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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 - Business benefits, ROI, and efficiency gains - Selecting generative AI solutions for business needs - Foundational concepts of generative AI |
| Topic 2: Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20–25% | - Organizational readiness and change management - Measuring success and continuous improvement - Responsible AI principles and governance - Adoption planning and scaling AI solutions |
| Topic 3: Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35–40% | - Azure AI services and Azure AI Foundry tools - Microsoft Copilot Studio capabilities - Aligning Microsoft AI tools to business requirements - Microsoft 365 Copilot features and use cases |
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NEW QUESTION # 78
- 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:
Answer Area
* Using incomplete or poor-quality data during generative AI model training can increase costs. Answer:
Yes
* AI models rely on training data to learn patterns and identify relationships to produce outputs. Answer:
Yes
* Generative AI models trained on non-representative datasets can produce inaccurate or unbalanced results. Answer: Yes
* Yes - Poor-quality or incomplete training data increases cost because it drives more iterations:
additional data cleaning, relabeling, re-training, and re-evaluation to reach acceptable performance. It can also increase operational costs after deployment if the model produces low-quality outputs that require human rework, escalations, or incident handling. In practice, data quality debt becomes model cost debt.
* Yes - Training data is the primary mechanism by which AI models learn statistical patterns and relationships. For generative models, the training corpus shapes language fluency, factual associations, style tendencies, and the kinds of content the model can produce. Without sufficient and appropriate training signals, outputs degrade.
* Yes - If the training dataset is not representative of the real-world population or business context, the model can systematically underperform for certain groups, topics, or edge cases. This can manifest as biased language, missing perspectives, and uneven accuracy, producing "unbalanced" results. That is why Responsible AI practice emphasizes representative data, evaluation across slices, and continuous monitoring.
NEW QUESTION # 79
Match the business scenario to the appropriate AI solution design approach. Each solution may be used once, more than once, or not at all.
Answer:
Explanation:
Explanation:
* The marketing department at your company wants AI to summarize emails and create presentations. Answer: Use Microsoft 365 Copilot
* The HR department at your company wants a conversational agent for policy questions and leave requests. Answer: Build with Microsoft Copilot Studio
* The manufacturing department at your company wants AI to predict maintenance schedules. Answer: Build with Azure Machine Learning
* The finance department at your company wants AI-powered access to enterprise resource planning ERP data by using familiar productivity tools. Answer: Extend with Microsoft 365 Copilot connectors These scenarios map to four distinct solution patterns: out-of-the-box productivity assistance, low-code conversational agents, predictive ML, and enterprise data integration.
Marketing's need to summarize emails and create presentations is a core "productivity copilot" use case.
Microsoft 365 Copilot is embedded in Outlook, Word, PowerPoint, and Teams, so it directly supports summarization, drafting, and presentation generation without building a custom solution-making Use Microsoft 365 Copilot the best fit.
HR's requirement is a conversational agent tailored to internal policies and workflows such as leave requests.
That typically needs custom dialog, grounded knowledge sources, and possibly actions/workflows. Microsoft Copilot Studio is designed to build and manage such agents with organizational knowledge and business process integration, so Build with Microsoft Copilot Studio fits best.
Manufacturing's predictive maintenance scheduling is classic predictive analytics: learning patterns from historical telemetry/maintenance data to forecast failures or optimal service windows. This is best addressed with Azure Machine Learning , which supports training, evaluating, and deploying custom predictive models.
Finance wants AI-powered access to ERP data "using familiar productivity tools," which implies bringing external line-of-business data into the Microsoft 365 Copilot experience. That is precisely where Microsoft
365 Copilot connectors help-indexing and exposing enterprise data sources so Copilot can reference them in a governed way-so Extend with Microsoft 365 Copilot connectors is the best approach.
NEW QUESTION # 80
HOTSPOT - 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:
Answer Area
* Larger datasets can increase the cost of a generative AI solution that uses an Azure Machine Learning workspace. Answer: Yes
* The cost of consuming Azure OpenAI models is primarily identified by the number of input and output tokens processed. Answer: Yes
* The cost of custom generative AI solutions always remains the same regardless of the model version or capability used. Answer: No
* Yes - In Azure Machine Learning, cost is driven by the resources you consume to store, process, and train. Larger datasets typically require more storage, more data transfer, and more compute time for preprocessing, training, evaluation, and experimentation. Even if you are not training foundation models, handling larger corpora can increase pipeline duration and the number/size of compute instances used, which increases overall cost.
* Yes - For Azure OpenAI usage under Standard (on-demand), pricing is primarily tied to token-based consumption (input tokens + output tokens). The more context you send and the longer the generated responses, the more tokens you consume, and the higher the cost. This is why prompt optimization, response length controls, and grounding strategies matter for cost management.
* No - Costs vary with model choice and capability. Different model families and versions have different price points, and larger/more capable models generally cost more per token or per unit of throughput. Additionally, architecture choices (RAG, vector search, caching), usage patterns, and throughput requirements can significantly change total cost-so it is never "always the same."
NEW QUESTION # 81
- Select the answer that correctly completes the sentence.
The Analyst agent in Microsoft 365 Copilot __________.
Answer:
Explanation:
Explanation:
uses structured data and provides insights by using text, charts, tables, and other visuals.
The Analyst agent in Microsoft 365 Copilot is positioned as a "data analysis" reasoning agent that helps users work through structured information (for example, tables, spreadsheets, and other dataset-like inputs) and then produces analytical outputs . The best completion is the option stating it "uses structured data and provides insights by using text, charts, tables, and other visuals," because that describes the hallmark outcome of analyst-style work: summarizing patterns, highlighting key drivers, and presenting results in formats that business users can act on. Analyst-style assistance typically includes exploring the data, identifying trends and anomalies, comparing segments, and explaining findings clearly-often accompanied by tables and visual representations that make the insights easier to consume.
The other dropdown options align to different use cases: "compiles background research for a new market or initiative" describes a research-oriented agent, "generate audio summaries" is a media summarization function, and "answer employee FAQs" describes a conversational knowledge assistant. Analyst is the one most directly associated with structured-data interpretation and producing a mix of narrative plus analytical artifacts (tables/charts) to communicate conclusions.
NEW QUESTION # 82
- 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 # 83
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The Microsoft AB-731 certification exam offers a great opportunity for Microsoft professionals to demonstrate their expertise and knowledge level. In return, they can become competitive and updated with the latest technologies and trends. To do this they just need to enroll in AI Transformation Leader (AB-731) certification exam and have to put all efforts and resources to pass this challenging AB-731 exam. You should also keep in mind that to get success in the Microsoft AB-731 exam is not an easy task.
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