Microsoft AI-901 Deutsche, AI-901 Testengine

In den letzten Jahren ist die Microsoft AI-901 Zertifizierungsprüfung schon eine der einflussreichsten Zertiftierungsprüfung in Bezug auf das Computer geworden. Aber wie kann man die Microsoft AI-901 Zertifizierungsprüfung mühlos bestehen? Unser ZertFragen kann Ihnen immer helfen, dieses Problem schnell zu lösen, indem wir Ihnen die AI-901 Schulungsunterlagen zu AI-901 Zertifikationsprüfung anbieten. Die Inhalte der AI-901 Zertifizierungsprüfung bestehen aus den neuesten Prüfungsmaterialien von den IT-Fachleuten.

Microsoft AI-901 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Thema 2
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

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Microsoft AI-901 Fragen und Antworten, Microsoft Azure AI Fundamentals Prüfungsfragen

Die Microsoft AI-901 Prüfungsfragen von ZertFragen sind in Übereinstimmung mit dem neuesten Lehrplan und der echten Microsoft AI-901 Zertifizierungsprüfung. Wir aktualisieren auch ständig unsere Schulungsunterlagen. Alle Produkte erhalten Sie mit einjährigen kostenlosen Updates. Sie können auch das Abozeit verlängern, so dass Sie mehr Zeit bekommen, um sich besser auf die Prüfung vorzubereiten. Wenn Sie zögert sind oder nicht dafür entscheiden können, ob Sie die Microsoft AI-901 Schulungsunterlagen von ZertFragen kaufen oder nicht. Dann können Sie die Demo umsonst auf der ZertFragen website herunterladen. Wenn es Ihnen passt, dann gehen Sie zum Kaufen ohne Bereuung.

Microsoft Azure AI Fundamentals AI-901 Prüfungsfragen mit Lösungen (Q47-Q52):

47. Frage
You are developing a lightweight application that will call an agent programmatically by using the Microsoft Foundry SDK. You need to identify the agent ID. What should you do?

Antwort: B

Begründung:
When calling a Foundry agent programmatically by using the Microsoft Foundry SDK, the application needs the agent's configuration values, including the agent ID . The Foundry playground provides a Code view that shows sample application code and environment variables needed to call the agent.
The other options are incorrect:
A). copy the tool name used by the agent identifies a tool, not the agent.
B). copy the model family name identifies the model family, not the agent instance.
C). copy the project name identifies the project, not the specific agent ID.


48. Frage
You have a Microsoft Foundry project that has a generative AI model deployment.
You need to ensure that responses generated by the model minimize costs and remain within a defined length.
Which parameter should you configure?

Antwort: C

Begründung:
To minimize cost and keep generated responses within a defined length, configure Max Completion Tokens.
Microsoft's Azure OpenAI / Foundry API reference defines max_completion_tokens as an upper bound for the number of tokens that can be generated for a completion. Because generated tokens contribute to usage and response length, limiting completion tokens helps control both output length and cost.
Temperature and Top P control randomness or sampling behavior, not maximum response length. Model version settings do not directly define the generated response length.


49. Frage
Hotspot Question
Select the answer that correctly completes the sentence.

Antwort:

Begründung:

Explanation:
Box: analyzes digital images to detect objects
A computer vision model analyzes digital images and video frames to locate, identify, and classify specific items by drawing bounding boxes around them.
How It Works
Input: The system ingests a raw image or video stream.
Pre-processing: The model normalizes pixel data, adjusts contrast, and clears noise.
Feature Extraction: Deep learning architectures like Convolutional Neural Networks (CNNs) scan for lines, shapes, and textures.
Classification and Localization: The model categorizes what the object is and maps out exact coordinates.
Reference:
https://www.ibm.com/think/topics/computer-vision


50. Frage
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments. This is an example of which Microsoft guiding principle for responsible AI?

Antwort: C

Begründung:
An AI system designed to empower everyone, including people with hearing, visual, and other impairments, is a direct example of the inclusiveness guiding principle for responsible AI.
Accessibility: It ensures technology adapts to human needs rather than forcing humans to adapt to technology.
Equal Opportunity: It provides users of all abilities with the same access to information, tools, and digital experiences.
Diverse Design: It reflects a development process that intentionally considers and incorporates the needs of historically underserved populations.
Reference:
https://uitop.design/blog/design/inclusive-design/


51. Frage
You are explaining generative AI workflows to a colleague. What is the first step of a generative AI workflow?

Antwort: D

Begründung:
Training a generative AI model using data is a foundational step in the workflow, but it is preceded by data collection and preparation. After the model is trained, subsequent steps involve validation, generation, and refinement.
Steps in a generative AI workflow
1. Data Gathering: Collect a massive, relevant dataset (images, text, audio, etc.) that will be used for training.
2. Data Preparation: Clean and preprocess the raw data. This includes removing errors, standardizing formats, and structuring the data so the model can comprehend it.
*-> 3. Model Training: Feed the prepared data into the model. During this stage, the model learns patterns and relationships within the data to understand how to create new content.
4. Model Validation: Test the model on a separate set of data to evaluate its performance and accuracy.
5. Content Generation: Use the trained and validated model to create new content based on prompts.
6. Refinement: Fine-tune the model using human feedback or additional data to improve its output and address specific tasks.
Reference:
https://www.tricension.com/domainai/proper-training-for-generative-ai/


52. Frage
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AI-901 Testengine: https://www.zertfragen.com/AI-901_prufung.html