MLS-C01 Practice Test 13 — 15 Questions

Question 1

A data scientist is working on a project to predict customer churn. The dataset contains 50,000 rows and 20 features, including categorical variables like 'Region' (10 categories) and 'SubscriptionType' (5 categories). The target variable is binary (churn or not). During exploratory data analysis, they plot the distribution of each feature and notice that 'Region' has a highly imbalanced distribution: one region accounts for 80% of the data. Which of the following is the most appropriate next step?

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

Accepted Answer

Group rare categories into an 'Other' category.. Grouping rare categories into an 'Other' category helps manage highly imbalanced categorical features, preventing the model from overemphasizing the dominant category and allowing rare categories to be represented without causing sparse or noisy signals. Option A is incorrect: one-hot encoding does not address the imbalance; it simply creates dummy variables, and rare categories would still be underrepresented. Option B is incorrect: removing the 'Region' feature could discard potentially useful information; the problem is imbalance, not irrelevance. Option D is incorrect: oversampling the minority class targets address target imbalance, not feature imbalance.

Answer

Apply one-hot encoding to the 'Region' feature.

Answer

Remove the 'Region' feature from the dataset.

Answer

Oversample the minority classes in the target variable.

Question 2

Which TWO steps are required to set up cross-account access to an Amazon S3 data lake for AWS Glue jobs running in a different AWS account? (Choose two.)

Accepted Answer

Add a bucket policy to the S3 bucket that grants access to the Glue service role from the other account.. An S3 bucket policy can grant cross-account access by specifying the AWS account ID of the second account as the principal, allowing the Glue service role from that account to read objects. This is a standard method for delegating access to S3 resources across accounts without requiring IAM roles in the source account.

Answer

Create a cross-account Glue crawler in the source account.

Answer

Set up VPC peering between the two accounts' VPCs.

Answer

Ensure both accounts are in the same AWS organization.

Question 3

A data analyst is performing EDA on a dataset containing timestamps of user logins. They want to understand daily login patterns. The timestamp column is in Unix epoch format (integer). Which of the following is the most appropriate transformation to extract day-of-week patterns?

Accepted Answer

Convert the timestamps to datetime objects and extract the day-of-week.. Converting Unix epoch timestamps to datetime objects allows extraction of the day-of-week using functions like .dt.dayofweek() in pandas. Option B is wrong because converting to string and splitting into date and time does not directly give day-of-week patterns and loses temporal properties. Option C is wrong because min-max scaling is used for normalizing numerical features, not for extracting temporal patterns. Option D is wrong because binning into 1-hour intervals captures hourly patterns, not day-of-week patterns.

Answer

Convert the timestamps to string and split into date and time.

Answer

Apply min-max scaling to the timestamp values.

Answer

Bin the timestamps into 1-hour intervals.

Question 4

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job uses a hyperparameter tuning job to optimize hyperparameters. The tuning job has been running for 3 hours and has completed 20 training jobs. The data scientist wants to stop the tuning job early if it is not making progress. What should the data scientist do to accomplish this?

Accepted Answer

Configure the tuning job with early stopping enabled.. SageMaker's automatic model tuning supports early stopping. When enabled, the tuning job stops automatically if no significant improvement is observed. Option B is incorrect because CloudWatch alarms can monitor metrics but cannot directly stop a tuning job; they can trigger actions like notifications but not stop the tuning. Option C is incorrect because SageMaker Experiments is for tracking experiments, not for stopping tuning jobs. Option D is incorrect because SageMaker Debugger stops individual training jobs, not the hyperparameter tuning job itself; early stopping is a built-in feature of the tuning job.

Answer

Set up a CloudWatch alarm to stop the tuning job if a metric does not improve.

Answer

Use SageMaker Experiments to monitor and manually stop the tuning job.

Answer

Use SageMaker Debugger to stop training jobs that are not improving.

