Question 1

A company is building a new version of a recommendation engine.Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations.The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3.A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance.The whole process takes more than 20 hours to finish, with each script taking at least an hour.The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.Which approach will address all of these requirements with the LEAST development effort?

Options :

A : Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.

B : Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.

C : Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.

D : Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.

Answer: C

Question 2

A company is planning a marketing campaign to promote a new product to existing customers. The company has data for past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials. The company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%. How should the company retrain the model to meet these requirements?

Options :

A : Set the target_recall hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to recall_at_target_precision.

B : Set the target_precision hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall.

C : Use 90% of the historical data for training. Set the number of epochs to 20.

D : Set the normalize_label hyperparameter to true. Set the number of classes to 2.

Answer: B

Question 3

What is the first step in building a machine learning application?

Options :

A : Preparing the data

B : Training the model

C : Formulating the problem

D : Testing the model

Answer: C

Question 4

A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily.The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.What should the company do to reduce the processing time of loan applications?

Options :

A : Use an Amazon Textract synchronous operation instead of an asynchronous operation.

B : Use Amazon Rekognition-s feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.

C : Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation.

D : Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.

Answer: D

Question 5

Which aspect of AI does not directly involve machine learning?

Options :

A : Data extraction in Amazon Textract.

B : Intelligent search in Amazon Kendra.

C : Document analysis in Amazon Comprehend.

D : Rule-based automation without learning.

Answer: D

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