MLS-C01 Mock Exam 77 — 25 Questions

Question 1

A data scientist is training a classification model on a dataset with missing values in several features. The data scientist wants to use SageMaker to train the model. Which TWO approaches can the data scientist use to handle missing data within the SageMaker training pipeline? (Choose two.)

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

Accepted Answer

Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default.. The SageMaker built-in XGBoost algorithm has a built-in mechanism to handle missing values by default. It learns the best direction (left or right branch) to route missing values during training, so no explicit imputation is needed. This makes it a seamless choice for datasets with missing data within the SageMaker training pipeline.

Answer

Use the SageMaker BlazingText algorithm, which automatically imputes missing values.

Answer

Use SageMaker Inference Pipeline to handle missing values at inference time.

Answer

Use SageMaker PCA algorithm, which automatically handles missing values.

Question 2

A data scientist is tuning a linear regression model and observes that the model has high bias and low variance. Which action is most likely to improve model performance?

Accepted Answer

Add more features. High bias and low variance indicate underfitting, meaning the model is too simple to capture the underlying patterns in the data. Adding more features increases model complexity, allowing it to learn more relevant relationships and reduce bias. This directly addresses the core issue of underfitting in linear regression.

Answer

Reduce the number of features

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Increase regularization

Answer

Reduce the amount of training data

Question 3

A data scientist is training a classification model on an imbalanced dataset where the positive class represents only 5% of the data. Which technique would BEST address the class imbalance without discarding data?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Oversampling Technique) is the best choice because it generates synthetic samples for the minority class by interpolating between existing minority instances, effectively balancing the dataset without discarding any data. This avoids the information loss of undersampling and the overfitting risk of simple random oversampling, making it ideal for a 5% positive class scenario.

Answer

Randomly undersample the majority class

Answer

Adjust the decision threshold to 0.95

Answer

Randomly oversample the minority class with replacement

Question 4

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.. 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.

Answer

Reduce the scaling cooldown period.

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Place an Application Load Balancer in front of the endpoint.

Answer

Use Amazon API Gateway to throttle requests.

Question 5

A data scientist runs the above AWS CLI command and gets the output. The object size is 1 GB. They try to open the CSV file in Amazon Athena but get an error. What is the most likely cause?

Accepted Answer

The file exceeds the maximum CSV file size that Athena can query without partitioning. Amazon Athena has a default limit of 100 MB per CSV file when querying without partitioning. A 1 GB file exceeds this limit, causing an error. Option A is wrong because CSV is a supported file format in Athena. Option C is wrong while gzip compression is supported, it is not required; the issue is file size, not compression. Option D is wrong because Athena can query large files, but only if they are properly partitioned or if the file size is within the per-file limit.

Answer

The file format is not supported by Athena

Answer

The file is not compressed with gzip

Answer

The file is too large for Athena to query at all

Question 6

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.

Answer

Use Pipe input mode.

Answer

Increase the number of instances.

Answer

Use a Spot instance.

Question 7

A company wants to use Amazon Rekognition to detect objects in images stored in an S3 bucket. The images are uploaded by users. Which IAM policy statement is necessary to allow Rekognition to read from the bucket?

Accepted Answer

s3:GetObject. Amazon Rekognition needs to read the image files from the S3 bucket to perform object detection. The s3:GetObject permission grants read access to the objects stored in the bucket, which is the minimum required action for Rekognition to retrieve and analyze the images.

Answer

s3:PutObject

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s3:DeleteObject

Answer

s3:ListBucket

Question 8

Which TWO actions should a data scientist take when exploring a dataset that contains missing values and outliers? (Select TWO.)

Accepted Answer

Calculate the percentage of missing values per column.. Calculating the percentage of missing values per column is a standard first step in exploratory data analysis (EDA) to quantify data completeness. This informs downstream decisions such as whether to impute, drop, or flag missing data, and helps assess the risk of bias or information loss. It is a diagnostic action, not a transformation, and should precede any imputation or removal.

Answer

Normalize all features using Min-Max scaling.

Answer

Remove all rows with outliers.

Answer

Impute missing values with the mean immediately.

Question 9

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.. 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 10

A company uses Amazon DynamoDB as the primary data store for a real-time application. The data science team wants to analyze the data using Amazon Athena. What is the most efficient way to make the DynamoDB data available for Athena queries?

Accepted Answer

Use DynamoDB Streams to invoke an AWS Lambda function that writes data to Amazon S3 in Parquet format. Then query the data in S3 using Athena.. DynamoDB Streams captures real-time changes, and an AWS Lambda function can efficiently write these changes to Amazon S3 in Parquet format, which is optimized for columnar storage and Athena queries. This approach minimizes the overhead of scheduled batch jobs and provides near-real-time data availability for analytics.

Answer

Use AWS Glue to extract data from DynamoDB and load into S3 on a schedule.

Answer

Use Amazon Redshift Spectrum to query DynamoDB directly.

Answer

Use Amazon EMR to read directly from DynamoDB and run Hive queries.

