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The Microsoft AI-900 exam consists of 40-60 multiple-choice questions and has a time limit of 60 minutes. To pass the exam, candidates must achieve a minimum score of 700 out of 1000. AI-900 Exam can be taken online or through a testing center, and the cost of the exam varies by location.
NEW QUESTION # 62
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 63
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: A feature
Box 2: A label
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results
NEW QUESTION # 64
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 65
Which metric can you use to evaluate a classification model?
Answer: D
Explanation:
For evaluating a classification model, the appropriate metric from the options provided is the True Positive Rate (TPR), also known as Sensitivity or Recall. According to the Microsoft Azure AI Fundamentals (AI-
900) official study guide and Microsoft Learn module "Evaluate model performance", classification models are evaluated using metrics that measure how accurately the model predicts categorical outcomes such as "yes
/no," "spam/not spam," or "approved/denied."
The True Positive Rate measures the proportion of correctly identified positive cases out of all actual positive cases. Mathematically, it is expressed as:
True Positive Rate (Recall)=True PositivesTrue Positives + False Negatives\text{True Positive Rate (Recall)}
= \frac{\text{True Positives}}{\text{True Positives + False Negatives}}True Positive Rate (Recall)
=True Positives + False NegativesTrue Positives
This metric is important when missing positive predictions carries a high cost, such as in medical diagnosis or fraud detection. Microsoft Learn highlights classification evaluation metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve) as suitable for classification models.
The other options-Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²)-are regression metrics used to evaluate models that predict numeric values rather than categories. For example, they apply to predicting house prices or temperatures, not yes/no decisions.
Therefore, the correct classification evaluation metric among the choices is A. True Positive Rate.
Reference:Microsoft Learn - Evaluate model performance - Understand metrics for classification and regression models
NEW QUESTION # 66
To complete the sentence, select the appropriate option in the answer area.
Computer vision capabilities can be Deployed to....................
Answer:
Explanation:
see the answer in below Explanation
Integrate a facial recognition feature into an app.
NEW QUESTION # 67
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