer

The policy does not include glue:CreateTable permission.

Answer

The policy does not include s3:PutObject permission for the output bucket.

Question 19

A research institution is building a data lake to store genomics data. Each experiment generates multiple files totaling about 500 GB. The data is stored in Amazon S3 and needs to be processed by multiple machine learning (ML) training jobs running on Amazon SageMaker. The data has a high churn rate; after 30 days, most data becomes irrelevant and should be moved to Amazon S3 Glacier Deep Archive. The institution wants to minimize storage costs while maintaining data durability. Which S3 storage class should they use for the first 30 days?

Accepted Answer

Use S3 Intelligent-Tiering for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.. Option C is correct because S3 Intelligent-Tiering automatically moves data between access tiers based on usage, which is ideal for data with unknown or changing access patterns. Option A is wrong because S3 Standard is more expensive for data that may not be accessed frequently after the initial processing. Option B is wrong because S3 One Zone-IA is not durable across AZs. Option D is wrong because S3 Glacier Instant Retrieval is for long-lived, rarely accessed data requiring millisecond access; not cost-effective for the first 30 days.

Answer

Use S3 One Zone-IA for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.

Answer

Use S3 Standard for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.

Answer

Use S3 Glacier Instant Retrieval for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.

Question 20

A data engineer needs to ingest data from an on-premises Apache Kafka cluster into Amazon S3 with minimal latency (under 5 minutes) for real-time analytics. The data volume is approximately 10 MB per second. Which solution is MOST cost-effective and meets the latency requirement?

Accepted Answer

Use Amazon MSK to mirror the on-premises Kafka cluster, then use Kinesis Firehose to write to S3. Option B is correct. Amazon MSK (Managed Streaming for Kafka) can replicate the on-premises Kafka topics to the cloud with low latency, and then Kinesis Firehose can deliver to S3 with a 60-second buffer. Option A (Direct connect + Lambda) has higher latency. Option C (Kinesis Data Streams) requires additional ingestion from on-premises. Option D (S3 Transfer Acceleration) is for file uploads, not streaming.

Answer

Use Amazon S3 Transfer Acceleration for direct uploads from on-premises

Answer

Use Amazon Kinesis Data Streams with a Direct Connect connection from on-premises

Answer

Set up a VPN connection and use AWS Lambda to consume from Kafka and write to S3