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AI-900 Pdf Exam Dump | AI-900 Certification Exam Infor

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Microsoft Azure AI Fundamentals Sample Questions (Q299-Q304):

NEW QUESTION # 299
Match the Azure Cognitive Services service to the appropriate actions.
To answer, drag the appropriate service from the column on the left to its action on the right. Each service may he used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:
Explanation

NEW QUESTION # 300
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:

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

NEW QUESTION # 301
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:
Explanation

Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud.
Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

NEW QUESTION # 302
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/azure/machine-learning/studio/evaluate-model-performance

NEW QUESTION # 303
For a machine learning progress, how should you split data for training and evaluation?

A. Randomly split the data into columns for training and columns for evaluation.
B. Use labels for training and features for evaluation.
C. Use features for training and labels for evaluation.
D. Randomly split the data into rows for training and rows for evaluation.

Answer: D

Explanation:
Explanation
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data

NEW QUESTION # 304
......

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