Question 11

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 12

A data scientist is training a deep learning model using Amazon SageMaker. The training loss is decreasing, but the validation loss starts increasing after 10 epochs. The model is overfitting. Which TWO actions should the data scientist take to reduce overfitting? (Choose 2.)

Accepted Answer

Add dropout layers. Dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, a common cause of overfitting. This technique is particularly effective in deep learning models trained on SageMaker, where large architectures can quickly memorize training data.

Answer

Increase the number of layers

Answer

Remove L2 regularization

Answer

Increase the number of training steps

Question 13

A company wants to monitor a deployed model for data drift. Which AWS service should they use?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor deployed machine learning models for data drift and quality issues. It automatically detects deviations in the input data distribution compared to a baseline, triggering alerts when drift exceeds defined thresholds, which directly addresses the company's requirement.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon CloudWatch Logs

Answer

Amazon SageMaker Clarify

Question 14

A company uses Amazon RDS for its transactional database and needs to export a daily snapshot of a table to Amazon S3 in Parquet format for analytics. Which AWS service can perform this export without writing custom code?

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a fully managed ETL service that can natively read from Amazon RDS and write to Amazon S3 in Parquet format using a scheduled job, without requiring any custom code. Glue's built-in transform capabilities and crawlers can convert the data to columnar Parquet format efficiently for analytics workloads.

Answer

Amazon Redshift

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Athena

Question 15

A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The dataset has missing values in several features. Which TWO actions should the data scientist take to handle missing values effectively? (Choose two.)

Accepted Answer

Impute missing values with the median of each feature using a preprocessing step.. (impute with median) is a standard preprocessing technique that can help gradient boosting models handle missing data. Option E (set the 'missing' parameter) leverages XGBoost's built-in capability to treat missing values as a separate direction, allowing the algorithm to learn the best split. Option D (PCA) is incorrect because PCA does not handle missing values; it requires complete data or imputation first. Option B (one-hot encoding for missingness) is more appropriate for categorical features and may add noise. Option C (remove rows) leads to data loss and is generally not recommended when missing values are not too extensive.

Answer

Use one-hot encoding to create binary columns indicating missingness.

Answer

Remove all rows with missing values from the training dataset.

Answer

Apply PCA to reduce dimensionality and ignore missing values.

Question 16

Which THREE of the following are valid metrics for evaluating a regression model?

Accepted Answer

R-squared (R²). R-squared (R²) is a valid regression metric 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, and is commonly used alongside other error metrics to assess model performance.

Answer

F1 score

Answer

Accuracy

Question 17

A team deployed a SageMaker endpoint with the configuration shown in the exhibit. During a traffic spike, the endpoint becomes unresponsive. Which change to the endpoint configuration would best improve availability?

Accepted Answer

Configure auto-scaling for the endpoint. Configuring auto-scaling for the SageMaker endpoint allows it to automatically add or remove instances based on traffic load, improving availability during spikes. Option A is incorrect because setting initial instance count to 0 would cause requests to fail until an instance is provisioned, and on-demand invocation does not improve availability. Option B is incorrect because adding a second production variant with the same model does not change the total number of instances; the endpoint would still have only one instance if both variants share the same instance count. Option D is incorrect because changing the instance type to ml.m5.xlarge might provide more resources per instance but does not increase the number of instances; a single instance can still become overwhelmed.

Answer

Reduce the initial instance count to 0 and use on-demand invocation

Answer

Add a second production variant with the same model

Answer

Change the instance type to ml.m5.xlarge

Question 18

A data scientist is analyzing a dataset with 500 features and 10,000 samples. After running a correlation matrix, they find that many feature pairs have correlation >0.95. What is the most appropriate next step to improve model performance?

Accepted Answer

Apply principal component analysis (PCA) to reduce dimensionality.. PCA reduces dimensionality by transforming correlated features into uncorrelated principal components, addressing multicollinearity while retaining most of the variance. Option A is wrong: collecting more data does not reduce correlation between features. Option B is wrong: increasing regularization (e.g., L2) can mitigate multicollinearity effects, but with 500 features and many highly correlated pairs, PCA is more effective as a dimensionality reduction technique. Option D is wrong: removing all features with correlation >0.95 may discard useful information and is less systematic than PCA.

Answer

Collect more training data to reduce the impact of correlated features.

Answer

Increase the regularization parameter in the model.

Answer

Remove all features with correlation above 0.95.

Question 19

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 20

Which TWO AWS services can be used to move data from an on-premises database to Amazon S3 on a recurring schedule without writing custom code? (Choose 2.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a fully managed ETL service that can run crawlers and jobs on a recurring schedule to extract data from on-premises databases (via JDBC connections) and write it to Amazon S3 without requiring any custom code. AWS DMS is correct because it supports continuous replication or scheduled tasks to migrate data from on-premises databases to S3 as a target, using built-in transformation capabilities and no custom scripting.

Answer

AWS Snowball Edge

Answer

Amazon Athena

Answer

Amazon Kinesis Data Firehose