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Amazon Web Services MLS-C01 Exam Sample Questions


Question # 1

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
A. Recall
B. Misclassification rate
C. Mean absolute percentage error (MAPE)
D. Area Under the ROC Curve (AUC)


D. Area Under the ROC Curve (AUC)

Explanation:

Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model.

References:

• AWS Machine Learning Specialty Exam Guide

• AWS Machine Learning Specialty Sample Questions





Question # 2

A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.

Which nodes should the Specialist launch on Spot Instances?

A. Master node
B. Any of the core nodes
C. Any of the task nodes
D. Both core and task nodes


C. Any of the task nodes

Explanation:

The best option for using Spot Instances in a long-running Amazon EMR cluster is to use them for the task nodes. Task nodes are optional nodes that are used to increase the processing power of the cluster. They do not store any data and can be added or removed without affecting the cluster’s operation. Therefore, they are more resilient to interruptions caused by Spot Instance termination. Using Spot Instances for the master node or the core nodes is not recommended, as they store important data and metadata for the cluster. If they are terminated, the cluster may fail or lose data.

References:

• Amazon EMR on EC2 Spot Instances

• Instance purchasing options - Amazon EMR




Question # 3

A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

A. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.
B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
C. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.
D. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.


D. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

Explanation:

The solution that will accomplish the necessary transformation to train the Amazon SageMaker model with the least amount of administrative overhead is to crawl the data using AWS Glue crawlers, write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. This solution leverages the serverless capabilities of AWS Glue to automatically discover the schema of the data sources, and to perform the data integration and transformation without requiring any cluster management or configuration. The output in CSV format is compatible with Amazon SageMaker and can be easily loaded into a training job.





Question # 4

Which AWS service can provide a curated selection of pre-trained embedding models to reduce the complexity and cost of vector embeddings?
A. Amazon SageMaker Feature Store
B. Amazon Kendra
C. Amazon SageMaker JumpStart
D. Amazon Comprehend


C. Amazon SageMaker JumpStart




Question # 5

A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.

Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

A. Emails exchanged by customers and the company’s customer service agents
B. Social media posts containing the name of the company or its products
C. A publicly available collection of news articles
D. A publicly available collection of customer reviews
E. Product sales revenue figures for the company


A. Emails exchanged by customers and the company’s customer service agents

B. Social media posts containing the name of the company or its products

D. A publicly available collection of customer reviews

Explanation:

The data sources that the data scientist should use to augment the dataset of reviews are those that contain relevant and diverse customer feedback about the company or its products. Emails exchanged by customers and the company’s customer service agents can provide valuable insights into the issues and complaints that customers have, as well as the solutions and responses that the company offers.

Social media posts containing the name of the company or its products can capture the opinions and sentiments of customers and potential customers, as well as their reactions to marketing campaigns and product launches. A publicly available collection of customer reviews can provide a large and varied sample of feedback from different online platforms and marketplaces, which can help to generalize the ML models and avoid bias.

References:

• Detect sentiment from customer reviews using Amazon Comprehend | AWS Machine Learning Blog

• How to Apply Machine Learning to Customer Feedback




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