MLS-C01 Mock Exam 89 — 25 Questions

Question 1

Refer to the exhibit. A data scientist wants to use SageMaker to train a model using data stored in 'my-bucket'. The training job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The training job is not requesting server-side encryption with AES256. Option C is correct because SageMaker training jobs require server-side encryption with AES256 when the S3 bucket uses default encryption with AES256. If the training job does not explicitly request SSE-S3 (AES256) in its S3 data source configuration, the S3 service denies access, resulting in an 'access denied' error even if the IAM role has full S3 permissions.

Answer

The bucket uses AWS KMS key encryption instead of AES256

Answer

The bucket name in the policy does not match the actual bucket name

Answer

The bucket is publicly accessible but the IAM role lacks permissions

Question 2

A data scientist is using SageMaker to train an XGBoost model for regression. The training data contains categorical features with high cardinality (e.g., zip code with over 10,000 unique values). Which feature engineering approach is MOST appropriate to avoid overfitting while preserving predictive power?

Accepted Answer

Use target encoding with smoothing. Target encoding with smoothing is the most appropriate approach because it replaces each high-cardinality category with the mean of the target variable for that category, regularized by a smoothing factor that pulls estimates toward the global mean. This preserves predictive power by capturing the relationship between the category and the target while preventing overfitting on rare categories that have few samples. In SageMaker XGBoost, this avoids the curse of dimensionality from one-hot encoding and the arbitrary ordering from label encoding.

Answer

One-hot encode the categorical features

Answer

Apply frequency encoding based on category occurrence

Answer

Label encode the categorical features

Question 3

A data scientist ran an XGBoost training job on Amazon SageMaker using a CSV dataset. The training job failed with the error shown. What is the most likely cause of this failure?

Accepted Answer

One of the rows in the CSV has an extra column. XGBoost on SageMaker expects the CSV input to have a consistent number of columns across all rows. If one row contains an extra column, the parser will fail because it cannot map the additional field to the feature schema defined by the training job. This mismatch causes the 'Error: Number of columns does not match' or similar parsing error.

Answer

The dataset includes a header row

Answer

The delimiter used in the CSV is not a comma

Answer

The dataset contains missing values

Question 4

A company uses SageMaker to deploy a model for predicting customer churn. The model was trained on historical data and achieves 85% accuracy on the test set. After deployment, the model's predictions are significantly worse on new data due to changes in customer behavior. What is the MOST likely cause?

Accepted Answer

Concept drift in the underlying data distribution. The model's performance degradation on new data, despite high accuracy on the test set, is a classic symptom of concept drift. Concept drift occurs when the statistical properties of the target variable (customer churn) change over time due to shifts in customer behavior, making the trained model's decision boundary obsolete. SageMaker deployed the model as a persistent endpoint, but the underlying data distribution has evolved, so the model no longer generalizes to the current environment.

Answer

Data leakage during training

Answer

The training dataset was too small

Answer

The model is overfitting to the training data

Question 5

A company is using SageMaker to deploy a real-time inference endpoint for a natural language processing model. The model receives input text and returns predictions. The data scientist notices that the endpoint latency increases significantly under load. Which design change would MOST effectively reduce latency?

Accepted Answer

Use an inference pipeline to combine preprocessing and model inference. Option D is correct because an inference pipeline in SageMaker allows you to chain preprocessing logic directly with the model inference within the same endpoint container. This eliminates the need for separate Lambda functions or client-side preprocessing, which reduces network round-trips and serialization overhead, thereby lowering latency under load.

Answer

Enable data capture for monitoring

Answer

Switch to batch transform for real-time predictions

Answer

Increase the number of instances behind the endpoint

Question 6

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 7

A data scientist is training a binary classification model on a dataset where the positive class represents only 1% of the data. The model's accuracy is 99%, but the recall for the positive class is 0%. Which metric should the scientist use to evaluate the model's performance effectively?

