MLS-C01 Machine Learning Implementation and Operations Practice Test 18 — 15 Questions

Question 1

An engineer sees the error in the exhibit when trying to deploy a model from a model registry in SageMaker. What is the MOST likely cause?

Accepted Answer

The model package version does not exist in the registry. The error in the exhibit indicates that the model package ARN does not exist in the registry. This occurs when the model package version has not been successfully created or registered, meaning it does not exist. Option B is therefore correct: the model package version does not exist. Option A would result in an access denied error, not this ARN-not-found error. Option C is incorrect because an 'Approved' status is actually required for deployment, so it would not cause this error. Option D would produce a different error about an existing endpoint configuration or conflict, not a missing ARN.

Answer

The IAM role lacks permission to access the model registry

Answer

The model package is still in 'Approved' status

Answer

The SageMaker endpoint is already deployed with the same model

Question 2

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. The error 'Cannot find inference code' occurs because SageMaker requires the `SAGEMAKER_PROGRAM` environment variable in the inference container to specify the entry-point script (e.g., `inference.py`) inside the `model.tar.gz`. Without this variable, SageMaker does not know which script to execute for inference, causing the deployment to fail. Option C correctly identifies this missing environment variable as the root cause.

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 3

A company is using Amazon SageMaker to train a large natural language processing model. The training job uses a GPU instance and is expected to take several hours. The data scientist wants to monitor GPU utilization in real-time. Which approach is MOST effective?

Accepted Answer

Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console. Amazon SageMaker automatically publishes GPU utilization metrics (e.g., `GPUUtilization`, `GPUMemoryUtilization`) to Amazon CloudWatch for training jobs running on GPU instances. By enabling CloudWatch metrics (which is the default behavior for SageMaker training jobs), the data scientist can view real-time GPU utilization directly in the CloudWatch console without any code modifications. This is the most effective approach because it requires no changes to the training script and provides native, real-time monitoring.

Answer

Use SageMaker Managed Spot Training to reduce cost and monitor utilization via spot instance status

Answer

Modify the training script to periodically log GPU utilization to a file in S3

Answer

Use SageMaker Debugger to capture GPU utilization tensors

Question 4

A data scientist needs to deploy a trained model to Amazon SageMaker for real-time inference. The model is stored as a .tar.gz file in Amazon S3. Which AWS service is used to create a SageMaker endpoint?

Accepted Answer

SageMaker Model and Endpoint Configuration. To create a SageMaker endpoint, you must first create a SageMaker Model (which points to the model artifact in S3 and the inference code) and then create an Endpoint Configuration (which specifies the model variant, instance type, and initial instance count). These are done using the SageMaker Model and Endpoint Configuration services. AWS Lambda, CloudFormation, and ECS are not directly used for creating the endpoint; they could be part of deployment automation but are not required.

Answer

AWS Lambda

Answer

AWS CloudFormation

Answer

Amazon ECS

Question 5

A machine learning engineer is using Amazon SageMaker to train a model. The training job is taking too long. The engineer suspects the data loading is a bottleneck. Which action would MOST effectively diagnose the issue?

Accepted Answer

Use SageMaker Debugger to profile the training job. SageMaker Debugger can profile the training job and identify bottlenecks. Option A may add overhead. Option B is not detailed. Option D is for inference.

Answer

Monitor CPU utilization in CloudWatch

Answer

Enable SageMaker Model Monitor

Answer

Increase the instance type to a larger one

Question 6

A company is using Amazon SageMaker to run a hyperparameter tuning job. The tuning job uses Bayesian optimization. Which THREE statements about Bayesian optimization are correct? (Choose THREE.)

Accepted Answer

It works well for continuous hyperparameters. Options B, C, and D are correct. Bayesian optimization uses past trial results to select hyperparameter combinations (C), it works well for continuous hyperparameters (B), and it typically finds optimal values in fewer trials than random search (D). Option A is false because Bayesian optimization can handle many hyperparameters, not just 5. Option E is false because Bayesian optimization generally requires fewer trials than grid search, not more.

Answer

It can only handle a maximum of 5 hyperparameters

Answer

It requires more trials than grid search to find optimal values

Question 7

A data scientist wants to deploy a PyTorch model for real-time inference. Which SageMaker deployment option provides the lowest latency for single-digit millisecond responses?

Accepted Answer

SageMaker Real-Time Inference endpoint. SageMaker Real-Time Inference endpoints (Option A) are optimized for low-latency, real-time predictions, often achieving single-digit millisecond response times because they maintain a persistent endpoint with pre-warmed instances. Option B (SageMaker Asynchronous Inference) is designed for non-real-time workloads with higher latency due to queuing. Option C (SageMaker Serverless Inference) can introduce cold starts and higher latency, especially for sporadic traffic. Option D (SageMaker Batch Transform) is for offline batch processing and not suitable for real-time inference.

Answer

SageMaker Asynchronous Inference

Answer

SageMaker Serverless Inference

Answer

SageMaker Batch Transform

Question 8

A team is training a large NLP model using SageMaker. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 61 GB GPU memory. Which action should the team take to resolve the issue without changing the model architecture?

Accepted Answer

Reduce the batch size in the training script. Reducing the batch size in the training script decreases GPU memory usage per iteration, which can resolve the OutOfMemory error without changing the model architecture. Option A is incorrect because switching to a regression model changes the problem type. Option B is incorrect because increasing the number of epochs does not affect memory per step. Option C is incorrect because Managed Warm Pools are for reducing cold start times, not for memory issues.

