AWS Certified Machine Learning Specialty MLS-C01 Q21-Q30

21. A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse.

A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse.

Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Choose three.)

A. Define the feature variables and target variable for the churn prediction model.
B. Use the SOL EXPLAIN_MODEL function to run predictions.
C. Write a CREATE MODEL SQL statement to create a model.
D. Use Amazon Redshift Spectrum to train the model.
E. Manually export the training data to Amazon S3.
F. Use the SQL prediction function to run predictions.

22. A data scientist is conducting exploratory data analysis (EDA) on a dataset that contains information about product suppliers. The dataset records the country where each product supplier is located as a two-letter text code. For example, the code for New Zealand is “NZ.”

The data scientist needs to transform the country codes for model training. The data scientist must choose the solution that will result in the smallest increase in dimensionality. The solution must not result in any information loss.

Which solution will meet these requirements?

A. Add a new column of data that includes the full country name.
B. Encode the country codes into numeric variables by using similarity encoding.
C. Map the country codes to continent names.
D. Encode the country codes into numeric variables by using one-hot encoding.

23. A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.

The data scientist wants to understand the variance in the data along various directions in the feature space.

Which solution will meet these requirements?

A. Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.
B. Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.
C. Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.
D. Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.

24. A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.

Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

A. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
B. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
C. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
D. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

25. A financial company sends special offers to customers through weekly email campaigns. A bulk email marketing system takes the list of email addresses as an input and sends the marketing campaign messages in batches. Few customers use the offers from the campaign messages. The company does not want to send irrelevant offers to customers.

A machine learning (ML) team at the company is using Amazon SageMaker to build a model to recommend specific offers to each customer based on the customer’s profile and the offers that the customer has accepted in the past.

Which solution will meet these requirements with the MOST operational efficiency?

A. Use the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
B. Use the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
C. Use the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
D. Use the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.

26. A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations.

Which solution will meet these requirements with LEAST development effort?

A. Use AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
B. Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.
C. Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
D. Use Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.

27. A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.

Which solution will meet these requirements?

A. Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.
B. Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook’s VPC.
C. Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the IAM role.
D. Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.

28. A machine learning (ML) specialist has prepared and used a custom container image with Amazon SageMaker to train an image classification model. The ML specialist is performing hyperparameter optimization (HPO) with this custom container image to produce a higher quality image classifier.

The ML specialist needs to determine whether HPO with the SageMaker built-in image classification algorithm will produce a better model than the model produced by HPO with the custom container image. All ML experiments and HPO jobs must be invoked from scripts inside SageMaker Studio notebooks.

How can the ML specialist meet these requirements in the LEAST amount of time?

A. Prepare a custom HPO script that runs multiple training jobs in SageMaker Studio in local mode to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value.
B. Use SageMaker Autopilot to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Compare the objective metric values of the resulting models of the SageMaker AutopilotAutoML job and the automatic model tuning job. Select the model with the best objective metric value.
C. Use SageMaker Experiments to run and manage multiple training jobs and tune the model of the custom container image. Use the automatic model tuning capability of SageMaker to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value.
D. Use the automatic model tuning capability of SageMaker to tune the models of the custom container image and the built-in image classification algorithm at the same time. Select the model with the best objective metric value.

29. A digital media company wants to build a customer churn prediction model by using tabular data. The model should clearly indicate whether a customer will stop using the company’s services. The company wants to clean the data because the data contains some empty fields, duplicate values, and rare values.

Which solution will meet these requirements with the LEAST development effort?

A. Use SageMaker Canvas to automatically clean the data and to prepare a categorical model.
B. Use SageMaker Data Wrangler to clean the data. Use the built-in SageMaker XGBoost algorithm to train a classification model.
C. Use SageMaker Canvas automatic data cleaning and preparation tools. Use the built-in SageMaker XGBoost algorithm to train a regression model.
D. Use SageMaker Data Wrangler to clean the data. Use the SageMaker Autopilot to train a regression model

30. A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository.

Which combination of steps will meet these requirements? (Choose two.)

A. Create an IAM role in the development account that the integration account and production account can assume. Attach IAM policies to the role that allow access to the feature repository and the S3 buckets.
B. Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM).
C. Use AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account.
D. Set up S3 replication between the development S3 buckets and the integration and production S3 buckets.
E. Create an AWS PrivateLink endpoint in the development account for SageMaker.