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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Available Languages: | German, Chinese (Simplified), French, Japanese, Portuguese (Brazil), Spanish, Korean, English |
| Passing Score: | 700/1000 |
| Related Certifications: | Microsoft Azure Data Fundamentals Microsoft Azure Fundamentals Microsoft Azure AI Engineer Associate |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Exam Duration: | 85 minutes |
| Exam Format: | Multiple response, Multiple choice, Drag and drop, Case studies |
| Exam Price: | USD 99 (varies by region) |
| Real Exam Qty: | 40-60 |
| Recommended Training: | Azure AI Fundamentals Practice Modules Microsoft Learn AI-900 Learning Path |
| Exam Registration: | Pearson VUE Exam Registration Official Microsoft Certification Page |
| Sample Questions: | Microsoft AI-900 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required; basic understanding of cloud and AI concepts recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/ |
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In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:
NEW QUESTION # 81
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
NEW QUESTION # 82
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/Translator/translator-info-overview
https://docs.microsoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-note
NEW QUESTION # 83
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 84
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Speech recognition means Speech to Text. In the above example as a person speaks the words are converted into text of the same language. Hence Speech to Text also called Speech recognition is the right answer.
Speech recognition - the ability to detect and interpret spoken input.
Speech synthesis - the ability to generate spoken output.
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction
NEW QUESTION # 85
You are building a tool that will process images from retail stores and identity the products of competitors.
The solution must be trained on images provided by your company.
Which Azure Al service should you use?
Answer: B
NEW QUESTION # 86
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