Free AWS Certified Machine Learning - Specialty Exam Braindumps (page: 48)

Page 48 of 84

An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website. The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.

The historical transactions data is in a .csv file that is stored in Amazon S3. The data contains features such as the user's IP address, navigation time, average time on each page, and the number of clicks for each session. There is no label in the data to indicate if a transaction is anomalous.

Which models should the company use in combination to detect anomalous transactions? (Choose two.)

  1. IP Insights
  2. K-nearest neighbors (k-NN)
  3. Linear learner with a logistic function
  4. Random Cut Forest (RCF)
  5. XGBoost

Answer(s): A,D



A healthcare company is using an Amazon SageMaker notebook instance to develop machine learning (ML) models. The company's data scientists will need to be able to access datasets stored in Amazon S3 to train the models. Due to regulatory requirements, access to the data from instances and services used for training must not be transmitted over the internet.

Which combination of steps should an ML specialist take to provide this access? (Choose two.)

  1. Configure the SageMaker notebook instance to be launched with a VPC attached and internet access disabled.
  2. Create and configure a VPN tunnel between SageMaker and Amazon S3.
  3. Create and configure an S3 VPC endpoint Attach it to the VP
  4. Create an S3 bucket policy that allows traffic from the VPC and denies traffic from the internet.
  5. Deploy AWS Transit Gateway Attach the S3 bucket and the SageMaker instance to the gateway.

Answer(s): A,C



A machine learning (ML) specialist at a retail company is forecasting sales for one of the company's stores. The ML specialist is using data from the past 10 years. The company has provided a dataset that includes the total amount of money in sales each day for the store. Approximately 5% of the days are missing sales data.

The ML specialist builds a simple forecasting model with the dataset and discovers that the model performs poorly. The performance is poor around the time of seasonal events, when the model consistently predicts sales figures that are too low or too high.

Which actions should the ML specialist take to try to improve the model's performance? (Choose two.)

  1. Add information about the store's sales periods to the dataset.
  2. Aggregate sales figures from stores in the same proximity.
  3. Apply smoothing to correct for seasonal variation.
  4. Change the forecast frequency from daily to weekly.
  5. Replace missing values in the dataset by using linear interpolation.

Answer(s): A,C



A newspaper publisher has a table of customer data that consists of several numerical and categorical features, such as age and education history, as well as subscription status. The company wants to build a targeted marketing model for predicting the subscription status based on the table data.

Which Amazon SageMaker built-in algorithm should be used to model the targeted marketing?

  1. Random Cut Forest (RCF)
  2. XGBoost
  3. Neural Topic Model (NTM)
  4. DeepAR forecasting

Answer(s): B



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Perumal commented on March 01, 2024
Very useful
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Reddy commented on December 14, 2023
these are pretty useful
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Reddy commented on December 14, 2023
These are pretty useful
Anonymous
upvote

Nik commented on July 16, 2021
These study guides are the same as any other exam dums except you get them here for a very discounted price. Quality and formatting is good plus the Xengine App software is a good simulator tool which comes for free.
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