AI-901 Questions Answers - Pass AI-901 Test Guide

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| Section | Weight | Objectives |
|---|
| Topic 1: Describe Artificial Intelligence workloads and considerations | 20-25% | - Identify features of common AI workloads
- 1. Document processing and extraction
- 2. Conversational AI and chatbots
- 3. Natural language processing use cases
- 4. Computer vision use cases
- Responsible AI principles
- 1. Transparency and accountability
- 2. Fairness
- 3. Reliability and safety
- 4. Privacy and security
- 5. Inclusiveness
|
| Topic 2: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Azure NLP services
- 1. Speech services (speech-to-text, text-to-speech)
- 2. Azure AI Language
- Text analysis
- 1. Sentiment analysis
- 2. Key phrase extraction
- 3. Language detection
|
| Topic 3: Describe features of generative AI workloads on Azure | 10-15% | - Generative AI concepts
- 1. Prompt engineering basics
- 2. Large language models (LLMs)
- 3. Responsible use of generative AI
- Azure OpenAI and generative services
- 1. Azure OpenAI Service capabilities
- 2. Use cases: chatbots, content generation, summarization
|
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
- 1. Face detection and analysis (limited use cases)
- 2. Azure AI Vision
- Image analysis
- 1. Object detection
- 2. Image classification
- 3. Optical character recognition (OCR)
|
| Topic 5: Describe fundamental principles of machine learning on Azure | 25-30% | - Machine learning concepts
- 1. Training and evaluation concepts
- 2. Supervised vs unsupervised learning
- 3. Regression and classification
- Azure Machine Learning basics
- 1. AutoML concepts
- 2. Azure ML workspace components
|
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Microsoft Azure AI Fundamentals Sample Questions (Q113-Q118):
NEW QUESTION # 113
Drag and Drop Question
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 114
Select the answer that correctly complete the sentence.

Answer:
Explanation:

NEW QUESTION # 115
You are using the Azure Speech SDK to develop a Python application that supports real-time spoken conversations. Which Azure Speech class should you use to configure the connection to the Azure Speech service?
- A. SpeechSynthesizer
- B. AudioOutputConfig
- C. SpeechConfig
- D. AudioConfig
Answer: C
Explanation:
To configure the connection to the Azure Speech service, you should use the azure.cognitiveservices.speech.SpeechConfig class.
Implementation Details
Initialization: This class holds authentication and endpoint details, such as your Azure resource key and region.
Usage: You pass this configuration object into your real-time processing classes, like SpeechRecognizer (for speech-to-text) or SpeechSynthesizer (for text-to-speech).
Reference:
https://docs.azure.cn/en-us/ai-services/speech-service/how-to-recognize-speech
NEW QUESTION # 116
Hotspot Question
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:

NEW QUESTION # 117
Which type of compute resource should you use to attach an existing Azure Kubernetes Service (AKS) cluster to Azure Machine Learning?
- A. compute instance
- B. serverless compute
- C. compute cluster
- D. inference cluster
Answer: A
Explanation:
To attach an existing Azure Kubernetes Service (AKS) cluster to Azure Machine Learning, you should use the "Attached Compute" or "Kubernetes Compute Target". This allows you to leverage your existing AKS cluster as a compute resource for your machine learning tasks within Azure Machine Learning. You can achieve this using the Azure CLI v2, Python SDK v2, or Machine Learning Studio UI.
1. Kubernetes Compute Target:
Azure Machine Learning treats your AKS cluster as a compute target, allowing you to specify it as the location for running your training jobs or deploying models.
2. Attached Compute:
This refers to the ability to connect existing compute resources, like your AKS cluster, to your Azure Machine Learning workspace.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace
NEW QUESTION # 118
......
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