An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

1. A. m5 4xlarge (general purpose)
2. B. r5.2xlarge (memory optimized)
3. C. p3.2xlarge (GPU accelerated computing)
4. D. p3 8xlarge (GPU accelerated computing)

Correct Answer: C

A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure,” and "documentary.”
A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?

1. A. Add the unrecognized words in the enumeration values list as new values in the slot type.
2. B. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.
3. C. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.
4. D. Add the unrecognized words as synonyms in the custom slot type.

Correct Answer: C

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS
Which approach should the Specialist use for training a model using that data?

1. A. Write a direct connection to the SQL database within the notebook and pull data in
2. B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
3. C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
4. D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

Correct Answer: B

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

1. A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
2. B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
3. C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
4. D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Correct Answer: B
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

1. A. Create one-hot word encoding vectors.
2. B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
3. C. Create word embedding factors that store edit distance with every other word.
4. D. Download word embedding’s pre-trained on a large corpus.

Correct Answer: D