MLS-C01 Practice Test 8 — 10 Questions

Question 1

A data engineer needs to run a one-time ETL job to transform 500 GB of data from Amazon RDS to Amazon S3. The job should be cost-effective and require minimal infrastructure management. Which AWS service should be used?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it is a fully managed, serverless ETL service designed for one-time or scheduled data transformation jobs. It automatically provisions and scales the underlying Spark environment, requires no infrastructure management, and charges only for the resources consumed during job execution, making it highly cost-effective for a 500 GB ETL workload from RDS to S3.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

AWS Data Pipeline

Question 2

A data scientist wants to visualize the correlation between a continuous feature and a binary target variable. Which plot is most appropriate?

Accepted Answer

Box plot of the feature grouped by target class. A box plot displays the distribution of the continuous feature for each category of the binary target, allowing easy comparison of medians, spreads, and outliers. This reveals correlation: if the distributions differ notably between classes, the feature is likely correlated with the target. Option A is wrong because a scatter plot is typically used for two continuous variables, not a binary target. Option B is wrong because a histogram shows the distribution of a single continuous variable, ignoring the target. Option D is wrong because a bar chart of target class counts only shows class frequency, not the relationship with the feature.

Answer

Scatter plot with feature on x-axis and target on y-axis

Answer

Histogram of the feature

Answer

Bar chart of target class counts

Question 3

A data scientist is building a recommendation system for an e-commerce platform. The dataset includes user-item interactions (clicks, purchases, ratings). The scientist wants to use matrix factorization. Which approach is most appropriate for handling implicit feedback (e.g., clicks) rather than explicit ratings?

Accepted Answer

Use weighted alternating least squares (WALS) with confidence weights. Weighted Alternating Least Squares (WALS) is specifically designed for implicit feedback scenarios because it treats unobserved interactions as negative signals with low confidence, rather than missing values. By assigning confidence weights (e.g., based on click frequency or dwell time), WALS can factorize the implicit feedback matrix effectively, avoiding the bias introduced by treating all zeros as true negatives.

Answer

Use k-means clustering to segment users and then use item popularity within clusters

Answer

Use singular value decomposition (SVD) on the interaction matrix with missing values filled with 0

Answer

Use a deep neural network with a softmax output to predict item probabilities

Question 4

A data scientist is using Amazon SageMaker to train a model. The training job fails with an error 'Insufficient instance capacity'. Which action should the scientist take to resolve this?

Accepted Answer

Retry with a different instance type or Availability Zone. Retrying with a different instance type or in a different Availability Zone often resolves capacity issues. Option A is incorrect because the error is about capacity, not IAM permissions. Option C is incorrect because increasing the number of instances may exacerbate the capacity issue. Option D is incorrect because reducing the training data size does not affect instance capacity.

Answer

Check IAM permissions

Answer

Increase the number of instances

Answer

Reduce the training data size

Question 5

A data scientist is working with a dataset containing text reviews. The goal is to classify sentiment. During EDA, they compute the word frequency distribution. They notice that the most frequent words are common stop words like 'the', 'and', 'a'. Which action should they take to improve the feature representation for modeling?

Accepted Answer

Remove the stop words from the text before creating the bag-of-words representation.. Stop words like 'the', 'and', and 'a' carry little semantic meaning for sentiment classification and dominate the bag-of-words feature space, adding noise and dimensionality without discriminative value. Removing them before vectorization reduces feature count, speeds training, and lets the model focus on sentiment-bearing terms like 'excellent', 'terrible', or 'disappointing'. This is a standard text preprocessing step in NLP pipelines for sentiment analysis.

Answer

Use n-grams instead of unigrams to capture phrase patterns.

Answer

Add more stop words to the default list to remove even more common words.

Answer

Apply stemming to reduce words to their root forms.

Question 6

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 7

The Glue job my-glue-job fails after a few successful runs. The error log shows 'Job run exceeds max concurrent runs limit'. The CloudFormation template is shown in the exhibit. What change should be made to allow multiple runs to execute concurrently?

Accepted Answer

Set the MaxConcurrentRuns property to 3. The error 'Job run exceeds max concurrent runs limit' indicates that the AWS Glue job is configured to allow fewer concurrent runs than are being attempted. The MaxConcurrentRuns property in the job definition controls how many runs of the same job can execute simultaneously. Setting it to 3 would allow up to three concurrent runs, resolving the error.

Answer

Change the IAM role to one with more permissions

Answer

Increase the MaxRetries property to 3

Answer

Remove the --job-bookmark-option argument

Question 8

A company wants to use SageMaker to host multiple models behind a single endpoint to reduce costs. Which SageMaker feature should they use?

Accepted Answer

SageMaker Multi-Model Endpoints. SageMaker Multi-Model Endpoints allow you to deploy multiple models behind a single endpoint, each loaded dynamically from Amazon S3 based on the inference request. This reduces hosting costs by sharing a single instance across many models, as only the models that are actively invoked consume memory. The correct answer is E because this feature is specifically designed for cost-efficient multi-model hosting.

Answer

SageMaker Elastic Inference

Answer

SageMaker inference pipeline

Answer

SageMaker batch transform

Answer

SageMaker multi-container endpoints

Question 9

Which THREE of the following are valid approaches for deploying a machine learning model to an Amazon SageMaker endpoint for real-time inference?

Accepted Answer

Use a SageMaker Inference Pipeline with multiple containers. Option A is correct because a SageMaker Inference Pipeline lets you chain multiple containers (for example, a preprocessing container plus a model container) behind a single real-time endpoint, which is a supported deployment approach for real-time inference. Option B is correct because SageMaker provides pre-built containers for built-in algorithms, and you can deploy those directly to a real-time endpoint without building custom images. Option E is correct because SageMaker supports bringing your own Docker container that conforms to the SageMaker inference container contract, allowing custom models to be hosted on a real-time endpoint. Option C is not correct because Amazon EMR is a big data processing service for running frameworks like Spark and Hadoop, not a service for hosting SageMaker real-time inference endpoints. Option D is not correct because AWS Lambda runs function code in response to events and is not the mechanism for deploying a model to a SageMaker endpoint, even though Lambda can invoke a SageMaker endpoint.

Answer

Use Amazon EMR to host the model

Answer

Deploy the model as an AWS Lambda function

Question 10

A data scientist builds a Random Forest model using SageMaker. The model performs well on training data but poorly on test data. Which step is 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 decision trees, preventing them from memorizing noise and specific patterns in the training data. This directly addresses overfitting by enforcing simpler, more generalized splits, which improves performance on unseen test data.

Answer

Increase the number of trees

Answer

Switch to a linear model

Answer

Increase the number of features considered at each split