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NEW QUESTION 26
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

• A. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 -
  0.32).
• B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
  1312%
• C. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20
  85%
• D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: C

NEW QUESTION 27
A Machine Learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar- sized datasets. The team's leaders need to accelerate the training process.
What can a Machine Learning Specialist do to address this concern?

• A. Use Amazon SageMaker Pipe mode.
• B. Use Amazon Kinesis to stream the data to Amazon SageMaker.
• C. Use AWS Glue to transform the CSV dataset to the JSON format.
• D. Use Amazon Machine Learning to train the models.

Answer: A

Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure

NEW QUESTION 28
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• B. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• D. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Answer: C

NEW QUESTION 29
A company wants to predict the sale prices of houses based on available historical sales dat a. 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?

• A. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
• B. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
• C. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
• D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

Answer: D

NEW QUESTION 30
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