← Back to exam page

AWS-Certified-Machine-Learning-Specialty-MLS-C01 – AWS Certified Machine Learning - Specialty (MLS-C01)

Loading demo links...

Showing 7–9 of 15 questions

Question 7

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.

Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

Select all that apply, then click Submit answer.

  • ○

    Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
  • ○

    The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
  • ○

    Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
  • ○

    The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
  • ○

    Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
Question 8

A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members’ faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.

The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service.

How should a machine learning specialist architect the solution to satisfy these requirements?

Select an option, then click Submit answer.

  • ○

    Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support.
  • ○

    Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation.
  • ○

    Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
  • ○

    Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
Question 9

A company wants to predict the sale prices of houses based on available historical sales data. The target

variable in the company’s dataset is the sale price. The features include parameters such as the lot size, living

area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built,

and postal code. The company wants to use multi-variable linear regression to predict house sale prices.

Which step should a machine learning specialist take to remove features that are irrelevant for the analysis

and reduce the model’s complexity?

Select an option, then click Submit answer.

  • ○

    Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
  • ○

    Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
  • ○

    Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual
    correlation scores.
  • ○

    Run a correlation check of all features against the target variable. Remove features with low target variable
    correlation scores.