MLS-C01 Practice Test 13 — 15 Questions

Question 1

Match each data format to its typical use in AWS ML.

Question 2

A data scientist wants to understand the distribution of a categorical feature with 100 unique values. Which visualization is most appropriate?

Accepted Answer

Bar chart. A bar chart is the most appropriate visualization for displaying the distribution of a categorical feature with 100 unique values because it uses discrete bars to represent the frequency or proportion of each category. Unlike a histogram, which requires continuous numeric bins, a bar chart preserves the distinct categories and allows clear comparison of counts across all 100 levels.

Answer

Histogram

Answer

Scatter plot

Answer

Pie chart

Question 3

A data scientist is analyzing a dataset with a target variable that is heavily imbalanced (e.g., 99% negative class, 1% positive class). Which exploratory data analysis technique is most appropriate to understand the relationship between features and the target before modeling?

Accepted Answer

Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.. Option C is correct because stratified sampling preserves the class distribution in the sample, allowing you to create a balanced subset for exploratory analysis. Computing correlation matrices and box plots on this balanced subset reveals feature-target relationships without being overwhelmed by the majority class, which is critical for imbalanced datasets like 99% negative vs. 1% positive.

Answer

Randomly sample 10% of the data and plot feature distributions by class.

Answer

Apply PCA to reduce dimensionality, then visualize the first two components.

Answer

Focus only on the majority class features to avoid bias.

Question 4

Which TWO of the following are benefits of feature scaling for machine learning algorithms?

Accepted Answer

Faster convergence of gradient descent. Option D is correct because feature scaling, typically via standardization (z-score) or min-max normalization, ensures that gradient descent converges faster. Without scaling, features with larger numerical ranges dominate the gradient updates, causing the algorithm to oscillate and require more iterations to reach the optimum. Scaling produces a more spherical contour of the loss function, allowing gradient descent to take more direct steps toward the minimum.

Answer

Eliminates the effect of outliers

Answer

Reduces the need for feature selection

Answer

Improves performance of decision tree algorithms

Question 5

During EDA, a data scientist plots the distribution of a numeric feature and observes that it is right-skewed. The feature will be used as input to a linear model. Which transformation should the data scientist apply?

Accepted Answer

Log transformation. A right-skewed distribution indicates that the feature has a long tail on the right, which can violate the linear model assumption of normally distributed errors. The log transformation compresses the high values and expands the low values, making the distribution more symmetric and stabilizing variance, which improves linear model performance.

Answer

Square transformation

Answer

One-hot encoding

Answer

Standardization (Z-score)

Question 6

A company is using SageMaker to host a model that performs real-time fraud detection. The model receives high request volumes with occasional spikes. The company wants to ensure that the endpoint can handle spikes without throttling while minimizing cost. Which scaling strategy should be used?

Accepted Answer

Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.. A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.

Answer

Use a simple scaling policy with a step adjustment based on the InvocationsPerInstance metric.

Answer

Manually adjust the instance count based on monitoring dashboards.

Answer

Use a scheduled scaling action to add instances during peak hours.

Question 7

In exploratory data analysis, a data scientist notices that the distribution of a feature 'income' is heavily right-skewed. Which transformation is most appropriate to reduce skewness?

Accepted Answer

Log transformation.. Log transformation is the most appropriate technique to reduce right skewness in a feature like 'income' because it compresses the long tail of high values while expanding the lower end, making the distribution more symmetric. This is particularly effective for income data, which often follows a log-normal distribution, and is a standard preprocessing step in machine learning to improve model performance.

Answer

Standardization (z-score).

Answer

Square transformation.

Answer

Min-max scaling.

Question 8

A data scientist is using SageMaker to train a model and wants to track experiments, including hyperparameters and metrics. Which TWO actions should the scientist take to set up experiment tracking? (Choose TWO.)

Accepted Answer

Use the SageMaker Experiments Python SDK to create an experiment and log runs.. Option A is correct because the SageMaker Experiments Python SDK provides the primary interface for creating and managing experiments, allowing the data scientist to log runs, hyperparameters, and metrics in a structured way. This SDK directly integrates with SageMaker training jobs and notebook executions to capture experiment metadata.

