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Microsoft AI-901 learning materials help you to easily acquire the Microsoft Azure AI Fundamentals AI-901 certification even if you have never touched the relative knowledge before. With our AI-901 Exam Questions, you will easily get the favor of executives and successfully enter the gates of famous companies.
NEW QUESTION # 45
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:
Explanation:
Statement 1: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.
Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.
Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.
NEW QUESTION # 46
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct parameter is analyzer_id. The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.
Therefore, the code should be completed as:
poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)
A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer, that value is supplied through analyzer_id when the analysis operation begins.
extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.
Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze (analyzer_id=analyzer_id, inputs=[...]), confirming the required SDK pattern
NEW QUESTION # 47
You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users. What should you configure?
Answer: B
Explanation:
In Microsoft Foundry, instructions (formerly referred to as system messages) are the primary way to define the role, behavior, tone, and constraints of your AI agent.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/agents/quickstart
NEW QUESTION # 48
You are developing a system to predict the prices of insurance for drivers in the United Kingdom. You need to minimize unintended bias in the system. What should you do?
Answer: C
Explanation:
To minimize unintended bias, the training data should be representative of the population for which the model will be used. Microsoft responsible AI guidance identifies fairness as a key principle and notes that discrepancies can occur when systems underperform for specific demographic groups or rarely observed input conditions in training data.
A global insurance dataset may not represent UK drivers. A completely random sample may still underrepresent important groups. Removing protected characteristics alone does not guarantee fairness, because other fields can act as proxies. The best answer is to use a training sample representative of the target UK population.
NEW QUESTION # 49
You are explaining generative AI workflows to a colleague. What is the first step of a generative AI workflow?
Answer: A
Explanation:
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/
NEW QUESTION # 50
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