MLS-C01 Practice Test 47 — 15 Questions

Question 1

Refer to the exhibit. A data scientist is training a PyTorch model on a SageMaker ml.p3.2xlarge instance (16 GB GPU memory). The training fails with the shown error. Which change should the scientist make to resolve the error?

Accepted Answer

Reduce the batch size in the training script.. Reducing the batch size (Option D) reduces GPU memory usage per iteration and can resolve OOM errors. Option A (increase instance count) does not reduce per-GPU memory. Option B (use Spot) does not help. Option C (increase epochs) will still fail.

Answer

Increase the number of instances to 2.

Answer

Use SageMaker Managed Spot Training.

Answer

Increase the number of epochs.

Question 2

Refer to the exhibit. An administrator has attached this IAM policy to a user. The user tries to start a SageMaker training job that uses a custom Docker image from Amazon ECR. The training job fails with an access denied error. What is the MOST likely reason?

Accepted Answer

The policy is missing ecr:GetDownloadUrlForLayer and ecr:BatchGetImage.. The policy grants s3:* on the bucket but does not include ecr:* permissions (Option B) needed to pull the Docker image. Option A (s3:* on objects) is too broad but not the issue. Option C (iam:PassRole on role) is allowed. Option D (sagemaker:* on specific resources) is not the issue.

Answer

The s3:* action is too permissive and should be scoped.

Answer

The iam:PassRole permission is missing the SageMaker service principal.

Answer

The sagemaker:* action should be restricted to specific resources.

Question 3

A company wants to use Amazon SageMaker to train a model using data stored in Amazon S3. The data is sensitive and must be encrypted at rest and in transit. Which THREE steps should be taken to ensure data security?

Accepted Answer

Configure the SageMaker training job to use an IAM role with least privilege and enable network isolation. Encrypting the S3 bucket with KMS ensures encryption at rest. Using VPC endpoints for S3 ensures data does not traverse the public internet. Enabling encryption in transit between SageMaker and S3 (using HTTPS) is essential. Option A (public internet) is not secure; Option E (Redshift) is irrelevant.

Answer

Store the data in Amazon Redshift instead of S3

Answer

Allow internet access for the SageMaker notebook instance

Question 4

A data scientist needs to run complex ETL transformations on a large dataset stored in Amazon S3. The transformations are written in PySpark and require occasional access to Hive metastore. The solution should minimize operational overhead and allow the data scientist to focus on code development. Which AWS service should be used?

Accepted Answer

Amazon EMR. Amazon EMR is a managed Hadoop framework that supports PySpark and Hive metastore. AWS Glue is good for simpler ETL but has limitations on custom PySpark code. Amazon SageMaker is for ML training, not general ETL. Amazon Redshift is a data warehouse.

Answer

Amazon Redshift

Answer

AWS Glue

Answer

Amazon SageMaker

Question 5

A data scientist is training a model using Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single ml.p3.2xlarge instance. The data is stored in S3 and is about 50 GB in size. Which action would MOST likely reduce training time?

Accepted Answer

Change the input mode from File to Pipe.. Option C is correct because using Pipe input mode streams data directly from S3 to the training algorithm without writing to disk, reducing I/O latency. Option A is wrong because increasing instance size may help but incurs higher cost and may not address data loading bottleneck. Option B is wrong because SageMaker automatically manages data channels. Option D is wrong because enabling data compression on the S3 objects does not reduce training time significantly and adds CPU overhead for decompression.

Answer

Enable automatic data sharding in the SageMaker training job.

Answer

Enable S3 server-side encryption on the training data.

Answer

Use a larger instance type, such as ml.p3.16xlarge.

Question 6

A machine learning engineer is deploying a model using Amazon SageMaker. The model is a PyTorch model that performs real-time inference with low latency requirements. The engineer wants to use automatic scaling based on the number of concurrent requests. Which SageMaker feature should be used to achieve this?

Accepted Answer

Use Amazon SageMaker automatic scaling with a target tracking scaling policy.. Option B is correct because SageMaker's application auto scaling with a target tracking scaling policy based on the SageMakerVariantInvocationsPerInstance metric automatically adjusts the number of instances. Option A is wrong because SageMaker does not have an integrated Elastic Load Balancer; it uses a built-in load balancer. Option C is wrong because SageMaker does not natively support AWS Auto Scaling groups for endpoints. Option D is wrong because Amazon API Gateway is used for REST APIs, not for scaling SageMaker endpoints.

Answer

Create an AWS Auto Scaling group for the SageMaker endpoint.

Answer

Enable Elastic Load Balancing for the endpoint.

Answer

Deploy the model behind Amazon API Gateway with a Lambda function.

Question 7

A company wants to deploy a machine learning model on Amazon SageMaker and needs to monitor the model's performance in production. Which TWO AWS services can be used to set up monitoring?

Accepted Answer

Amazon CloudWatch. Options A and D are correct. Amazon CloudWatch can monitor endpoint metrics, and SageMaker Model Monitor can detect data drift. Option B is wrong because AWS Config is for resource compliance, not model monitoring. Option C is wrong because Amazon Inspector is for security assessment. Option E is wrong because AWS X-Ray is for tracing requests, not model performance.

Answer

AWS X-Ray

Answer

Amazon Inspector

Answer

AWS Config

Question 8

A data scientist creates a model resource in SageMaker using the JSON configuration in the exhibit. When creating an endpoint, the deployment fails with an error 'ModelError: Cannot find inference code'. What is the MOST likely cause?

Accepted Answer

The inference container environment does not specify SAGEMAKER_PROGRAM. Option D is correct because the inference container's Environment is empty; it should define SAGEMAKER_PROGRAM for the inference script. Option A is not necessarily missing. Option B would cause a different error. Option C is not required for inference.

