MLS-C01 Practice Test 33 — 15 Questions

Question 1

A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long and the scientist wants to reduce the training time by distributing the workload across multiple GPUs. Which SageMaker feature should be used to achieve this?

Accepted Answer

Use SageMaker's distributed training libraries. Option D is correct because SageMaker's distributed training libraries enable efficient distribution across multiple GPUs. Option A is wrong because Hyperparameter Tuning is for optimizing hyperparameters, not for distributed training. Option B is wrong because Automatic Model Tuning is for hyperparameter optimization. Option C is wrong because Amazon EMR is for big data processing, not for deep learning training.

Answer

Use Amazon EMR to distribute the training

Answer

Use SageMaker Automatic Model Tuning

Answer

Use SageMaker Hyperparameter Tuning

Question 2

Refer to the exhibit. An IAM policy is attached to an IAM role used by a SageMaker training job. The training job fails with an access denied error when trying to write model artifacts to an S3 bucket. What is the most likely cause?

Accepted Answer

The IAM role does not have permission to write to the S3 bucket. Option D is correct because the policy only allows s3:GetObject, not s3:PutObject, so the training job cannot write artifacts. Option A is wrong because the policy allows sagemaker:CreateTrainingJob. Option B is wrong because the policy allows s3:GetObject for the training data. Option C is wrong because the policy does not restrict the S3 bucket; it allows GetObject on a specific path.

Answer

The training job is trying to write to a different S3 bucket

Answer

The IAM role does not have permission to read the training data

Answer

The IAM role does not have permission to create training jobs

Question 3

A machine learning team is using Amazon SageMaker to train a deep learning model on a large dataset stored in Amazon S3. The training job is taking too long. The team wants to reduce training time without modifying the model architecture. Which THREE actions should the team take? (Choose 3.)

Accepted Answer

Enable SageMaker Managed Spot Training to use cheaper spot instances.. Option B is correct because Pipe mode streams data directly from S3, reducing download time. Option C is correct because SageMaker Managed Spot Training can reduce training time by using cheaper spot instances. Option D is correct because distributed training with multiple GPUs can parallelize computation. Option A is wrong because increasing instance size may help but is less effective than distributed training. Option E is wrong because SageMaker Processing is for data processing, not training.

Answer

Use a larger instance type with more vCPUs and memory.

Answer

Use SageMaker Processing to preprocess the data.

Question 4

A company is using SageMaker to host a model for real-time inference. They notice that the endpoint's latency increases over time. The model is stateless and the inference code does not log any errors. What is the MOST likely cause?

Accepted Answer

Memory leak in the inference container. Memory leaks cause gradual performance degradation. Option A is correct. Option B is wrong because the model is stateless. Option C is wrong because auto scaling would add instances, not degrade existing ones. Option D is wrong because the model is stateless.

Answer

Gradual increase in request payload size

Answer

Endpoint auto scaling is adding new instances

Answer

Model is accumulating state from previous requests

Question 5

A machine learning engineer is deploying a model that was trained on a large dataset stored in Amazon S3. The model needs to be retrained daily with new data. Which approach is the MOST cost-effective for storing the training data while allowing quick access for retraining?

Accepted Answer

Use S3 Intelligent-Tiering. Option B is correct because S3 Intelligent-Tiering automatically moves data between access tiers to optimize costs, and it provides low-latency access. Option A is wrong because S3 Standard is more expensive for data that is not frequently accessed. Option C is wrong because S3 Glacier is for long-term archival, not for data that needs to be accessed daily. Option D is wrong because S3 One Zone-IA is cheaper but may not be suitable for critical data and still costs more than Intelligent-Tiering for varying access patterns.

Answer

Store all data in S3 Standard

Answer

Use S3 Glacier Deep Archive

Answer

Use S3 One Zone-IA

Question 6

Which TWO AWS services can be used to deploy a machine learning model for serverless inference? (Choose 2.)

Accepted Answer

Amazon SageMaker Serverless Inference. SageMaker Serverless Inference (Option A) and AWS Lambda (Option C) both support serverless inference. Option B (Amazon ECS) requires managing clusters. Option D (Amazon EMR) is for big data. Option E (AWS Batch) is for batch computing.

Answer

Amazon EMR

Answer

Amazon ECS with Fargate

Answer

AWS Batch

Question 7

A data science team is using Amazon SageMaker to train a model. The training job is failing with an 'OutOfMemory' error. The team is using a p3.2xlarge instance with 61 GB of memory. They need to resolve this issue as quickly as possible. Which action should they take?

Accepted Answer

Use a larger instance type, such as p3.8xlarge. Option A is correct because switching to a larger instance type with more memory will immediately resolve the out-of-memory error. Option B is wrong because reducing batch size may help but requires code changes and might still not be enough. Option C is wrong because using spot instances does not affect memory. Option D is wrong because using distributed training adds complexity and may not resolve memory issues on a single instance.

Answer

Reduce the batch size in the training script

Answer

Use a spot instance to save costs

Answer

Enable distributed training across multiple instances

Question 8

A company is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU inference. The company wants to minimize latency and cost. Which instance type and deployment strategy should be used?

