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Microsoft AI-900 Exam (page: 5)
Microsoft Azure AI Fundamentals
Updated on: 25-Aug-2025

Viewing Page 5 of 36
AI-900 PDF 273 Questions

What is an example of the Microsoft responsible AI principle of transparency?

  1. ensuring that opportunities are allocated equally to all applicants
  2. helping users understand the decisions made by an AI system
  3. ensuring that developers are accountable for the solutions they create
  4. ensuring that the privileged data of users is stored in a secure manner

Answer(s): B

Explanation:

Transparency
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Each predictive value should be broken down into individual features or vectors by importance or impact and deliver thorough prediction explanations that can be exported into a business report for audit and compliance reviews, customer transparency, and business readiness.
Transparency Notes provide our customers with information about the intended uses, capabilities, and limitations of our AI platform services.


Reference:

https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://www.microsoft.com/en-us/ai/responsible-ai



HOTSPOT (Drag and Drop is not supported)
Select the answer that correctly completes the sentence.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: reliability and safety
Unusual or missing values is an example of the application of the     principle of responsible AI.
The handling of unusual or missing values given to an AI system falls under the reliability and safety principle of Microsoft's guidelines for responsible AI.
Note: Reliability and safety
To build trust, it's critical that AI systems operate reliably, safely, and consistently. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. How they behave and the variety of conditions they can handle reflect the range of situations and circumstances that developers anticipated during design and testing.
Reliability and safety in Azure Machine Learning: The error analysis component of the Responsible AI dashboard enables data scientists and developers to:
Get a deep understanding of how failure is distributed for a model.
Identify cohorts (subsets) of data with a higher error rate than the overall benchmark.
These discrepancies might occur when the system or model underperforms for specific demographic groups or for infrequently observed input conditions in the training data.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai



You plan to use Azure Machine Learning Studio and automated machine learning (automated ML) to build and train a model.
What should you create first?

  1. a Machine Learning workspace
  2. a Machine Learning designer pipeline
  3. a registered dataset
  4. a Jupyter notebook

Answer(s): A

Explanation:

Set up no-code AutoML training for tabular data with the studio UI Prerequisites
An Azure subscription.
*-> An Azure Machine Learning workspace.
Note: Azure Machine Learning workspace
Workspaces are places to collaborate with colleagues to create machine learning artifacts and group related work. For example, experiments, jobs, datasets, models, components, and inference endpoints.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models?



Verifying that machine learning models do NOT show racial or gender bias is an example of which Microsoft responsible AI principle?

  1. fairness
  2. privacy and security
  3. safety
  4. reliability

Answer(s): A

Explanation:

Fairness and inclusiveness in Azure Machine Learning: The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2



DRAG DROP (Drag and Drop is not supported)
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: Classifying
Box 2: Summarizing
Text summarisation is the process of creating a short and coherent version of a longer document . It is useful for helping people to discover and consume relevant information faster.
Box 3: Generating


Reference:

https://medium.com/@nfmoore/prompt-engineering-experiments-with-llms-on-azure-openai-2e5daf75fa08



HOTSPOT (Drag and Drop is not supported)
Select the answer that correctly completes the sentence.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: reliability and safety
Correctly handling unusual or missing values is an example of the application of the         principle for responsible AI.
Reliability and safety
For AI systems to be trusted, they need to be reliable and safe. It's important for a system to perform as it was originally designed and to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation.
An organization should establish rigorous testing and validation for operating conditions to ensure that the system responds safely to edge cases. It should integrate A/B testing and champion/challenger methods into the evaluation process.
An AI system's performance can degrade over time. An organization needs to establish a robust monitoring and model-tracking process to reactively and proactively measure the model's performance (and retrain it for modernization, as necessary).
Incorrect:
* Inclusiveness
Inclusiveness mandates that AI should consider all human races and experiences. Inclusive design practices can help developers understand and address potential barriers that could unintentionally exclude people.
Where possible, organizations should use speech-to-text, text-to-speech, and visual recognition technology to empower people who have hearing, visual, and other impairments.


Reference:

https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai



DRAG DROP (Drag and Drop is not supported)
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: Knowledge mining
You can use Azure Cognitive Search's knowledge mining results and populate your knowledge base of your chatbot.
Box 2: Computer vision
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis.


Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing



You have the process shown in the following exhibit.


Which type of AI solution is shown in the diagram?

  1. a chatbot
  2. a computer vision application
  3. a machine learning model
  4. a sentiment analysis solution

Answer(s): A

Explanation:

Azure AI Bot Service provides an integrated development environment for bot building. Its integration with Power Virtual Agents, a fully hosted low-code platform, enables developers of all technical abilities build conversational AI bots—no code needed.


Reference:

https://azure.microsoft.com/en-us/products/ai-services/ai-bot-service



Viewing Page 5 of 36



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