Answer

The model.tar.gz file is missing the model weights

Answer

The ECR image does not exist

Answer

The training container does not have the SAGEMAKER_PROGRAM variable

Question 9

A data engineer needs to continuously ingest streaming data from thousands of IoT devices and store the raw data in Amazon S3 for archival processing. The data volume varies significantly throughout the day, and the solution must be serverless, scalable, and cost-effective. Which AWS service should be used to capture and buffer the streaming data before writing to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is a serverless service that can capture, transform, and load streaming data into S3. It automatically scales and can buffer data to handle varying volumes. Amazon Kinesis Data Streams (option A) is for real-time processing but requires a consumer to write to S3. Amazon SQS (option C) is a message queue, not designed for streaming data. AWS Glue (option D) is a batch ETL service.

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Answer

Amazon Simple Queue Service (SQS)

Question 10

A company is running a machine learning training job on Amazon SageMaker that reads training data from an S3 bucket. The job fails intermittently with an S3 throttling error. The data is partitioned across thousands of small files (average 100 KB). Which strategy is MOST effective to resolve the throttling issue?

Accepted Answer

Combine the small files into larger files (e.g., 100 MB) using a preprocessing step. Combining small files into larger files reduces the number of S3 GET requests, which reduces the chance of throttling. Increasing the number of instances (option A) would increase parallelism and could worsen throttling. Using S3 Transfer Acceleration (option B) improves transfer speed but does not reduce request rate. Using Athena (option D) is for querying, not for training data access.

Answer

Use Amazon Athena to query the data and output results to a new S3 location

Answer

Enable S3 Transfer Acceleration on the bucket

Answer

Increase the number of SageMaker training instances to distribute the load

Question 11

A data scientist wants to explore a large dataset stored in Amazon S3 using SQL queries without moving the data. The dataset is in CSV format and is updated daily with new partitions. Which AWS service should be used to directly query the data in S3?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows querying data directly in S3 using standard SQL. Amazon Redshift Spectrum (option B) can also query S3 but requires a Redshift cluster. Amazon EMR (option C) requires cluster management. AWS Glue (option D) is for ETL, not interactive querying.

Answer

Amazon Redshift Spectrum

Answer

Amazon EMR

Answer

AWS Glue

Question 12

A company is building a data pipeline that ingests data from multiple sources into a centralized data lake on Amazon S3. The data must be transformed before it is available for analysis. The pipeline should be event-driven, automatically triggering transformation jobs when new data arrives. Which combination of AWS services should be used?

Accepted Answer

Amazon S3 event notifications to invoke AWS Lambda, which triggers an AWS Glue job. Amazon S3 can send events to AWS Lambda or SQS when new objects are created. AWS Glue can be triggered by Lambda to run ETL jobs. Step Functions (option A) can orchestrate but adds complexity. Kinesis Data Analytics (option B) is for streaming analytics, not batch. EMR (option D) requires cluster management.

Answer

Amazon Kinesis Data Analytics for transformation

Answer

Amazon EMR with automatic scaling

Answer

AWS Step Functions to orchestrate the pipeline

Question 13

A data engineering team is designing a data lake on Amazon S3. They need to enforce encryption at rest for all data stored in the bucket. The security policy requires that the encryption keys be managed by the organization using AWS Key Management Service (KMS), and that the bucket must deny uploads of unencrypted objects. Which bucket policy should be applied?

Accepted Answer

A bucket policy that denies PutObject unless the request includes the 'x-amz-server-side-encryption-aws-kms-key-id' header matching the desired KMS key ID. To enforce encryption, a bucket policy can deny PutObject if the object is not encrypted with the required KMS key. The condition 's3:x-amz-server-side-encryption-aws-kms-key-id' checks the key ID. Option B correctly denies requests that do not include the required key. Option A is incomplete; Option C uses the wrong condition; Option D uses SSE-S3 instead of KMS.

Answer

A bucket policy that denies PutObject unless the request includes the 'x-amz-server-side-encryption' header with value 'AES256'

Answer

A bucket policy that denies PutObject if the 'x-amz-server-side-encryption' header is not present

Answer

Enable default encryption on the bucket with AWS-KMS

Question 14

A company uses Amazon RDS for its transactional database and needs to export a daily snapshot of a table to Amazon S3 in Parquet format for analytics. Which AWS service can perform this export without writing custom code?

Accepted Answer

AWS Glue. AWS Glue can connect to RDS, extract data, and write to S3 in Parquet format using a Glue job. AWS Database Migration Service (DMS) is primarily for migration, not scheduled exports. Amazon Athena cannot directly connect to RDS. Amazon Redshift is a data warehouse, not a migration tool.

Answer

Amazon Redshift

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Athena

Question 15

A company is streaming data from thousands of devices using Amazon Kinesis Data Streams. The data is consumed by a AWS Lambda function that processes each record. The Lambda function is experiencing high error rates and throttling due to the volume of data. Which action would MOST effectively improve the processing throughput and reduce errors?

Accepted Answer

Increase the number of shards in the Kinesis stream. Increasing the number of shards increases the stream's capacity. Using a larger batch size in Lambda (option B) can improve throughput but may cause timeout. Using SQS (option C) introduces another queue. Using Firehose (option D) changes the architecture. Increasing shards is the most direct way to increase throughput.

Answer

Send the data to Amazon SQS first and then process with Lambda

Answer

Use Amazon Kinesis Data Firehose instead of Kinesis Data Streams

Answer

Increase the Lambda function's batch size and reduce the batch window

MLS-C01 Practice Test 47 — 15 Questions

Exhibit

MLS-C01 Practice Test 47 — 15 Questions

Exhibit