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NEW QUESTION 35
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

• A. Convert current documents to SSML with pronunciation tags
• B. Use Amazon Lex to preprocess the text files for pronunciation
• C. Create an appropriate pronunciation lexicon.
• D. Output speech marks to guide in pronunciation

Answer: A

NEW QUESTION 36
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

• A. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
• B. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
• C. Randomly select 10% of the users. Split off all interaction data from these users for the test set.
• D. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.

Answer: D

Explanation:
https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagemaker/

NEW QUESTION 37
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

• A. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
• B. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
• C. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
• D. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

Answer: C

Explanation:
https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/tf-horovod-inference-pipeline/train.py

NEW QUESTION 38
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