Accepted Answer

Area under the Precision-Recall curve (PR AUC). In a highly imbalanced dataset where the positive class is only 1%, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the negative class for all samples, resulting in 0% recall for the positive class. The Area under the Precision-Recall curve (PR AUC) is the correct metric because it focuses on the performance of the positive class by evaluating the trade-off between precision and recall, making it sensitive to changes in the minority class. Unlike ROC AUC, which can be overly optimistic in imbalanced settings due to the large number of true negatives, PR AUC provides a more realistic assessment of model performance for rare events.

Answer

Area under the ROC curve (ROC AUC)

Answer

Accuracy

Answer

F1 score

Question 8

A machine learning engineer is deploying a model on Amazon SageMaker. Which TWO steps are required to create a SageMaker endpoint?

Accepted Answer

Create a SageMaker model. A is correct because creating a SageMaker model is the first required step to define the model artifacts, inference code, and container image that will be used for predictions. Without a model object, SageMaker has no executable artifact to deploy behind the endpoint.

Answer

Submit a training job

Answer

Create a SageMaker pipeline

Answer

Create a SageMaker notebook instance

Question 9

A company is building a recommender system using matrix factorization. The dataset contains user-item interactions. The model is trained on a large dataset, but the recommendations for new users are poor. Which approach would MOST effectively address this cold-start problem?

Accepted Answer

Incorporate user demographic features as side information. Matrix factorization models learn latent factors only from user-item interactions. For new users with no history, the model cannot compute a meaningful latent vector, leading to poor recommendations. Incorporating user demographic features as side information allows the model to initialize or infer latent factors for new users based on their attributes, directly addressing the cold-start problem.

Answer

Switch to item-based collaborative filtering only

Answer

Increase the number of latent factors in the model

Answer

Use only implicit feedback signals for training

Question 10

A company wants to build a machine learning model to predict customer churn. The dataset includes customer demographics, usage patterns, and support interactions. The data is stored in Amazon S3. The data scientist needs to perform feature engineering, including creating aggregate features from support interactions and encoding categorical variables. Which AWS service is most suitable for building the feature engineering pipeline?

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing is the most suitable service because it is purpose-built for data preprocessing and feature engineering within the SageMaker ecosystem. It allows you to run custom Python scripts (e.g., using pandas or PySpark) on managed infrastructure to create aggregate features from support interactions and encode categorical variables, and it integrates seamlessly with SageMaker for model training and deployment.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Batch

Question 11

A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model requires low latency (<100 ms) and the team expects a steady stream of requests with occasional spikes. Which instance type and deployment strategy should they use to minimize cost while meeting latency requirements?

Accepted Answer

Use ml.c5 instances with a single endpoint and provisioned concurrency.. Option D is correct because ml.c5 instances are compute-optimized for low-latency inference, and provisioned concurrency pre-warms the endpoint to handle steady traffic with spikes without cold starts, meeting the <100 ms requirement cost-effectively. This combination avoids over-provisioning while ensuring consistent performance.

Answer

Use ml.p3 instances with a multi-model endpoint.

Answer

Use AWS Lambda with a container image for serverless inference.

Answer

Use ml.m5 instances with a production variant and auto-scaling.

Question 12

A machine learning engineer is trying to deploy a model using a SageMaker endpoint but receives an access denied error. The IAM policy attached to the role is shown in the exhibit. What is the MOST likely cause of the error?

Accepted Answer

The policy does not include iam:PassRole.. The error occurs because the IAM role used by SageMaker does not have the iam:PassRole permission, which is required to allow SageMaker to assume the role and access the necessary resources (e.g., S3 buckets, EC2 instances) during endpoint deployment. Without this permission, SageMaker cannot pass the role to the service, resulting in an access denied error even if other SageMaker actions are allowed.

Answer

The policy does not include sagemaker:CreateEndpoint.

Answer

The policy does not specify resource ARNs.

Answer

The policy does not include sagemaker:InvokeEndpoint.

Question 13

A data scientist is training a deep learning model for object detection. The training loss decreases rapidly in the first few epochs but then plateaus at a high value. The validation loss starts increasing after a few epochs. Which adjustment is MOST likely to improve generalization?

