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NEW QUESTION 20
A Data Scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result.
The models should be evaluated based on the following criteria:
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs
After creating each binary classification model, the Data Scientist generates the corresponding confusion matrix.
Which confusion matrix represents the model that satisfies the requirements?

• A. TN = 96, FP = 4
  FN = 10, TP = 90
• B. TN = 91, FP = 9
  FN = 22, TP = 78
• C. TN = 99, FP = 1
  FN = 21, TP = 79
• D. TN = 98, FP = 2
  FN = 18, TP = 82

Answer: D

Explanation:
The following calculations are required:
TP = True Positive
FP = False Positive
FN = False Negative
TN = True Negative
FN = False Negative
Recall = TP / (TP + FN)
False Positive Rate (FPR) = FP / (FP + TN)
Cost = 5 * FP + FN

Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective.

NEW QUESTION 21
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model What should the Specialist do to address the performance issues with the current solution?

• A. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
• B. Ensure that the input mode for the training job is set to Pipe.
• C. Use the SageMaker batch transform feature
• D. Compress the training data into Apache Parquet format.

Answer: D

NEW QUESTION 22
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

• A. Recall
• B. Mean absolute percentage error (MAPE)
• C. Misclassification rate
• D. Area Under the ROC Curve (AUC)

Answer: D

NEW QUESTION 23
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 57.92% and the predicted class frequency for Adventure is
  13.12%
• B. The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47-
  0.32)
• 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: A

NEW QUESTION 24
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

• A. Logarithmic transformation
• B. High-degree polynomial transformation
• C. One hot encoding
• D. Cross-validation
• E. Numerical value binning

Answer: D,E

NEW QUESTION 25
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