Question 5

A data scientist is performing EDA on a dataset that contains customer demographics and purchase history. The dataset has a column 'age' with some values that are negative or unreasonably high (e.g., 200). The scientist wants to identify and handle these outliers. The scientist is using a SageMaker notebook with pandas. Which approach should the scientist take to effectively handle these outliers?

Accepted Answer

Define reasonable bounds based on domain knowledge and filter or cap the outliers. The most appropriate approach is to define reasonable bounds based on domain knowledge (e.g., 0-120) and filter out or cap the outliers. Option A is incorrect because standard scaling does not handle outliers; it will still be influenced by extreme values. Option B is incorrect because imputing with the mean can distort the distribution when outliers are present. Option D is incorrect because removing the entire column discards valuable information.

Answer

Apply standard scaling to the 'age' column

Answer

Impute the outlier values with the mean of the column

Answer

Remove the 'age' column entirely

Question 6

A machine learning team is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). They want to understand the distribution and relationships before modeling. Which exploratory data analysis technique is most appropriate to visualize the imbalance and guide resampling strategy?

Accepted Answer

Bar chart of class frequencies and a correlation heatmap. A bar chart of class frequencies clearly visualizes the imbalance (99% negative vs 1% positive), and a correlation heatmap helps identify which features are correlated with the target, guiding resampling strategy. Option A is wrong because a confusion matrix is used for evaluating model predictions, not for initial exploratory data analysis of class imbalance. Option B is wrong because a scatterplot matrix is designed to visualize relationships between continuous variables and can be overwhelming with many features; it does not directly highlight the class imbalance. Option C is wrong because box plots grouped by target class show feature distributions across classes but do not explicitly quantify the imbalance ratio itself.

Answer

Confusion matrix on a sample of the data

Answer

Scatterplot matrix of all features colored by class

Answer

Box plots of each feature grouped by the target class

Question 7

A data scientist is building a model to predict customer churn. The dataset includes both numerical features (e.g., account age, usage minutes) and categorical features (e.g., region, plan type). The data scientist wants to use a linear classifier. Which feature engineering step is required before training?

Accepted Answer

One-hot encode categorical features. Linear classifiers (e.g., logistic regression, linear SVM) require numerical input and cannot directly process categorical text labels. One-hot encoding converts each categorical feature into binary indicator columns, allowing the linear model to learn separate weights for each category. Without this step, the model would either fail to train or treat categorical strings as ordinal values, which is mathematically invalid for linear decision boundaries.

Answer

Normalize numerical features

Answer

Impute missing values

Answer

Remove outliers

Question 8

Which TWO of the following are appropriate use cases for using Amazon SageMaker BlazingText? (Choose 2)

Accepted Answer

Text classification using supervised learning.. Amazon SageMaker BlazingText supports text classification using supervised learning. Option C is correct because BlazingText can learn word embeddings (e.g., Word2Vec) from large text corpora. Option B is incorrect because time series forecasting is not a capability of BlazingText; it is suited for NLP tasks. Option D is incorrect because BlazingText does not support image classification—that would require a different service or algorithm. Option E is incorrect because sequence-to-sequence translation is not supported by BlazingText; it is designed for word-level embeddings and text classification.

Answer

Time series forecasting.

Answer

Classifying images.

Answer

Sequence-to-sequence translation.

Question 9

A company is building a recommendation system for an e-commerce platform. The data includes user-item interactions and features such as user demographics and item categories. Which algorithm would be most appropriate for generating personalized recommendations?

Accepted Answer

Factorization Machines. Factorization Machines (FM) are specifically designed for recommendation tasks with sparse, high-dimensional data like user-item interactions. They model pairwise feature interactions (e.g., user demographics × item categories) using factorized parameters, enabling personalized recommendations even when many user-item pairs are unobserved. This makes FM far more effective than tree-based or clustering methods for collaborative filtering and feature-rich recommendation scenarios.

