MLS-C01 Modeling Practice Test 26 — 15 Questions

Question 1

A data scientist is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking too long due to high I/O latency waiting for data to be downloaded from S3. Which action would MOST effectively reduce the I/O latency?

Accepted Answer

Use Pipe mode for the training channel. Pipe mode streams data directly from S3 into the training algorithm without writing to disk, eliminating the I/O latency caused by downloading files to the local storage. This is the most effective solution because the bottleneck is data transfer from S3, and Pipe mode reduces it to near-zero latency by feeding data on the fly.

Answer

Use File mode for the training channel

Answer

Increase the number of training instances

Answer

Use Amazon SageMaker Elastic Inference

Question 2

A data scientist is training a linear regression model on a dataset with 10 features. After training, the model has high variance on the test set. Which technique should the data scientist use to reduce variance without significantly increasing bias?

Accepted Answer

Use L2 regularization. L2 regularization (Ridge regression) adds a penalty term proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces model complexity and variance by preventing any single feature from having an overly large influence, without eliminating features entirely, thus keeping bias relatively low.

Answer

Add more features

Answer

Use a simpler model

Answer

Use a deeper decision tree

Question 3

A company uses Amazon SageMaker to deploy a real-time inference endpoint for a regression model. The endpoint is experiencing high latency during spikes in traffic. The data scientist needs to reduce latency while maintaining cost efficiency. Which action should the data scientist take?

Accepted Answer

Enable automatic scaling for the endpoint. Option D is correct because enabling automatic scaling for the SageMaker endpoint allows the number of instances to dynamically adjust based on traffic patterns, reducing latency during spikes by adding capacity when needed and removing it during low traffic to maintain cost efficiency. Automatic scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or CPUUtilization) to trigger scale-out and scale-in policies, ensuring the endpoint can handle bursts without over-provisioning.

Answer

Use batch transform instead of real-time inference

Answer

Use a larger instance type for the endpoint

Answer

Deploy the model on a multi-model endpoint

Question 4

A data scientist is training a binary classifier using logistic regression. The dataset has 100,000 samples and 500 features. After training, the model achieves 95% accuracy on the training set but only 70% on the test set. The data scientist suspects overfitting. Which technique would best reduce overfitting while preserving interpretability?

Accepted Answer

Apply L1 regularization (Lasso). L1 regularization (Lasso) adds a penalty equal to the absolute value of the magnitude of coefficients, which drives many feature weights to exactly zero. This performs automatic feature selection, reducing model complexity and overfitting while keeping the model as a simple linear logistic regression, thus preserving interpretability.

Answer

Increase the maximum number of iterations

Answer

Add polynomial features

Answer

Use a random forest model instead

Question 5

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 6

A data scientist is using Amazon SageMaker to train an XGBoost model for a regression problem. The training data contains missing values in some features. Which approach should the data scientist use to handle missing values in XGBoost?

Accepted Answer

Leave missing values as-is; XGBoost handles them natively. XGBoost has a built-in mechanism to handle missing values natively by learning the best direction to split on missing values during training. For each split, XGBoost assigns missing values to the left or right child node based on which direction minimizes the loss function, making explicit imputation unnecessary for this algorithm.

Answer

Use K-nearest neighbors imputation

Answer

Remove all rows with missing values

Answer

Impute missing values with the mean of the column

Question 7

A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The data scientist trains a gradient boosting model and deploys it to a SageMaker endpoint with a single ml.c5.xlarge instance. During load testing, the endpoint exceeds the latency threshold. Which change is MOST likely to reduce latency?

Accepted Answer

Replace the model with a simpler model, such as logistic regression. Option A is correct because replacing the gradient boosting model with a simpler model like logistic regression reduces the computational complexity per inference. Gradient boosting involves traversing many decision trees, each requiring multiple conditional checks and arithmetic operations, while logistic regression is a single linear transformation. This directly lowers CPU utilization per request, reducing latency under the same instance resources.

Answer

Use a larger instance type, such as ml.c5.4xlarge

Answer

Switch to batch transform for inference

Answer

Enable automatic scaling on the endpoint

Question 8

A data scientist is training a text classification model using a bag-of-words approach. The dataset contains 1 million documents and 100,000 unique words. The resulting feature matrix is very sparse. Which technique should the data scientist use to reduce the dimensionality of the feature space?

Accepted Answer

Use word embeddings to represent documents. Word embeddings (e.g., Word2Vec, GloVe) map words to dense, low-dimensional vectors that capture semantic relationships, effectively reducing the 100,000-dimensional sparse bag-of-words feature space to a much smaller dense representation (e.g., 100–300 dimensions). This directly addresses the sparsity and high dimensionality of the term-document matrix while preserving meaningful word context.

