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[General] Microsoft Azure AI Fundamentals latest practice pdf & AI-900 free study torr

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【General】 Microsoft Azure AI Fundamentals latest practice pdf & AI-900 free study torr

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Microsoft Azure AI Fundamentals Sample Questions (Q274-Q279):NEW QUESTION # 274
To complete the sentence, select the appropriate option in the answer area.

Answer:
Explanation:


NEW QUESTION # 275
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is.
This is an example of which type of natural language processing workload?
  • A. language detection
  • B. entity recognition
  • C. key phrase extraction
  • D. sentiment analysis
Answer: D
Explanation:
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://docs.microsoft.com/en-us ... s/natural-language- processing

NEW QUESTION # 276
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:


NEW QUESTION # 277
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.

Answer:
Explanation:

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 ... e-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us ... arning-designer/eva

NEW QUESTION # 278
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:
Explanation:

Explanation:


NEW QUESTION # 279
......
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