MLS-C01 Practice Test 30 — 15 Questions

Question 1

An ML team uses Amazon SageMaker to train a deep learning model. The training job runs on a single ml.p3.2xlarge instance and is taking 10 hours. The team wants to reduce the training time to under 2 hours without changing the model architecture. Which approach is MOST effective?

Accepted Answer

Use SageMaker distributed training with multiple ml.p3.2xlarge instances.. Using SageMaker's distributed training with multiple GPU instances (A) can significantly reduce training time by parallelizing the workload. Changing to a larger instance (B) may help but not as much as multiple instances. Using SageMaker Debugger (C) does not speed up training. Using Spot Instances (D) saves cost but not time.

Answer

Use SageMaker Managed Spot Training to reduce cost.

Answer

Switch to a single ml.p3.16xlarge instance with more GPUs.

Answer

Enable SageMaker Debugger to identify bottlenecks.

Question 2

A company uses SageMaker to host a model for real-time predictions. The model is updated weekly. To minimize downtime during model updates, what should the company do?

Accepted Answer

Create a new endpoint configuration with the new model and update the endpoint to use the new configuration. Option D is correct: Creating a new endpoint configuration with the new model and updating the endpoint with a blue/green deployment minimizes downtime. Option A (delete and recreate) causes downtime. Option B (update endpoint directly) causes brief downtime. Option C (multiple endpoints with load balancer) works but is more complex than SageMaker's built-in blue/green.

Answer

Create a second endpoint with the new model and use an Application Load Balancer to route traffic

Answer

Update the existing endpoint configuration with the new model URL

Answer

Delete the existing endpoint and create a new one with the updated model

Question 3

A company is deploying a SageMaker model for real-time inference. The endpoint must be highly available and cost-effective. Which TWO actions should the company take? (Select TWO.)

Accepted Answer

Deploy the endpoint with at least two instances in different Availability Zones. Options A and C are correct. A: Multiple instances across AZs ensures HA. C: Auto-scaling adjusts capacity based on demand, improving cost. B (single instance) lacks HA. D (spot instances) are cheaper but not for real-time HA. E (GPU) is not necessarily cost-effective.

Answer

Use managed spot training for inference

Answer

Use GPU instances for all models even if not required

Answer

Use a single large instance to handle peak load

Question 4

A SageMaker endpoint has a CloudWatch alarm configured as shown in the exhibit. The alarm fires when the p99 latency exceeds 500 ms for two consecutive minutes. Which action should the data scientist take to reduce latency?

Accepted Answer

Increase the number of instances behind the endpoint. Option A is correct: Increasing instance count reduces load per instance, reducing latency. Option B (GPU) may not help if model is not compute-bound. Option C (batch size) depends on model. Option D (async inference) changes architecture.

Answer

Increase the batch size in the inference request

Answer

Use SageMaker asynchronous inference instead of real-time

Answer

Switch to GPU instances even if the model does not require GPU

Question 5

A data scientist is deploying a real-time inference endpoint using SageMaker. The model is a large NLP model requiring GPU for low latency. The endpoint must be highly available across two Availability Zones. Which deployment configuration meets these requirements?

Accepted Answer

Deploy a multi-model endpoint on an ml.p3.2xlarge instance with auto-scaling and at least two instances in different AZs. Option B is correct: A multi-model endpoint on a GPU instance with auto-scaling across AZs ensures high availability. Option A uses CPU. Option C (single instance) lacks HA. Option D (batch transform) is not real-time.

Answer

Deploy a single model endpoint on an ml.c5.xlarge instance with auto-scaling

Answer

Use SageMaker batch transform on GPU instances

Answer

Deploy a single model endpoint on an ml.p3.2xlarge instance with one instance

Question 6

A data scientist wants to use AWS Step Functions to orchestrate a machine learning workflow including data preprocessing, training, and evaluation. Which SageMaker integration is best suited for this purpose?

Accepted Answer

Use the SageMaker SDK with Step Functions service integrations. Option B is correct: SageMaker SDK with Step Functions integration allows direct orchestration. Option A (SageMaker Pipelines) is a different orchestration tool. Option C (Lambda) adds complexity. Option D (Batch) is for non-interactive jobs.

Answer

Implement each step as an AWS Lambda function and call Step Functions

Answer

Use SageMaker Pipelines to define the workflow

Answer

Use AWS Batch to run the steps sequentially

Question 7

A data scientist is using SageMaker to train a model. The training job needs to access data in an S3 bucket in a different AWS account. The data scientist has set up proper S3 bucket policies and IAM roles. Which THREE steps are necessary to allow SageMaker to access the cross-account S3 bucket? (Select THREE.)

Accepted Answer

Configure the S3 bucket policy to grant access to the SageMaker execution role ARN from the training account. Options A, B, and C are correct. A: Role must have S3 access. B: Bucket policy must allow cross-account access. C: SageMaker execution role must be able to assume the cross-account role. D (VPC endpoint) is not required for cross-account access. E (KMS) is only if encryption is used.

Answer

Create a VPC endpoint for S3 in the training account

Answer

Use an AWS KMS key to encrypt the data in transit

Question 8

A data scientist attempts to create a SageMaker training job using the IAM policy shown in the exhibit. The training job fails with an access denied error. What is the most likely cause?

Accepted Answer

The IAM policy is missing the s3:ListBucket action. Option C is correct: The policy allows s3:GetObject but not s3:ListBucket, which is required to read objects. Option A (missing sagemaker:DescribeTrainingJob) is not needed for creation. Option B (resource arn for s3 is wrong) is correct but not the cause of access denied. Option D (missing s3:PutObject) is not required for reading training data.

