AWS Certified Machine Learning Specialty (MLS-C01)

The AWS Certified Machine Learning Specialty (MLS-C01) were last updated on today.
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Topic 1 - Exam A

Question #11 Topic 1

A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago. Which method should the Specialist try to improve model performance?

  • A The model needs to be completely re-engineered because it is unable to handle product inventory changes.
  • B The model's hyperparameters should be periodically updated to prevent drift.
  • C The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
  • D The model should be periodically retrained using the original training data plus new data as product inventory changes.
Suggested Answer: D
NOTE:
Question #12 Topic 1

A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked. Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

  • A AWS CloudTrail
  • B AWS Health
  • C AWS Trusted Advisor
  • D Amazon CloudWatch
  • E AWS Config
Suggested Answer: AD
NOTE: Reference: https://aws.amazon.com/sagemaker/faqs/
Question #13 Topic 1

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 Create an appropriate pronunciation lexicon.
  • C Output speech marks to guide in pronunciation.
  • D Use Amazon Lex to preprocess the text files for pronunciation
Suggested Answer: A
NOTE: Reference: https://docs.aws.amazon.com/polly/latest/dg/ssml.html
Question #14 Topic 1

A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII). The dataset: ✑ Must be accessible from a VPC only. ✑ Must not traverse the public internet. How can these requirements be satisfied?

  • A Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
  • B Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
  • C Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
  • D Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
Suggested Answer: A
NOTE:
Question #15 Topic 1

A company is running a machine learning prediction service that generates 100 TB of predictions every day. A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team. Which solution requires the LEAST coding effort?

  • A Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Give the Business team read-only access to S3.
  • B Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.
  • C Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team.
  • D Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
Suggested Answer: C
NOTE: