MLS-C01 Practice Test 12 — 15 Questions

Question 1

A data engineer is designing a data pipeline that uses Amazon S3 events to trigger an AWS Lambda function for processing. The pipeline must handle high throughput with low latency. Which TWO configurations should be applied?

Accepted Answer

Configure Lambda with reserved concurrency. Reserved concurrency ensures that the Lambda function always has a guaranteed number of concurrent executions available, preventing it from being throttled by other functions in the same AWS account. This is critical for high-throughput, low-latency pipelines because S3 event notifications can burst many invocations simultaneously, and without reserved concurrency, the function might hit the account-level concurrency limit and drop events.

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Use an SQS queue between S3 and Lambda

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Place Lambda in a VPC to reduce network latency

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Use Amazon Kinesis Data Streams as an intermediary

Question 2

Which THREE of the following are common issues that can be identified during exploratory data analysis? (Select THREE.)

Accepted Answer

Multicollinearity between features. Multicollinearity occurs when two or more features in a dataset are highly correlated, meaning they contain redundant information. During exploratory data analysis (EDA), correlation matrices and variance inflation factor (VIF) calculations can reveal this issue, which can destabilize linear regression models and inflate coefficient standard errors.

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High latency in API endpoints

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Gradient vanishing in neural networks

Question 3

Which THREE of the following are best practices for feature engineering during EDA? (Select THREE.)

Accepted Answer

Apply log transformation to highly skewed features. Option C is correct because applying a log transformation to highly skewed features helps normalize their distribution, reducing the impact of extreme values and making the data more suitable for many machine learning algorithms that assume normally distributed features. This is a common technique during exploratory data analysis (EDA) to stabilize variance and improve model performance, especially for linear models and neural networks.

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Remove all outliers from the dataset

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Standardize all features to have zero mean and unit variance

Question 4

Which TWO AWS services can be used to visualize data distributions as part of exploratory data analysis? (Select TWO.)

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a cloud-native business intelligence service that can visualize data distributions through histograms, box plots, scatter plots, and other chart types, making it suitable for exploratory data analysis. Amazon SageMaker Data Wrangler provides a visual interface to create data distribution charts (e.g., histograms, bar charts) directly within the data preparation workflow, enabling quick inspection of feature distributions before model building.

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AWS Glue

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Amazon Athena

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Amazon Comprehend

Question 5

A company stores sensor data in Amazon S3. A data scientist wants to explore the data using SQL without moving it. Which AWS service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any data movement or infrastructure management. Athena uses Presto under the hood and charges only for the data scanned per query, making it ideal for ad-hoc exploratory analysis on sensor data stored in S3.

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Amazon EMR

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Amazon Redshift

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Amazon QuickSight

Question 6

A machine learning engineer is exploring a dataset with 500 features and 10,000 samples. To reduce dimensionality for visualization, which technique is most suitable if the goal is to preserve global data structure?

Accepted Answer

Principal Component Analysis (PCA). PCA is the most suitable technique for preserving the global data structure when reducing dimensionality because it is a linear method that maximizes variance along orthogonal principal components, capturing the overall covariance structure of the 500 features. Unlike nonlinear methods, PCA ensures that the global relationships (e.g., distances between clusters) are retained, making it ideal for visualization of high-dimensional data where the goal is to see broad patterns.

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t-Distributed Stochastic Neighbor Embedding (t-SNE)

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Locally Linear Embedding (LLE)

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Uniform Manifold Approximation and Projection (UMAP)

Question 7

A data scientist is examining a dataset for a binary classification problem. The target variable has a 1:1000 imbalance. Which technique should be used to assess model performance during exploratory data analysis?

Accepted Answer

Area under the Precision-Recall curve. With a 1:1000 class imbalance, the positive class is extremely rare. The Area Under the Precision-Recall curve (AUPRC) focuses on the performance of the positive class and is sensitive to changes in precision and recall, making it a robust metric for imbalanced datasets. Unlike ROC AUC, which can be overly optimistic when negatives dominate, AUPRC provides a realistic assessment of model performance on the minority class.

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F1 score

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Area under the ROC curve

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Cohen's kappa

Question 8

A data scientist is using Amazon SageMaker to train a model, but the training job fails with an 'Out of memory' error. The instance type is ml.p3.2xlarge. Which action should the data scientist take to resolve the issue?

Accepted Answer

Reduce the mini-batch size in the training script.. The 'Out of memory' error on a single ml.p3.2xlarge instance indicates that the GPU memory is insufficient for the current workload. Reducing the mini-batch size directly decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without changing the instance type or incurring additional costs.

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Use Pipe input mode.

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Increase the number of instances.

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Use a Spot instance.

Question 9

A company's machine learning model is overfitting to the training data. The data scientist has already tried reducing the model complexity and adding regularization, but the model still overfits. Which technique could the data scientist use to further reduce overfitting?

Accepted Answer

Use data augmentation to increase the training dataset size. Data augmentation artificially increases the size and diversity of the training dataset by applying transformations (e.g., rotations, flips, noise injection) to existing samples. This exposes the model to more varied examples, reducing its tendency to memorize noise and improving generalization — directly countering overfitting when other methods have failed.

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Decrease the batch size

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Increase the number of training epochs

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Increase the learning rate

Question 10

A company uses Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.m5.large instance with automatic scaling based on CPU utilization. The team notices that during traffic spikes, the endpoint returns 5xx errors. What should the team do to improve the endpoint's availability?

Accepted Answer

Increase the instance type to ml.c5.2xlarge.. The correct answer is A because upgrading the instance type from ml.m5.large to ml.c5.2xlarge provides more CPU and memory resources, which directly addresses the root cause of 5xx errors during traffic spikes — insufficient compute capacity to handle the request load. Automatic scaling based on CPU utilization may not react quickly enough to sudden spikes, leading to request queuing and timeouts that manifest as 5xx errors. A larger instance type increases the baseline throughput, reducing the likelihood of resource exhaustion before scaling can take effect.

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Reduce the scaling cooldown period.

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Place an Application Load Balancer in front of the endpoint.

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Use Amazon API Gateway to throttle requests.

Question 11

A data scientist wants to understand the relationship between a categorical feature with 3 levels and a continuous target variable. Which visualization is most appropriate?

Accepted Answer

Box plot grouped by category. A box plot grouped by category (Option C) is the most appropriate visualization because it directly compares the distribution of a continuous target variable across the three levels of a categorical feature. It displays median, quartiles, and potential outliers for each group, making it ideal for understanding central tendency, spread, and skewness in a side-by-side comparison.

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Correlation matrix

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Line chart

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Scatter plot

Question 12

Drag and drop the steps to evaluate a trained model using SageMaker Model Monitor in the correct order.

Question 13

Drag and drop the steps to use Amazon SageMaker Debugger to debug a training job in the correct order.

Question 14

Drag and drop the steps to use Amazon SageMaker Feature Store for feature engineering in the correct order.

Question 15

Match each AWS service to its primary purpose in a machine learning pipeline.