Answer

Enable SageMaker Model Monitor to track training metrics.

Answer

Configure CloudWatch Logs to store experiment data.

Answer

Enable SageMaker Studio to automatically capture experiments.

Question 9

A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.

Answer

Mean squared error

Answer

Accuracy

Answer

Precision

Question 10

A team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The endpoint must handle variable traffic patterns with minimal latency. Which deployment strategy should the team use?

Accepted Answer

Use a SageMaker multi-model endpoint with GPU instance type.. B is correct because SageMaker multi-model endpoints (MMEs) allow multiple models to be hosted on a single GPU-backed endpoint, dynamically loading and unloading models from disk to GPU memory as needed. This reduces cost and cold-start latency compared to single-model endpoints, while still providing GPU acceleration for deep learning inference. MMEs are ideal for variable traffic patterns because they can scale horizontally and share GPU resources efficiently.

Answer

Deploy a single model endpoint with an auto-scaling policy.

Answer

Deploy a serverless endpoint using SageMaker Serverless Inference.

Answer

Use SageMaker Batch Transform to process requests in batches.

Question 11

A machine learning engineer is training a deep learning model using the SageMaker built-in XGBoost algorithm. The training job is taking longer than expected. The engineer notices that the training data is stored in S3 in CSV format and is 500 GB in size. The instance type is ml.c4.8xlarge with 10 instances. Which change would most likely reduce training time?

Accepted Answer

Use Pipe input mode instead of File input mode.. Pipe input mode streams data directly from S3 to the training instances without first downloading it to the local EBS volume, eliminating the I/O bottleneck of reading a 500 GB CSV file. This reduces the time spent on data loading and allows the XGBoost algorithm to begin training sooner, which is especially beneficial for large datasets.

Answer

Convert the data to Parquet format.

Answer

Increase the number of instances to 20.

Answer

Increase the size of the EBS volume attached to each instance.

Question 12

A data scientist is building a regression model to predict house prices. The dataset has 10 features, and the model shows high variance with a low bias. Which technique should the data scientist use to reduce variance?

Accepted Answer

Apply L2 regularization to the model.. L2 regularization (Ridge regression) penalizes large coefficients by adding a squared magnitude term to the loss function, which shrinks the model's weights and reduces variance without substantially increasing bias. This directly addresses the high-variance, low-bias symptom, making the model less sensitive to fluctuations in the training data.

Answer

Increase the depth of decision trees in the ensemble.

Answer

Add more features to the model.

Answer

Reduce the amount of training data.

Question 13

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). AUC-ROC is the most appropriate metric for imbalanced binary classification because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, without being biased by the 95% negative majority. It measures the trade-off between true positive rate and false positive rate, making it robust to class imbalance.

Answer

R-squared

Answer

Mean Squared Error (MSE)

Answer

Accuracy

Question 14

A data analyst is exploring a dataset and notices that the target variable has a Poisson distribution. Which type of model is most appropriate for this target?

Accepted Answer

Poisson regression. Poisson regression is the correct choice because it is specifically designed for modeling count data where the target variable follows a Poisson distribution, which is characterized by non-negative integer values and a variance equal to the mean. This aligns directly with the data analyst's observation of a Poisson-distributed target, making Poisson regression the most appropriate generalized linear model (GLM) for this scenario.

Answer

Linear regression

Answer

Cox proportional hazards model

Answer

Logistic regression

Question 15

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset and notices that the target variable is highly imbalanced. Which technique should the data scientist apply to balance the dataset before training?

Accepted Answer

Synthetic Minority Oversampling Technique (SMOTE). Option D is correct because SMOTE generates synthetic samples for the minority class. Option A is wrong because it discards majority class data. Option B is wrong because scaling does not balance classes. Option C is wrong because encoding is for categorical features.

Answer

One-hot encoding of the target variable

Answer

Random undersampling of the majority class

Answer

Min-Max scaling of all features