Answer

Switch to a regression model

Answer

Increase the number of epochs

Answer

Enable SageMaker Managed Warm Pools

Question 9

A company uses SageMaker Pipelines to automate model retraining. The pipeline fails intermittently at the Preprocess step with a 'ResourceLimitExceeded' error. The team uses a ml.m5.xlarge instance. What is the most likely cause?

Accepted Answer

The account has reached the limit for concurrent ml.m5.xlarge instances. The error indicates reaching a service quota. SageMaker has a default limit of concurrent training jobs per account. Option A is correct. Option B would cause different errors. Option C is unrelated. Option D would cause a SageMaker service error, not a resource limit.

Answer

The preprocessing script has a memory leak

Answer

The S3 bucket has insufficient permissions

Answer

The pipeline execution role is missing the PassRole permission

Question 10

A machine learning engineer needs to store and version datasets for reproducibility. Which AWS service is designed for this purpose?

Accepted Answer

SageMaker Feature Store. SageMaker Feature Store is designed to store, manage, and version features for machine learning, ensuring reproducibility. Option A (CodeCommit) is for version control of code. Option B (S3) is an object store not specialized for ML features. Option D (Redshift) is a data warehouse. Therefore, Option C is correct.

Answer

AWS CodeCommit

Answer

Amazon S3

Answer

Amazon Redshift

Question 11

A data scientist uses SageMaker to train a model. The training job takes 10 hours, but the team needs to reduce costs. Which approach is MOST cost-effective?

Accepted Answer

Enable Managed Spot Training. Managed Spot Training leverages Amazon EC2 Spot Instances, which offer spare compute capacity at up to 90% discount compared to On-Demand instances. This makes it the most cost-effective approach for reducing training costs. Option A is correct. Option B (using Automatic Model Tuning) is designed for hyperparameter optimization, not cost reduction, and may increase cost due to additional training jobs. Option C (using a larger instance type) finishes faster but at a higher per-hour cost, potentially increasing total cost. Option D (using Distributed Training with more instances) increases resource usage and cost, though it may reduce training time.

Answer

Use SageMaker Automatic Model Tuning

Answer

Use a larger instance type to finish faster

Answer

Use SageMaker Distributed Training with more instances

Question 12

A company deploys a SageMaker endpoint for real-time inference. After a week, the response latency increases from 50 ms to 500 ms. CPU utilization is at 30%. What is the most likely cause?

Accepted Answer

The inference code makes a call to a downstream service that is throttling requests. Increased latency with low CPU utilization indicates that the model itself is not compute-bound. Instead, the bottleneck is likely external, such as a downstream service (e.g., database, API) that the inference code calls. Throttling by that service causes requests to queue up, increasing latency without raising CPU usage on the SageMaker instance. Option A would typically cause memory pressure rather than low CPU. Option B would show high CPU if underpowered. Option D would cause connection errors, not just latency increase.

Answer

The model has a memory leak

Answer

The instance type is underpowered for the inference load

Answer

The SageMaker endpoint is experiencing a network outage

Question 13

A team needs to automatically retrain a model every week using new data. Which SageMaker feature is designed to schedule and automate this workflow?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines enables building, automating, and scheduling end-to-end ML workflows, making it suitable for weekly retraining. Option A is correct. Option B, SageMaker Automatic Model Tuning, is for hyperparameter optimization, not scheduling. Option C, SageMaker Model Monitor, is for monitoring model quality and drift, not scheduling. Option D, SageMaker Data Wrangler, is for data preparation and feature engineering, not scheduling.

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Model Monitor

Answer

SageMaker Data Wrangler

Question 14

A model deployed on a SageMaker endpoint is producing predictions that are consistently biased against a certain demographic. Which step should the team take FIRST to address this issue?

Accepted Answer

Use SageMaker Clarify to analyze bias in the training data and predictions. The first step is to analyze the data and model for bias. SageMaker Clarify can detect bias in training data and predictions, making option C the correct first step. Option A (SageMaker Model Monitor) tracks prediction quality but does not specifically analyze bias. Option B (switching algorithm) is a reactive change without understanding the bias source. Option D (retraining with balanced data) is a potential fix but should come after bias analysis.

Answer

Enable SageMaker Model Monitor to track prediction quality

Answer

Switch to a different algorithm that is less prone to bias

Answer

Retrain the model with balanced data

Question 15

A machine learning team is using SageMaker to train a model with a custom Docker container. The training script runs locally but fails on SageMaker with a 'Permission denied' error when writing to /opt/ml/model. What is the likely cause?

Accepted Answer

The container's user does not have write permission to /opt/ml/model. In SageMaker, the training container is expected to store the trained model artifacts in the /opt/ml/model directory. If the user running the training script inside the container does not have write permissions to that directory, the training will fail with a 'Permission denied' error. Option A is correct. Option B (image too large) would cause different errors, such as EBS volume limits. Option C refers to input data paths; the error is about writing the model, not reading inputs. Option D (S3 bucket) would cause read errors, not a write permission issue.

Answer

The Docker image is too large

Answer

The training script is trying to read from /opt/ml/input/data instead of /opt/ml/input/data/training

Answer

The training data is not in the correct S3 bucket