MLS-C01 Practice Test 3 — 30 Questions

Question 1

A machine learning team is deploying a time-series forecasting model using Amazon SageMaker. The model is trained on historical data and needs to be updated daily with new data. The team wants to automate the retraining pipeline and avoid manual intervention. Which approach is the most efficient?

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

Accepted Answer

Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.. Amazon SageMaker Pipelines provides a fully managed, end-to-end orchestration service for building, training, and deploying machine learning models. By combining it with a scheduled AWS Lambda function, the team can automate daily retraining without manual intervention, leveraging SageMaker's native integration for step sequencing, artifact tracking, and model registry updates.

Answer

Use AWS Step Functions to orchestrate retraining, but require a manual approval step.

Answer

Use SageMaker training jobs manually triggered by the team each day.

Answer

Use a cron job on an EC2 instance to run a training script.

Question 2

A data scientist is training a linear regression model to predict house prices. The dataset contains 10 features. After training, the data scientist notices that the model has high bias (underfitting). Which action should the data scientist take to reduce bias?

Accepted Answer

Add more features, such as polynomial features. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features, such as polynomial features, increases model complexity, allowing the linear regression model to fit non-linear relationships and reduce bias. This directly addresses the underfitting issue by giving the model more expressive power.

Answer

Reduce the amount of training data

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Increase the regularization strength

Answer

Use a simpler model, such as ridge regression

Question 3

Which THREE of the following are valid strategies to reduce overfitting in a deep neural network? (Choose 3)

Accepted Answer

Use early stopping.. Option B (Use early stopping) is correct because monitoring validation loss and halting training when it stops improving prevents the model from continuing to fit noise in the training set, which is a classic overfitting-reduction technique. Option D (Add L2 regularization to the loss function) is correct because L2 adds a penalty proportional to the sum of squared weights, shrinking weights toward zero and constraining model complexity, thereby reducing overfitting. Option E (Use dropout layers) is correct because dropout randomly deactivates a fraction of neurons during each training step, forcing the network to learn redundant, robust features and acting as an ensemble-like regularizer. Option A (Increase the number of layers) is not correct because adding layers increases model capacity, which typically worsens overfitting rather than reducing it. Option C (Increase the learning rate) is not correct because a larger learning rate affects optimization speed and stability, not regularization, and can even lead to divergence or poorer generalization rather than reducing overfitting.

Answer

Increase the number of layers.

Answer

Increase the learning rate.

Question 4

A data scientist is training a Random Forest model on Amazon SageMaker. The model performs well on the training set but poorly on the test set. Which technique should the data scientist use to address this issue?

Accepted Answer

Decrease the maximum depth of each tree. The model is overfitting, as indicated by high training performance and poor test performance. Decreasing the maximum depth of each tree limits the complexity of individual trees, reducing overfitting by preventing them from memorizing noise in the training data. This is a standard regularization technique for Random Forest models in Amazon SageMaker.

Answer

Increase the number of trees in the forest

Answer

Increase the learning rate

Answer

Increase the maximum depth of each tree

Question 5

An e-commerce company uses a linear regression model to predict customer lifetime value (LTV). The model shows high variance on the test set, with training RMSE much lower than test RMSE. Which of the following is the MOST effective approach to reduce overfitting?

Accepted Answer

Apply L2 regularization (Ridge regression). High variance (low training RMSE, high test RMSE) indicates overfitting. L2 regularization (Ridge regression) adds a penalty proportional to the square of the coefficients, shrinking them toward zero without eliminating them, which reduces model complexity and improves generalization. This directly addresses overfitting by constraining the model's sensitivity to noise in the training data.

Answer

Use a polynomial kernel in a support vector regressor

Answer

Add more features, including interaction terms

Answer

Increase training data size by duplicating existing samples

Question 6

A data scientist is working with a dataset that contains both numerical and categorical features. The target variable is continuous. Which TWO EDA techniques should the scientist use to understand relationships between features and the target?

Accepted Answer

Create scatter plots of numerical features against the target variable.. Option C is correct because with a continuous target variable, scatter plots of each numerical feature against the target directly reveal the shape, direction, and strength of the relationship (linear, nonlinear, or none), which is exactly the goal of bivariate EDA. Option D is correct because box plots of the continuous target grouped by each categorical feature's categories show how the target's distribution (median, spread, outliers) shifts across groups, exposing categorical-feature/target relationships. Option A is wrong because a confusion matrix evaluates classification predictions and requires discrete class labels, not a continuous target. Option B is wrong because the silhouette score measures clustering quality/cohesion-separation, not feature-target relationships. Option E is wrong because a histogram of the target is a univariate analysis that describes the target's own distribution and does not relate any feature to the target.

Answer

Generate a confusion matrix for the target variable.

Answer

Compute the silhouette score for each feature.

Answer

Plot a histogram of the target variable.

Question 7

A machine learning engineer is deploying a PyTorch model to SageMaker. The model requires custom inference logic. Which approach should the engineer use?

Accepted Answer

Create a custom inference script and use the SageMaker PyTorch container. SageMaker allows you to provide a custom inference script (entry point) when using the PyTorch container, enabling custom inference logic. Option A is wrong because the built-in container as-is would not incorporate custom logic. Option B is wrong because SageMaker Ground Truth is for labeling, not model deployment. Option C is wrong because SageMaker Processing is for data processing, not inference.

Answer

Use a SageMaker built-in PyTorch container as-is

Answer

Use SageMaker Ground Truth to deploy the model

Answer

Use SageMaker Processing to run inference

Question 8

A company is using Amazon SageMaker to train a model on a large dataset stored in S3. The training job is taking a long time due to slow data loading. Which action can the data scientist take to reduce data loading time?

Accepted Answer

Use Pipe mode to stream data from S3.. Pipe mode streams data directly from S3 into the training algorithm without first downloading it to the local storage, eliminating the bottleneck of disk I/O and reducing data loading time. This is especially effective for large datasets where the time to copy data to EBS (File mode) dominates the training job duration.

Answer

Use File mode and copy data to Amazon EBS.

Answer

Use a larger instance type with more memory.

Answer

Enable data augmentation during training.

Question 9

A data scientist creates the above IAM policy and attaches it to a role used by an Amazon SageMaker notebook instance. When trying to save a file to the S3 bucket, the operation fails. What is the missing permission?

Accepted Answer

kms:GenerateDataKey. (kms:GenerateDataKey) because the S3 bucket is likely encrypted with a KMS key. When SageMaker writes an object to an encrypted bucket, it needs permission to call kms:GenerateDataKey to generate a data key for encryption. Option A (kms:Decrypt) is for decryption, not encryption. Option B (s3:ListBucket) allows listing objects, not writing. Option D (s3:GetObject) allows reading objects, not writing. The error when saving indicates missing encryption permissions.

Answer

kms:Decrypt

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s3:ListBucket

Answer

s3:GetObject

Question 10

A data scientist is analyzing server logs stored in Amazon CloudWatch Logs. The above snippet shows three log entries. They want to count the number of 500 errors per minute using CloudWatch Logs Insights. Which query should they use?

Accepted Answer

fields @timestamp, @message | filter @message like /500/ | stats count() by bin(1m). The correct query is D. It reads the @timestamp and @message fields, filters log entries containing '500' in the message, and then counts the number of such entries per minute using stats count() by bin(1m). This directly matches the requirement to count 500 errors per minute. Option A attempts to filter on a 'status' field, but the logs shown do not have a separate status field; the status code is embedded in the message. Option B filters for 'ERROR', but the logs use numeric status codes, not the word 'ERROR'. Option C sorts the results instead of aggregating, so it does not provide the count per minute.

Answer

fields @timestamp, status | filter status = 500 | stats count() by bin(1m)

Answer

fields @timestamp, @message | filter @message like /ERROR/ | stats count() by bin(1m)

Answer

fields @timestamp, @message | filter @message like /500/ | sort @timestamp desc

Question 11

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?

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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

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SageMaker batch transform

Answer

SageMaker multi-container endpoints

Question 19

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 20

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