MLS-C01 Practice Test 44 — 15 Questions

Question 1

A company is using Amazon Kinesis Data Streams with a Lambda consumer. The Lambda function writes results to an S3 bucket. The team wants to ensure that each record is processed exactly once and in order. Which TWO configurations should the team implement? (Choose 2.)

Accepted Answer

Set the parallelization factor to 1. Option B (Enable parallelization factor) and Option E (Use S3 bucket versioning) are correct. Option A is wrong because batch size does not affect ordering. Option C is wrong because concurrency limits don't ensure ordering. Option D is wrong because DLQ does not affect ordering.

Answer

Set the batch size to 1

Answer

Increase the Lambda function's reserved concurrency

Answer

Configure a dead-letter queue for failed records

Question 2

A company is using AWS Glue to run ETL jobs that process data from an Amazon RDS for PostgreSQL database. The jobs are failing with connection timeouts. The security group for the RDS instance allows inbound traffic from the Glue job's security group. What is the most likely cause?

Accepted Answer

The VPC does not have an S3 VPC endpoint. AWS Glue jobs run in a VPC that requires an S3 VPC endpoint to access the AWS Glue catalog and other services. Without the endpoint, the job cannot connect to Glue's metadata store, causing timeouts. Option A is wrong because the security group is correctly configured. Option C is wrong because the issue is not about IAM permissions for RDS. Option D is wrong because there is no subnet issue mentioned.

Answer

The security group inbound rule is incorrect

Answer

RDS is in a different subnet

Answer

The IAM role does not have permission to access RDS

Question 3

A data engineering team is designing a data pipeline to process streaming data from social media feeds. The data must be deduplicated, enriched with customer information from a relational database, and stored in Amazon S3 in Parquet format. Which AWS services should the team use to build this pipeline? (Select TWO.)

Accepted Answer

AWS Glue. Option A is correct because Kinesis Data Streams ingests streaming social media data. Option D is correct because AWS Glue ETL jobs can read from the stream, perform deduplication and enrichment using JDBC connections to the relational database, and write to S3 in Parquet. Option B is wrong because Kinesis Data Firehose does not support enrichment with a relational database. Option C is wrong because Athena is a query engine, not an ETL tool. Option E is wrong because SageMaker is for ML, not data pipeline.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Athena

Answer

Amazon SageMaker

Question 4

Refer to the exhibit. A CloudFormation template creates an S3 bucket. The data engineering team stores daily log files in this bucket and queries them using Amazon Athena. After 30 days, queries on logs older than 30 days start failing with 'Access Denied' errors. What is the MOST likely reason?

Accepted Answer

The lifecycle rule transitions objects to GLACIER after 30 days, making them inaccessible to Athena.. Option C is correct because the lifecycle rule transitions objects to GLACIER after 30 days, and Athena cannot query objects in GLACIER storage class. Option A is wrong because transition to GLACIER does not affect permissions. Option B is wrong because object is not deleted until 365 days. Option D is wrong because SSE-S3 is not mentioned.

Answer

The bucket uses default encryption with SSE-S3, which Athena does not support.

Answer

The lifecycle rule deletes objects after 30 days.

Answer

The bucket policy denies access to objects older than 30 days.

Question 5

A company captures streaming data from IoT devices using Amazon Kinesis Data Streams. The data is consumed by a custom application that processes records in near real-time. Recently, the application has been falling behind, and the stream is showing increased 'iterator age' metrics in CloudWatch. Which action is MOST likely to reduce the iterator age?

Accepted Answer

Increase the number of shards in the stream. Option D is correct because increasing the number of shards increases the capacity of the stream, allowing more parallel consumers and reducing backlog. Option A is wrong because reducing retention period does not affect processing speed. Option B is wrong because increasing retention period may increase backlog. Option C is wrong because decreasing shards reduces capacity, worsening the issue.

Answer

Increase the data retention period of the stream

Answer

Decrease the number of shards in the stream

Answer

Reduce the data retention period of the stream

Question 6

A company uses Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application uses event time and watermarks for windowed aggregations. The team notices that the output from tumbling windows is delayed, and many late records are being dropped. What is the MOST likely cause?