Answer

The S3 bucket policy does not grant access to the SageMaker service principal

Answer

The IAM policy is missing the sagemaker:DescribeTrainingJob permission

Answer

The IAM policy is missing the s3:PutObject permission

Question 9

A company is using Amazon SageMaker Ground Truth to create labeled datasets for a text classification task. The labeling job uses a private workforce of 10 annotators. After labeling 10,000 items, the quality of labels is inconsistent. Which approach will MOST effectively improve labeling consistency?

Accepted Answer

Configure the labeling job to use annotation consolidation with majority voting and require multiple annotations per item.. Option D is correct because using a consensus pipeline with majority voting and annotation consolidation reduces individual annotator bias. Option A is wrong because increasing workforce size does not directly improve consistency. Option B is wrong because removing outliers after labeling may discard valid data. Option C is wrong because active learning selects examples for labeling, but does not improve annotator consistency.

Answer

Remove annotations from annotators with low agreement after the job completes.

Answer

Increase the number of annotators to 20 to average out inconsistencies.

Answer

Use active learning to automatically label the most confident samples and only send uncertain ones to annotators.

Question 10

A data scientist is training a deep learning model on Amazon SageMaker using a custom TensorFlow container. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 16 GB GPU memory and 61 GB system memory. The model uses mixed precision training. Which step should the data scientist take to resolve the issue without changing the instance type?

Accepted Answer

Reduce the batch size. Option D is correct because reducing the batch size lowers memory usage during training. Option A (increase instance type) increases cost and may not be necessary. Option B (use gradient accumulation) simulates larger batch sizes without increasing memory footprint, but does not reduce memory usage per step. Option C (enable automatic mixed precision) is already in use. Option E (use model parallelism) is complex and may not be applicable for a single model fitting in memory with batch size reduction.

Answer

Use gradient accumulation to simulate a larger batch size

Answer

Use model parallelism across multiple GPUs

Answer

Enable automatic mixed precision (AMP)

Answer

Increase the instance type to ml.p3.8xlarge

Question 11

A company is using Amazon SageMaker to train a model. The training data includes sensitive personally identifiable information (PII). The company needs to ensure that the training data is protected and that the trained model does not inadvertently expose PII. Which TWO actions should the company take? (Choose TWO.)

Accepted Answer

Encrypt the training data in S3 using AWS KMS. Options A and D are correct. A: Encrypt the data at rest using KMS. D: Use SageMaker's data processing to redact PII before training. Option B (grant public access) is wrong. Option C (CloudTrail) is for auditing, not protection. Option E (server-side encryption with S3-managed keys) is less secure than KMS but still encryption; however, the question asks for TWO actions and A is the best encryption choice, while D directly addresses PII exposure.

Answer

Use server-side encryption with S3-managed keys

Answer

Enable AWS CloudTrail to log all access to the data

Answer

Grant public read access to the training data for faster access

Question 12

A data scientist is deploying a machine learning model on Amazon SageMaker for real-time inference. The model requires low-latency predictions and must be able to handle up to 1000 requests per second. Which TWO actions should the data scientist take to ensure the endpoint can meet the performance requirements? (Choose 2.)

Accepted Answer

Enable data capture to Amazon S3 for model monitoring and retraining.. Option B is correct because enabling data capture to S3 allows model monitoring and retraining. Option D is correct because auto scaling adjusts instances based on load. Option A is wrong because serverless inference has a cold start and max concurrency limits unsuitable for 1000 TPS. Option C is wrong because increasing instance size alone may not be cost-effective and auto scaling is better. Option E is wrong because multi-model endpoints share resources and may cause contention.

Answer

Use a multi-model endpoint to host multiple models on the same instance.

Answer

Use a serverless inference endpoint to automatically scale.

Answer

Deploy the model on a single large instance (e.g., ml.p3.16xlarge).

Question 13

A data scientist needs to perform hyperparameter optimization for a gradient boosting model. Which built-in Amazon SageMaker feature should they use?

Accepted Answer

Amazon SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning performs hyperparameter optimization. Option A is wrong because SageMaker Debugger is for monitoring. Option C is wrong because SageMaker Neo is for model compilation. Option D is wrong because SageMaker Clarify is for bias detection.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker Debugger

Answer

Amazon SageMaker Neo

Question 14

A data scientist is training a model on Amazon SageMaker and notices that the training job is taking much longer than expected. The instance type is ml.m5.xlarge and the dataset is 10 GB in CSV format. Which action is MOST likely to reduce training time without changing the instance type?

Accepted Answer

Convert the dataset to RecordIO or Parquet format before training.. Option B is correct because converting CSV to optimized formats like Parquet or RecordIO reduces I/O overhead and improves throughput. Option A is wrong because increasing batch size may help but does not address I/O. Option C is wrong because changing to a GPU instance is not allowed per stem. Option D is wrong because reducing epochs reduces accuracy, not training time effectively.

Answer

Change the instance type to ml.p3.2xlarge (GPU) for faster computation.

Answer

Reduce the number of training epochs to speed up convergence.

Answer

Increase the batch size to the maximum supported by the instance memory.

Question 15

Which THREE steps should be taken to secure a SageMaker notebook instance that accesses sensitive data? (Select THREE.)

Accepted Answer

Enable encryption at rest for the notebook's EBS volume. Using a VPC with no internet access keeps traffic private. IAM roles enforce least privilege access. Encryption at rest protects data on the notebook. Option D is wrong because root access should be disabled, not enabled. Option E is wrong because public internet access should be disabled for security.

Answer

Grant root access to the notebook instance for flexibility

Answer

Allow direct internet access from the notebook for downloading packages