Answer

XGBoost

Answer

k-means clustering

Answer

Principal Component Analysis (PCA)

Question 10

A bank is building a credit risk model using a large dataset with 500 features and 2 million samples. The dataset contains many categorical features with high cardinality (e.g., zip code, occupation). The model must be deployed on SageMaker and provide real-time predictions with low latency. They also need to explain individual predictions for regulatory compliance. Which approach is most appropriate?

Accepted Answer

Use a gradient boosting model with ordinal encoding for categorical features and use SageMaker's built-in XGBoost with SHAP. XGBoost with ordinal encoding and SHAP balances performance, latency, and explainability.

Answer

Use a linear model with target encoding for categorical features and deploy with SageMaker's built-in linear learner algorithm

Answer

Use a deep neural network with embedding layers for categorical features and use SageMaker's built-in Debugger for explanations

Answer

Use XGBoost with one-hot encoding for categorical features and deploy with SageMaker's built-in SHAP explainer

Question 11

A machine learning team is training a deep learning model on Amazon SageMaker and notices that the training loss is decreasing but the validation loss is increasing. What is the most likely cause?

Accepted Answer

Overfitting the training data. When training loss continues to decrease while validation loss increases, the model is memorizing the training data rather than learning generalizable patterns. This is the classic symptom of overfitting, where the model's capacity exceeds what is needed for the underlying data distribution, causing it to fit noise in the training set. In Amazon SageMaker, this can be observed by monitoring the validation loss metric during training jobs.

Answer

Vanishing gradients

Answer

Learning rate is too high

Answer

Underfitting the training data

Question 12

Refer to the exhibit. A company is using an IAM role with the attached policy to deploy a SageMaker model. The data scientist can create training jobs and models, but when trying to create an endpoint, they receive an access denied error. What is the missing permission?

Accepted Answer

ec2:CreateNetworkInterface. When SageMaker creates an endpoint, it provisions a network interface in the customer's VPC to route traffic to the endpoint instances. The IAM role must have the ec2:CreateNetworkInterface permission to allow SageMaker to create this ENI; without it, the endpoint creation fails with an access denied error.

Answer

cloudwatch:PutMetricData

Answer

iam:PassRole

Answer

sagemaker:InvokeEndpoint

Answer

kms:Decrypt

Question 13

Which THREE techniques can help reduce overfitting in a neural network trained on a small dataset?

Accepted Answer

Apply L2 weight regularization. L2 weight regularization (also known as weight decay) penalizes large weights by adding a term to the loss function proportional to the sum of squared weights. This forces the network to learn simpler patterns and reduces sensitivity to noise in the training data, which is especially helpful when the dataset is small and prone to overfitting.

Answer

Increase the number of hidden layers

Answer

Train for more epochs

Question 14

Which THREE factors should be considered when choosing between a parametric and a non-parametric machine learning model?

Accepted Answer

Non-parametric models are generally more flexible. Non-parametric models, such as k-nearest neighbors or decision trees, do not assume a fixed functional form for the data, allowing them to capture complex, non-linear relationships. This flexibility makes them well-suited for datasets where the underlying distribution is unknown or highly irregular, but it also increases the risk of overfitting if not properly regularized.

Answer

Parametric models have lower bias than non-parametric models

Answer

Non-parametric models are less prone to overfitting

Question 15

Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to their role. What is the most likely reason for the error?

Accepted Answer

The policy only allows CreateTrainingJob when the training job status is 'Failed', which is never true initially. The IAM policy uses a `Condition` block with `sagemaker:TrainingJobStatus` set to `Failed`. When a `CreateTrainingJob` API call is made, the training job status is not yet set (it is `Creating` or `InProgress`), so the condition evaluates to false, and the request is denied. The policy only grants permission when the status equals `Failed`, which never occurs at creation time.

Answer

The Action is not allowed because 'CreateTrainingJob' is misspelled

Answer

There is an explicit deny in another policy

Answer

The Resource is set to '*' which does not include the specific training job ARN