MLS-C01 Modeling Practice Test 32 — 15 Questions

Question 1

A machine learning engineer is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. The SageMaker training job fails with an AccessDenied error when trying to read the data. Which IAM policy addition should resolve the issue?

Accepted Answer

Add kms:Decrypt permission for the KMS key.. The correct answer is A because when an S3 bucket is encrypted with AWS KMS, the SageMaker training job's execution role must have the `kms:Decrypt` permission for the specific KMS key to read the encrypted objects. Without this permission, the job fails with an AccessDenied error, even if `s3:GetObject` is granted, because SageMaker must decrypt the data before reading it.

Answer

Add s3:GetObject permission for the bucket.

Answer

Add kms:GenerateDataKey permission for the key.

Answer

Attach the AmazonSageMakerFullAccess policy.

Question 2

A data scientist is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items, with a sparse user-item interaction matrix. The scientist wants to minimize training time on Amazon SageMaker. Which algorithm would be most appropriate?

Accepted Answer

Factorization Machines. Factorization Machines (B) are specifically designed for sparse, high-dimensional datasets like the user-item interaction matrix in recommendation systems. They extend matrix factorization by modeling pairwise feature interactions, which is ideal for collaborative filtering tasks. On Amazon SageMaker, the built-in Factorization Machines algorithm is optimized for sparse data and can train efficiently on 1 million users and 100,000 items, minimizing training time compared to general-purpose algorithms.

Answer

Linear Learner

Answer

K-Means

Answer

XGBoost

Question 3

A team is training a binary classifier and obtains a confusion matrix with 100 true positives, 10 false positives, 20 false negatives, and 200 true negatives. What is the precision of the model?

Accepted Answer

0.91. Precision is calculated as TP / (TP + FP). With 100 true positives and 10 false positives, precision = 100 / (100 + 10) = 100 / 110 ≈ 0.909, which rounds to 0.91. This metric measures how many of the positive predictions were actually correct.

Answer

0.87

Answer

0.94

Answer

0.83

Question 4

A data scientist is using Amazon SageMaker to train a model with a custom Docker container. The training job fails with an error: 'Container exited with code 137'. What is the most likely cause?

Accepted Answer

The training instance ran out of memory.. Exit code 137 (128+9) indicates the container was killed by the SIGKILL signal, which typically occurs when the Linux Out-Of-Memory (OOM) killer terminates a process that has exceeded its memory allocation. In Amazon SageMaker, training instances have finite memory, and if the training algorithm or data loading exceeds that limit, the OOM killer forcibly stops the container, resulting in exit code 137.

Answer

The training data was corrupted.

Answer

The training job exceeded the maximum runtime.

Answer

The Docker entrypoint script was not found.

Question 5

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model is a large ensemble that requires 4 GB of memory. The engineer wants to minimize cost while ensuring the endpoint can handle up to 100 concurrent requests with a latency under 200 ms. Which instance configuration is most appropriate?

Accepted Answer

One ml.c5.xlarge instance with auto-scaling up to 2 instances.. Option B is correct because the ml.c5.xlarge instance provides sufficient compute (4 vCPUs, 8 GB memory) for the 4 GB model, and auto-scaling up to 2 instances allows handling 100 concurrent requests with low latency while minimizing cost during low traffic. The ml.c5 family is optimized for compute-intensive inference, and auto-scaling ensures the endpoint scales out only when needed, avoiding over-provisioning.

Answer

Two ml.t3.medium instances behind a load balancer.

Answer

One ml.m5.2xlarge instance.

Answer

One ml.p3.2xlarge instance.

Question 6

Which TWO of the following are best practices for training deep learning models on Amazon SageMaker? (Select TWO.)

Accepted Answer

Use Pipe input mode to stream data directly from S3 to the algorithm.. Option B is correct because SageMaker's Pipe input mode streams training data directly from Amazon S3 to the algorithm without writing it to disk, reducing I/O latency and eliminating the need for large local storage. This is especially beneficial for deep learning models that iterate over large datasets, as it allows training to start faster and avoids the overhead of downloading data to EBS volumes.

Answer

Use SageMaker Processing to perform data augmentation before training.

Answer

Store training data on Amazon EBS volumes attached to the training instance.

Answer

Disable checkpointing to improve training speed.

Question 7

A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning (AMT). Which THREE hyperparameters should the scientist consider tuning to reduce overfitting? (Select THREE.)

Accepted Answer

Learning rate (eta). Learning rate (eta) controls the contribution of each tree to the ensemble. A lower learning rate forces the model to learn more slowly, requiring more trees but reducing the risk of overfitting by preventing any single tree from having too much influence on the final prediction.

Answer

Subsample ratio

Answer

Maximum depth (max_depth)

Question 8

Which TWO of the following are valid Amazon SageMaker built-in algorithms for regression tasks? (Select TWO.)

Accepted Answer

XGBoost. XGBoost is a valid Amazon SageMaker built-in algorithm for regression tasks because it supports regression objectives such as 'reg:squarederror' and 'reg:logistic'. It is a gradient boosting framework that builds an ensemble of decision trees, making it suitable for both regression and classification problems.