Accepted Answer

Implement early stopping with a patience parameter. The described behavior—training loss plateauing at a high value while validation loss increases—is a classic sign of overfitting. Early stopping with a patience parameter halts training when validation performance stops improving, preventing the model from memorizing noise and thus improving generalization. This directly addresses the overfitting without altering the model architecture or data distribution.

Answer

Add more convolutional layers

Answer

Use more aggressive data augmentation

Answer

Increase the learning rate

Question 14

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 15

A machine learning team is using Amazon SageMaker to tune hyperparameters for a neural network. They have defined a hyperparameter tuning job with a random search strategy. The training time per job is very long. Which strategy can reduce the total tuning time?

Accepted Answer

Enable early stopping to terminate poorly performing jobs.. Enabling early stopping allows SageMaker to terminate training jobs that are unlikely to produce better results based on the objective metric, which directly reduces total tuning time by freeing up compute resources for more promising hyperparameter combinations. This is especially effective with random search, where many trials may converge slowly or plateau.

Answer

Use a larger instance type for each training job.

Answer

Switch to Bayesian optimization.

Answer

Increase the number of training jobs.

Question 16

A data scientist is training a random forest model for a binary classification task. The dataset has 100,000 samples and 500 features. The model is overfitting. Which TWO actions are MOST likely to reduce overfitting?

Accepted Answer

Reduce the maximum depth of each tree. Reducing the maximum depth of each tree limits the complexity of individual trees, preventing them from memorizing noise and specific patterns in the training data. This is a standard regularization technique for random forests that directly combats overfitting by controlling the variance of the model.

Answer

Increase the number of trees in the forest

Answer

Increase the number of features considered at each split

Answer

Use all features for each tree

Question 17

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 18

A data scientist is training a classification model on a dataset with missing values in several features. The data scientist wants to use SageMaker to train the model. Which TWO approaches can the data scientist use to handle missing data within the SageMaker training pipeline? (Choose two.)

Accepted Answer

Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default.. Option A is correct because the SageMaker built-in XGBoost algorithm has a built-in mechanism to handle missing values by default. It learns the best direction (left or right branch) to route missing values during training, so no explicit imputation is needed. This makes it a seamless choice for datasets with missing data within the SageMaker training pipeline.

Answer

Use the SageMaker BlazingText algorithm, which automatically imputes missing values.

Answer

Use SageMaker Inference Pipeline to handle missing values at inference time.

Answer

Use SageMaker PCA algorithm, which automatically handles missing values.

Question 19

A machine learning team is using Amazon SageMaker to train a linear regression model. The team notices that the training loss decreases rapidly initially but then plateaus at a high value. What is the MOST likely cause?

Accepted Answer

The learning rate is set too high. A learning rate set too high causes the optimizer to take excessively large steps, overshooting the minimum of the loss function. This results in rapid initial decrease as the model makes large corrections, but then the loss plateaus at a high value because the parameters oscillate around the optimum without converging. In SageMaker's linear regression (typically using stochastic gradient descent), a high learning rate prevents fine-grained convergence, leading to a high plateau.

Answer

The model uses batch normalization

Answer

The learning rate is set too low

Answer

The model is over-regularized with L2 regularization

Question 20

A team is training a linear regression model to predict house prices. After training, they observe that the model has high bias (underfitting). Which action is most likely to reduce bias?

Accepted Answer

Add more relevant features and increase model complexity.. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more relevant features and increasing model complexity (e.g., using polynomial features or more interaction terms) gives the linear regression model greater capacity to fit the training data, directly reducing bias. This aligns with the bias-variance tradeoff, where increasing complexity lowers bias at the cost of potentially increasing variance.

Answer

Increase the regularization strength.

Answer

Reduce the amount of training data.

Answer

Decrease the number of model parameters.

MLS-C01 Mock Exam 89 — 25 Questions

Exhibit

MLS-C01 Mock Exam 89 — 25 Questions

Exhibit