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Microsoft AI-901 Exam Syllabus Topics:
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901
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Microsoft Azure AI Fundamentals Sample Questions:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Statement 1: Evaluators in Microsoft Foundry replace the need for configuring token limits. = No Evaluators are used to assess model or agent output quality, safety, and reliability. They do not replace model configuration settings such as token limits. Microsoft describes evaluators as tools that measure the quality, safety, and reliability of AI responses.
Statement 2: Evaluators in Microsoft Foundry can assess the quality and safety of responses generated by a generative AI model. = Yes This is correct. Microsoft Foundry includes built-in evaluators for general quality metrics such as coherence and fluency, safety/security metrics, RAG metrics, and agent-specific metrics.
Statement 3: Evaluators in Microsoft Foundry can retrain a deployed generative AI model automatically when quality issues are detected. = No Evaluators measure and report quality, safety, and reliability issues. They do not automatically retrain deployed generative AI models. Microsoft describes evaluation as measuring model or agent performance against test data, while monitoring can alert when outputs fail quality thresholds.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.
Match the description to the corresponding Microsoft Foundry tools.
To answer, drag the appropriate description from the column on the left to its tool on the right. Each description may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Speech recognition is used to recognize spoken language and convert it into written text. Therefore, the correct match is:
Speech recognition = Transcribes spoken input
Speech synthesis, also known as text to speech, is used to convert text into spoken audio. Therefore, the correct match is:
Speech synthesis = Generates spoken output
The other options are incorrect for these two tools:
Extracts entities from the output is a text analytics / language processing task.
Translates spoken input is speech translation.
Translates written output is text translation.
Based on the image provided, here is the transcribed text:
You need to build an AI solution that produces new product images based on written descriptions provided by users.
Which AI workload should you use?
- A. image analysis
- B. image generation
- C. object detection
- D. optical character recognition (OCR)
Correct Answer: B 🗳️
Explanation: Only visible for TestPassed members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
Correct selection: natural language processing
The correct answer is natural language processing . Interactive question answering requires an application to receive language from a user, interpret the meaning or intent of that language, and generate or retrieve an appropriate response. These are core capabilities associated with natural language processing (NLP) and modern generative AI systems.
Microsoft's current Azure AI Fundamentals study guide places text-oriented capabilities under AI workloads
, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
. It also requires candidates to recognize scenarios involving text processing and to implement AI solutions that work with textual user input.
Computer vision is concerned with understanding or generating visual information such as images and video.
It would therefore apply to tasks such as image classification, object detection, OCR-related visual processing, or image interpretation-not ordinary text-based question answering.
Forecasting predicts future numerical or categorical values from historical data, such as future sales, demand, or resource usage. It does not describe conversational interaction with users.
Therefore, an application that interactively receives questions expressed in natural language and returns answers is fundamentally performing a natural language processing workload .
Study Guide Reference: Identify AI concepts and capabilities # Identify AI workloads # text analysis and generative AI scenarios.
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