Answer

BlazingText

Answer

Image Classification

Answer

Object Detection

Question 9

Refer to the exhibit. The log shows the end of a successful SageMaker training job. However, the ML engineer cannot find the model artifacts in the specified S3 bucket. What is the most likely cause?

Accepted Answer

The IAM role used by the training job does not have permission to write to the S3 bucket.. The training job completed successfully, meaning the SageMaker training container executed without errors. However, if the model artifacts are not found in the specified S3 bucket, the most likely cause is that the IAM role associated with the training job lacks the necessary s3:PutObject permission for that bucket. SageMaker uses the role's credentials to write the output; without write access, the artifacts are silently dropped or fail to upload, even though the training code itself may have run to completion.

Answer

The S3 bucket does not exist.

Answer

The model artifacts were uploaded to a different S3 path.

Answer

The training job did not have network access to S3.

Question 10

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. A data scientist is trying to invoke the endpoint 'my-endpoint' from the notebook but receives an AccessDenied error. What is the likely cause?

Accepted Answer

The policy is not attached to the IAM role used by the notebook instance.. Option D is correct because the error 'AccessDenied' when invoking a SageMaker endpoint from a notebook instance typically indicates that the IAM role attached to the notebook does not have the required permissions. The policy shown in the exhibit grants sagemaker:InvokeEndpoint for the specific endpoint ARN, but if the policy is not attached to the IAM role that the notebook instance is using, the role lacks the permission, resulting in the AccessDenied error. Attaching the policy to the correct IAM role resolves the issue.

Answer

The policy allows InvokeEndpoint only for endpoints with the exact ARN, but the endpoint ARN is different.

Answer

The policy uses a wildcard for CreateEndpoint, which is too permissive.

Answer

The policy does not allow sagemaker:CreateEndpoint for the specific endpoint.

Question 11

Refer to the exhibit. The training job 'my-job' failed with the error 'Unable to pull image from ECR'. What is the most likely cause?

Accepted Answer

The IAM role does not have permission to pull images from the ECR repository.. The error 'Unable to pull image from ECR' indicates that the SageMaker training job could not retrieve the custom Docker image stored in Amazon ECR. The most likely cause is that the IAM role associated with the training job lacks the `ecr:GetDownloadUrlForLayer` and `ecr:BatchGetImage` permissions required to pull images from the ECR repository. Without these permissions, SageMaker cannot authenticate and download the container image, even if the repository and image exist.

Answer

The instance type ml.m5.large does not support custom images.

Answer

The S3 bucket for training data is in a different account.

Answer

The role ARN is incorrect.

Question 12

A data scientist is training a binary classification model on a highly imbalanced dataset (99% negative class, 1% positive class). The model currently achieves 99% accuracy but only identifies 0.5% of true positives. Which metric should the data scientist focus on to improve model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the dataset is highly imbalanced (99% negative, 1% positive) and the model fails to detect most positives (only 0.5% true positives). Improving recall directly addresses the model's inability to capture the minority class, even if it reduces precision or accuracy. In binary classification with severe class imbalance, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class, as seen here.

Answer

Precision

Answer

Root Mean Squared Error (RMSE)

Answer

Accuracy

Question 13

A company is deploying a real-time fraud detection model using Amazon SageMaker. The model must make predictions in under 100 milliseconds. The data scientist uses a pre-trained XGBoost model and deploys it to a SageMaker endpoint with an ml.c5.xlarge instance. After load testing, the average latency is 150 ms. Which action should the data scientist take to reduce latency?

Accepted Answer

Enable SageMaker Neo to compile the model for the target instance. Option C is correct because SageMaker Neo optimizes trained models for the target hardware platform by compiling them into an efficient runtime. This reduces inference latency without changing the model architecture, making it ideal for meeting the sub-100ms requirement when the current latency is 150ms on an ml.c5.xlarge instance.

Answer

Reduce the number of trees in the XGBoost model

Answer

Deploy multiple instances behind a load balancer

Answer

Use a larger instance type to increase compute capacity

Question 14

A data scientist is building a recommendation system for an e-commerce platform using Amazon SageMaker. The system needs to provide personalized product recommendations based on user purchase history and product metadata. The dataset contains 10 million users and 1 million products. Which algorithm should the data scientist use as the core of the recommendation engine?

Accepted Answer

Factorization Machines. Factorization Machines (FM) are specifically designed for high-dimensional sparse data like user-item interactions, making them ideal for recommendation systems with 10 million users and 1 million products. FM can capture pairwise feature interactions (e.g., user-product affinities) efficiently using factorized parameters, which scales well to large datasets and supports personalized recommendations from purchase history and metadata.

Answer

Linear Learner

Answer

XGBoost

Answer

K-Means

Question 15

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to use distributed training across multiple GPUs to speed up the process. Which SageMaker feature should the data scientist use?

Accepted Answer

SageMaker Distributed Training Libraries. SageMaker Distributed Training Libraries provide optimized implementations of data parallelism and model parallelism that automatically partition the model and data across multiple GPUs, reducing training time for deep learning models. This is the correct choice because the question specifically asks for a feature to enable distributed training across multiple GPUs, which is exactly what these libraries are designed for.

Answer

SageMaker Managed Spot Training

Answer

SageMaker Hyperparameter Tuning

Answer

SageMaker Automatic Model Tuning