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AWS Certified Machine Learning - Specialty

Last Update Apr 25, 2025
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AWS Certified Machine Learning - Specialty Questions and Answers

Questions 1

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

How should a data scientist adjust the solution?

Options:

A.

Use the event tracker in Amazon Personalize to include real-time user interactions.

B.

Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

C.

Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

D.

Add event type and event value fields to the interactions dataset in Amazon Personalize.

Questions 2

A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model.

Which change will create the required transformed records with the LEAST operational overhead?

Options:

A.

Create an AWS Lambda function that can transform the incoming records. Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target.

B.

Deploy an Amazon EMR cluster that runs Apache Spark and includes the transformation logic. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.

C.

Deploy an Amazon S3 File Gateway in the stores. Update the in-store software to deliver data to the S3 File Gateway. Use a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3.

D.

Launch a fleet of Amazon EC2 instances that include the transformation logic. Configure the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.

Questions 3

A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing.

How it should the data science team do to meet these requirements?

Options:

A.

Create new features and interaction variables.

B.

Use a principal component analysis (PCA) model.

C.

Apply normalization on the feature set.

D.

Use a multiple correspondence analysis (MCA) model