Accepted Answer

The allowed lateness is set too low, causing late records to be discarded. Option B is correct because late records are dropped when the watermark has passed the window's end; increasing the allowed lateness gives more time for late records to arrive. Option A is wrong because idle sources cause watermarks to stall, not drop late records. Option C is wrong because checkpointing interval does not affect watermark progress. Option D is wrong because parallelism affects throughput, not watermark behavior.

Answer

The checkpointing interval is too long, causing state to be lost

Answer

The parallelism is too low, causing backpressure

Answer

The source is marking itself as idle, causing watermarks to stall

Question 7

A team is building a data pipeline using Amazon Kinesis Data Firehose to deliver real-time clickstream data to an Amazon S3 bucket. The data must be partitioned by year, month, day, and hour. Which configuration should the team use to achieve this?

Accepted Answer

Enable dynamic partitioning in Firehose and configure the partition keys as YYYY/MM/dd/HH. Option D is correct because Firehose has a built-in feature to add dynamic partitioning using keys like YYYY/MM/dd/HH based on the timestamp. Option A is wrong because Lambda can partition but adds complexity. Option B is wrong because S3 lifecycle rules do not repartition on delivery. Option C is wrong because partition projection is for Athena, not Firehose.

Answer

Configure an S3 lifecycle rule to move data into partition folders after delivery

Answer

Use an AWS Lambda function to write data to S3 with the desired partition structure

Answer

Use Amazon Athena partition projection to dynamically create partitions

Question 8

An ML engineer runs the AWS CLI command shown in the exhibit on a file in S3. The engineer wants to use this file in a SageMaker training job. What does the output reveal about the data?

Accepted Answer

The file is 5 GB and is stored as a CSV. Option B is correct because ContentLength is 5,368,709,120 bytes = 5 GB, and the file is a CSV. Option A is wrong because the file is 5 GB, not 5 MB. Option C is wrong because versioning is disabled (VersionId: null). Option D is wrong because the metadata shows the file is CSV, not Parquet.

Answer

The file is 5 MB and is stored as a CSV

Answer

The file is in Parquet format with two features and one label

Answer

The file is versioned and can be accessed by version ID

Question 9

An ML engineer is using Amazon SageMaker to train a model on a dataset that contains personal identifiable information (PII). The data must be encrypted at rest and in transit. The company uses AWS KMS for key management. How should the engineer configure the SageMaker training job to meet these encryption requirements?

Accepted Answer

Specify a KMS key for the training job's VolumeKmsKeyId and enable inter-container traffic encryption. Option C is correct because SageMaker supports KMS encryption for both the ML storage volume (at rest) and inter-container traffic (in transit). Option A is wrong because S3 Server-Side Encryption only covers data at rest in S3, not during training. Option B is wrong because SSL does not encrypt the storage volume. Option D is wrong because disabling inter-container encryption is not secure.

Answer

Enable S3 Server-Side Encryption (SSE-S3) on the input data bucket

Answer

Use a custom Docker image with built-in encryption and disable inter-container traffic encryption for performance

Answer

Use a VPC with an S3 VPC Endpoint and enable SSL for the endpoint

Question 10

A company is using Amazon DynamoDB as a source for a machine learning pipeline. The data is exported nightly to Amazon S3 using DynamoDB Streams and an AWS Glue job. The Glue job reads the stream records, transforms them, and writes to S3 in Parquet format. The team notices that the Glue job is taking too long and consuming high DynamoDB read capacity. Which THREE actions would reduce the load on DynamoDB and improve performance? (Choose THREE.)

Accepted Answer

Use Amazon DynamoDB export to S3 (incremental) feature instead of Glue. Option A is correct because enabling DynamoDB Streams with a Lambda function to write to S3 directly avoids Glue's read from DynamoDB. Option B is correct because using DynamoDB export to S3 (incremental) does not consume read capacity. Option D is correct because using S3 as the source for Glue reduces DynamoDB reads. Option C is wrong because increasing read capacity increases load. Option E is wrong because increasing write capacity does not affect reads.

