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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify the business value of generative AI solutions | 35–40% | - Differences between generative AI and other AI types - Foundational concepts of generative AI - Selecting generative AI solutions for business needs - Business benefits, ROI, and efficiency gains |
| Topic 2: Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20–25% | - Responsible AI principles and governance - Organizational readiness and change management - Measuring success and continuous improvement - Adoption planning and scaling AI solutions |
| Topic 3: Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35–40% | - Aligning Microsoft AI tools to business requirements - Microsoft Copilot Studio capabilities - Microsoft 365 Copilot features and use cases - Azure AI services and Azure AI Foundry tools |
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NEW QUESTION # 30
Your company has a Microsoft 365 subscription and uses Microsoft 365 Copilot Chat.
Some users need to build and use declarative agents that can access work data.
Which type of license should you recommend for the users?
Answer: C
Explanation:
To use declarative agents that access work data (such as SharePoint or Graph connectors), users generally need a Microsoft 365 Copilot add-on license.
While Microsoft 365 Copilot Chat is included in most business subscriptions at no extra cost, its native capabilities are primarily limited to web-grounding and basic instructions. Accessing organizational data via agents typically requires one of the following licensing paths:
1. Microsoft 365 Copilot Add-on License
This is the most direct method. It grants full access to:
Declarative agents grounded in tenant data without additional usage fees.
Copilot Studio for authoring and managing these agents.
Embedded Copilot features in Word, Excel, Outlook, and Teams.
2. Pay-As-You-Go (Consumption) Model
If users do not have a full Copilot add-on license, organizations can enable metered usage (consumption-based billing).
Usage-based billing: Interactions with agents that access tenant data (SharePoint, connectors) consume "Copilot credits".
Requirement: This requires an Azure subscription and a billing policy set up in the Microsoft 365 admin center.
Reference:
https://support.microsoft.com/en-gb/topic/how-copilot-chat-works-with-and-without-a-microsoft-
365-copilot-license-5810b659-fbe0-48ee-9fe6-d731fe86cdeb
NEW QUESTION # 31
Your company creates a custom Azure Machine Learning model that uses a generative AI assistant. The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate. What is a possible cause of the issue?
Answer: A
Explanation:
A common reason models degrade after being successful in production is data drift (also called concept drift). Over time, the distribution of input data changes -for example, customer behavior shifts, product catalog changes, seasonality changes, new categories appear, sensors get recalibrated, or business processes evolve. When the model sees data that differs from what it was trained on, its predictions can become less accurate. This is exactly what option A describes and is the most likely "six months later" cause.
Option B is not a primary explanation for reduced predictive accuracy. More compute can improve throughput
/latency, but it does not inherently improve correctness of predictions. If anything, compute constraints typically cause timeouts or slower responses, not a systematic accuracy drop.
Option C (trained incorrectly) would usually manifest earlier-poor performance from the start-unless the
"incorrectness" is that the model was trained on a snapshot that later became stale (which again maps back to drift). The correct operational response is to monitor for drift, validate performance regularly, and retrain
/refresh the model using newer representative data and updated features/labels.
NEW QUESTION # 32
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
* A generative AI model guarantees factually accurate responses if the model is trained on a large dataset. Answer: No
* Content filtering and responsible AI safeguards help a generative AI model generate safe and inoffensive content. Answer: Yes
* A generative AI model always produces fair and unbiased results when the training data has been properly prepared and reviewed for fairness. Answer: No
* No - A larger training dataset can improve coverage and fluency, but it does not guarantee factual accuracy. Generative models can still hallucinate, mix concepts, or produce plausible-but-incorrect statements because they generate likely text rather than verifying truth. This is why solution designs commonly add grounding/retrieval, validation, and human review for high-stakes outputs.
* Yes - Content filtering and Responsible AI controls are specifically used to reduce harmful, unsafe, or policy-violating outputs . In practice, safeguards include input/output filters, safety classifiers, and governance controls that help enforce safety policies and minimize offensive content. These controls don't make outputs "perfect," but they materially reduce risk and are a standard part of production AI deployments.
* No - Even with careful data preparation and fairness reviews, models can still produce biased outcomes due to residual bias in data, label/measurement issues, deployment context, and shifting real- world distributions. "Always fair and unbiased" is an absolute claim that is not achievable in real systems; fairness is managed through continuous evaluation, monitoring, and mitigations-not assumed as guaranteed.
NEW QUESTION # 33
- What should you use for each task? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Extracting structured data from forms and invoices: Answer: Azure Document Intelligence in Foundry Tools
* Summarizing written content from business reports: Answer: Azure Language in Foundry Tools
* Generating descriptive text for uploaded images: Answer: Azure Vision in Foundry Tools These three tasks align to three different Azure AI capability families: document processing, language understanding/generation, and computer vision.
* Forms and invoices are semi-structured documents where the business need is to extract specific fields (IDs, names, totals, dates) reliably into structured output. Azure Document Intelligence is designed for intelligent document processing and includes prebuilt models (such as invoices) as well as custom extraction options, making it the correct choice for structured data extraction from documents.
* Summarizing written business reports is an NLP task focused on compressing long text into key points, themes, and action items. Azure Language provides language processing capabilities (including summarization features within language service capabilities), so it is the best fit for summarization scenarios.
* Generating descriptive text for images (image captioning/description) is a computer vision task.
Azure Vision can analyze uploaded images and return descriptions/captions and other visual insights, which directly matches the requirement to produce descriptive text from images.
NEW QUESTION # 34
You need to create a custom Azure Machine Learning model. The data used to train the model is consistent and uniform.
What should you do first?
Answer: A
Explanation:
The first step in creating a custom Azure Machine Learning model trained on your data is to acquire and prepare the data. This involves activities such as:
Data Collection: Gathering the relevant data from its sources, such as databases, streaming sources, or Azure Blob storage.
Data Cleaning and Preprocessing: Even with consistent and uniform data, you will need to perform steps like handling missing values, removing duplicates, and ensuring standardization.
Data Transformation and Feature Engineering: Converting the raw data into a format suitable for the chosen machine learning algorithm and creating new features that can improve model performance.
Data Splitting: Dividing the dataset into separate training, validation, and testing sets so the model can be trained on one portion and evaluated on data it hasn't seen before.
Note:
Once the data is prepared and ready, the subsequent steps in Azure Machine Learning typically involve:
1. Setting up an Azure Machine Learning workspace if you don't already have one.
2. Creating a data asset within the workspace that points to your data in Azure storage.
3. Configuring compute resources for training the model.
4, Selecting an appropriate model algorithm and writing a training script (or using automated ML features).
5. Training and tuning the model using the prepared data and compute resources Reference:
https://medium.com/@offpagework1.datatrained/building-custom-r-models-in-azure-machine- learning-is-easy-e548598c6325
NEW QUESTION # 35
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