NEW QUESTION 1

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft. Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

NEW QUESTION 2

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning: Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred. Finding abnormal clusters of patients.
Checking values entered into a system. Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection

NEW QUESTION 3

You run a charity event that involves posting photos of people wearing sunglasses on Twitter. You need to ensure that you only retweet photos that meet the following requirements: Include one or more faces.
Contain at least one person wearing sunglasses. What should you use to analyze the images?

• A. the Verify operation in the Face service
• B. the Detect operation in the Face service
• C. the Describe Image operation in the Computer Vision service
• D. the Analyze Image operation in the Computer Vision service

Answer: B

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview

NEW QUESTION 4

You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?

• A. Ensure that all visuals have an associated text that can be read by a screen reader.
• B. Enable autoscaling to ensure that a service scales based on demand.
• C. Provide documentation to help developers debug code.
• D. Ensure that a training dataset is representative of the population.

Answer: C

Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

NEW QUESTION 5

Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

• A. classification
• B. regression
• C. clustering

Answer: C

Explanation:
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-m

NEW QUESTION 6

To complete the sentence, select the appropriate option in the answer area.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features

NEW QUESTION 7

You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Box 1: 11

TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

NEW QUESTION 8

You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

• A. Education Level
• B. Last Name
• C. Age
• D. Income Range
• E. First Name

Answer: AC

Explanation:
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.

NEW QUESTION 9
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