Accepted Answer

Use a real-time endpoint with a GPU instance and enable multi-model endpoints.. Option C is correct because SageMaker real-time endpoints with multi-model endpoints allow hosting multiple models on a single GPU instance, reducing cost while maintaining low latency. Option A is wrong because batch transform is not real-time. Option B is wrong because Serverless Inference does not support GPUs. Option D is wrong because asynchronous inference is not real-time.

Answer

Use a serverless inference endpoint with a GPU instance.

Answer

Use a batch transform job with a GPU instance.

Answer

Use an asynchronous inference endpoint with a GPU instance.

Question 9

Which THREE actions should be taken to ensure data security when training a model using Amazon SageMaker with data stored in Amazon S3? (Choose 3.)

Accepted Answer

Use a VPC to isolate the SageMaker training job. Encrypt data at rest (Option A), use VPC (Option B), and use an IAM role with least privilege (Option D). Option C is wrong because SageMaker uses S3, not EBS, for training data. Option E is wrong because SageMaker does not support S3 bucket policies directly; IAM roles are used.

Answer

Apply an S3 bucket policy that denies all access except from the SageMaker service

Answer

Attach an EBS volume for storing training data

Question 10

A data scientist is training a model using Amazon SageMaker with a custom Docker container. The training job fails with an error: 'Resource exhausted: Out of memory'. The training data is stored in S3. What should the data scientist do to resolve this issue?

Accepted Answer

Increase the instance memory by selecting a larger instance type.. Option B is correct because 'Out of memory' indicates the instance does not have enough memory. Increasing the instance memory resolves the issue. Option A is wrong because using Pipe mode streams data directly from S3 and can reduce memory usage, but the error is about memory exhaustion, not data loading. Option C is wrong because EBS volume size does not affect memory. Option D is wrong because reducing batch size might help but is not the primary fix; increasing instance memory directly addresses the issue.

Answer

Increase the EBS volume size attached to the training instance.

Answer

Use Pipe mode for data loading instead of File mode.

Answer

Reduce the batch size in the training script.

Question 11

A machine learning engineer is setting up a training job in Amazon SageMaker. Which THREE components are required to define a training job? (Choose three.)

Accepted Answer

Output data configuration (e.g., model artifact path).. Options A, B, and D are correct. An algorithm or container, input data configuration, and output data configuration are required. Option C is wrong because hyperparameters are optional. Option E is wrong because an IAM role is required, not a VPC (though VPC is common).

Answer

VPC configuration for network isolation.

Answer

Hyperparameters for the algorithm.

Question 12

Which TWO of the following are benefits of using SageMaker Managed Spot Training? (Select TWO.)

Accepted Answer

Potential for significant cost savings. Options A and C are correct. Managed Spot Training uses spare EC2 capacity at a lower cost (A) and can significantly reduce training costs (C). Option B is wrong because spot instances can be interrupted. Option D is wrong because spot training may take longer due to interruptions. Option E is wrong because spot instances are not guaranteed to be available.

Answer

No need to checkpoint the model

Answer

Faster training times

Answer

Guaranteed instance availability

Question 13

Which TWO approaches can reduce inference latency on a SageMaker real-time endpoint? (Choose 2.)

Accepted Answer

Attach an Elastic Inference accelerator. Using a GPU instance (Option A) and enabling SageMaker Model Monitor (Option E) are not directly for latency reduction. Actually, correct: Option B (Elastic Inference) and Option D (compiled model with SageMaker Neo) reduce latency. Option A is wrong because GPU does not always reduce latency; it can add overhead. Option C is wrong because larger batch sizes increase latency. Option E is wrong because Model Monitor adds overhead.

Answer

Increase the batch size

Answer

Enable SageMaker Model Monitor

Answer

Use a GPU instance type

Question 14

A machine learning engineer needs to deploy a model that performs real-time fraud detection. The model must be highly available and scalable. Which AWS service should be used to host the model?

Accepted Answer

Amazon SageMaker real-time endpoint. Option D is correct because Amazon SageMaker real-time endpoints are designed for low-latency, scalable, and highly available model hosting. Option A is wrong because AWS Lambda has limited execution time and is not suitable for heavy inference. Option B is wrong because Amazon ECS can host containers but requires more management; SageMaker is purpose-built. Option C is wrong because SageMaker batch transform is for offline predictions.

Answer

AWS Lambda

Answer

Amazon ECS with a custom container

Answer

Amazon SageMaker batch transform

Question 15

A company is training a deep learning model on Amazon SageMaker. The training job is failing with an out-of-memory error. Which SageMaker feature should the company use to resolve this issue without changing the instance type?

Accepted Answer

Use SageMaker distributed training with model parallelism. Option D is correct because SageMaker Managed Spot Training can reduce costs but does not fix memory issues. Option A and B are about debugging, not memory. Option C (distributed training) can split the model across instances, reducing per-instance memory usage. Option E is about cost, not memory.

Answer

Use SageMaker Savings Plans

Answer

Enable SageMaker Managed Spot Training

Answer

Use SageMaker Debugger to monitor memory usage

Answer

Enable SageMaker Profiler to profile memory