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Free Amazon MLS-C01 Exam Questions

Absolute Free MLS-C01 Exam Practice for Comprehensive Preparation 

  • Amazon MLS-C01 Exam Questions
  • Provided By: Amazon
  • Exam: AWS Certified Machine Learning - Specialty
  • Certification: AWS Certified Machine Learning
  • Total Questions: 385
  • Updated On: Mar 28, 2025
  • Rated: 4.9 |
  • Online Users: 770
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  • Question 1
    • A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial dat
      a. The company is worried about data egress and wants an ML engineer to secure the environment.
      Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

      Answer: A,B,D
  • Question 2
    • 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?

      Answer: C
  • Question 3
    • A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

      Answer: D
  • Question 4
    • A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
      * Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
      * Support event-driven ETL pipelines.
      * Provide a quick and easy way to understand metadata.
      Which approach meets trfese requirements?

      Answer: A
  • Question 5
    • A company has a podcast platform that has thousands of users. The company has implemented an anomaly detection algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening, pausing, and exiting the podcast. A machine learning (ML) specialist is designing the data ingestion of these events with the knowledge that the event payload needs some small transformations before inference. How should the ML specialist design the data ingestion to meet these requirements with the LEAST operational overhead?

      Answer: B
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