Answer

Apply TF-IDF transformation

Answer

Remove stop words from the vocabulary

Answer

Apply Principal Component Analysis (PCA) to the term-document matrix

Question 9

Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads data from the S3 bucket 'my-bucket' and writes the model artifact to the same bucket. The training job fails with an access denied error. What is the likely cause?

Accepted Answer

The IAM role does not have permission to write to the S3 bucket for the model artifact. The training job fails with an access denied error because the IAM role used by SageMaker lacks the s3:PutObject permission (or equivalent write access) for the S3 bucket 'my-bucket'. While the script reads data from the bucket, writing the model artifact requires explicit write permissions on the same bucket. Without this, SageMaker cannot upload the model artifact, causing the job to fail.

Answer

The IAM role does not have sagemaker:CreateModel permission

Answer

The IAM role does not have s3:ListBucket permission

Answer

The IAM role does not have ec2:DescribeInstances permission

Question 10

Refer to the exhibit. A data scientist ran a SageMaker training job using a built-in XGBoost algorithm. The job failed with the error shown. Which step should the data scientist take to fix the issue?

Accepted Answer

Remove the metric definition from the training job configuration. The error indicates that SageMaker's built-in XGBoost algorithm does not support a custom metric named 'accuracy' because XGBoost's built-in objective functions (e.g., 'binary:logistic', 'reg:squarederror') do not compute accuracy natively. Removing the metric definition from the training job configuration resolves the issue by allowing SageMaker to use the default metrics that XGBoost does support, such as 'validation:rmse' or 'validation:error'.

Answer

Write a custom algorithm that calculates accuracy

Answer

Change the metric to 'validation:rmse'

Answer

Use a different built-in algorithm that supports accuracy

Question 11

Refer to the exhibit. A data scientist checks the status of a SageMaker endpoint and sees the output above. The endpoint is receiving traffic, but the data scientist notices that the number of instances has not increased to the desired count. What is the most likely reason?

Accepted Answer

Automatic scaling is not configured for the endpoint. Option D is correct because the endpoint is receiving traffic but not scaling out, which indicates that automatic scaling (Application Auto Scaling) has not been configured for the SageMaker endpoint. Without a scaling policy, the endpoint will only use the initial instance count, regardless of traffic load. The status shown does not indicate any update or quota issue, so the lack of scaling is the most likely cause.

Answer

The endpoint is performing a rolling update

Answer

The endpoint is currently being updated

Answer

The account has reached its instance limit

Question 12

A data scientist is building a binary classification model to predict loan default. The dataset is highly imbalanced (5% default, 95% non-default). Which TWO techniques should the data scientist use to address the class imbalance?

Accepted Answer

Oversample the minority class using SMOTE. Oversampling the minority class using SMOTE (Synthetic Minority Oversampling Technique) is correct because it generates synthetic samples for the minority class by interpolating between existing minority instances, rather than simply duplicating them. This helps balance the dataset without introducing exact copies, which can reduce overfitting and improve the model's ability to generalize to the minority class.

Answer

Undersample the majority class

Answer

Use RMSE as the evaluation metric

Answer

Use accuracy as the evaluation metric

Question 13

A data scientist is training a deep neural network on Amazon SageMaker. The training is taking a long time and the data scientist wants to speed it up. Which THREE actions can help reduce training time?

Accepted Answer

Use GPU instances instead of CPU instances. GPU instances (e.g., P3, P4d) are optimized for the massively parallel matrix operations required by deep neural networks, providing orders-of-magnitude faster computation than CPU instances for training tasks. By offloading tensor operations to GPU cores, the training time is significantly reduced, especially for large models and datasets.

Answer

Increase the batch size

Answer

Use a smaller instance type

Question 14

A data scientist is evaluating a linear regression model. Which TWO metrics are appropriate for evaluating the model's performance?

Accepted Answer

R-squared. R-squared is a standard metric for linear regression that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, making it directly appropriate for evaluating regression model performance.

Answer

Precision

Answer

Area Under the ROC Curve (AUC-ROC)

Answer

F1 score

Question 15

A company is building a fraud detection model that must achieve low false positive rates. The dataset is highly imbalanced (0.1% positive class). Which metric is most appropriate for model evaluation?

Accepted Answer

Area under the Precision-Recall curve. In highly imbalanced datasets (0.1% positive class), the Precision-Recall curve focuses on the performance of the positive class, which is the minority class of interest. Area under the Precision-Recall curve (AUPRC) is insensitive to the large number of true negatives, making it a robust metric for evaluating models where false positives must be minimized. Unlike ROC-AUC, which can be overly optimistic in severe imbalance, AUPRC directly reflects the trade-off between precision and recall for the rare positive class.

Answer

RMSE

Answer

Accuracy

Answer

R-squared