Answer

Increase the DynamoDB write capacity units to handle the stream writes

Answer

Increase the DynamoDB read capacity units to handle Glue's workload

Question 11

A company uses AWS Glue ETL jobs to transform data from Amazon RDS for MySQL to Amazon S3. The transformation includes aggregations and joins. The job runs daily and processes approximately 100 GB of data. Recently, the job started failing with memory errors on the worker nodes. Which approach would MOST effectively resolve the issue without changing the logic?

Accepted Answer

Increase the number of workers in the job configuration. Option A is correct because increasing the number of workers distributes the workload and reduces memory pressure per node. Option B is wrong because reducing workers increases memory pressure. Option C is wrong because changing to G.1X increases memory per worker but may not be as cost-effective as adding more G.2X workers. Option D is wrong because Python shell is not suitable for large data transformations.

Answer

Switch from a Spark ETL job to a Python shell job

Answer

Decrease the number of workers to reduce overhead

Answer

Change the worker type from G.2X to G.1X to increase memory per worker

Question 12

A data engineer is designing a data pipeline that uses Amazon Kinesis Data Streams to ingest sensor data. The data must be processed in real-time, and the results must be stored in Amazon DynamoDB. Which TWO AWS services can be used together to achieve this? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Option B is correct because Kinesis Data Analytics can process data in real-time using SQL or Flink. Option E is correct because Lambda can consume from Kinesis and write to DynamoDB. Option A is wrong because Glue is for batch ETL, not real-time. Option C is wrong because S3 is not real-time storage. Option D is wrong because Athena is for querying, not processing.

Answer

Amazon Athena

Answer

AWS Glue

Answer

Amazon S3

Question 13

An AWS Glue job is failing with an error that it cannot access an S3 bucket. The IAM role attached to the Glue job is shown in the exhibit. What is the MOST likely cause of the failure?

Accepted Answer

The S3 bucket has a bucket policy that denies access to this role. Option C is correct because the trust policy allows only Glue service to assume the role, but if the S3 bucket has a bucket policy that denies access to the role, the Glue job will fail. Option A is wrong because the trust policy allows Glue. Option B is wrong because the role has S3 full access. Option D is wrong because the role explicitly allows S3 access.

Answer

The role lacks S3 permissions

Answer

The role does not have permission to call S3 APIs

Answer

The trust policy does not allow Glue to assume the role

Question 14

A machine learning team is building a real-time inference pipeline using Amazon SageMaker. The input data is located in an S3 bucket, and the team needs to transform the data before inference using a custom Python script. The transformation should run on a serverless infrastructure and must be triggered automatically when new data arrives in S3. Which combination of services should the team use?

Accepted Answer

Use AWS Lambda functions triggered by S3 events to run the transformation, then invoke a SageMaker endpoint.. Option C is correct because S3 events can trigger Lambda, which runs the custom script, and the output can be sent to a SageMaker endpoint for inference. Option A is wrong because SageMaker Processing is not serverless (it runs on EC2 instances). Option B is wrong because Glue is for ETL, not real-time inference. Option D is wrong because Kinesis Data Firehose is for streaming ingestion, not suitable for S3-triggered batch processing.

Answer

Use AWS Glue jobs triggered by S3 events.

Answer

Use Amazon SageMaker Processing jobs triggered by S3 events.

Answer

Use Amazon Kinesis Data Firehose to transform data and deliver to SageMaker.

Question 15

A team is building a data pipeline that ingests data from an Amazon S3 bucket, transforms it using AWS Glue, and loads it into Amazon Redshift for analysis. The Glue job runs on a schedule every hour. The team has noticed that the job takes longer than expected and sometimes fails due to memory issues. The data volume is variable, with occasional spikes. Which solution should the team implement to optimize the pipeline?

Accepted Answer

Enable job bookmarks to process only new data and use a G.2X worker type for more memory.. Option C is correct because Glue Job Bookmarks help track processed data and avoid reprocessing, and using a larger worker type with more memory can handle spikes. Option A is wrong because increasing the schedule frequency would not address the root cause. Option B is wrong because using a smaller worker type would worsen memory issues. Option D is wrong because Spark is already used internally.

Answer

Decrease the number of workers to reduce memory contention.

Answer

Increase the schedule frequency to run the job more often with smaller data increments.

Answer

Replace AWS Glue with Amazon EMR using Spark.