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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Certified: AI Transformation Leader |
| Exam Number: | AB-731 |
| Available Languages: | English |
| Passing Score: | 700 / 1000 |
| Exam Format: | Drag and drop, Case studies, Build list, Multiple choice |
| Real Exam Qty: | 40-60 |
| Exam Price: | USD 99 |
| Exam Duration: | 45 minutes |
| Certificate Validity Period: | 1 year (renewable) |
| Related Certifications: | Microsoft Certified: AI Transformation Leader |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online or at a test centre |
| Pre Condition: | No formal prerequisites. Recommended: familiarity with Microsoft 365 services, Azure AI services, and experience with adoption or change management in a business context. This certification is designed for business decision-makers at all levels; no coding is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
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NEW QUESTION # 32
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is the process of ___________________.
Prompt engineering is the process of crafting, evaluating, and improving prompts to gain more accurate outputs from an AI model. Factors that improve prompts include the LLM's preferred format, specificity of language, appropriately identifying the audience's expectations, and making function calls for external data.
At its core, prompt engineering is about reducing ambiguity so the model doesn't have to "guess" what you want. It's the bridge between a vague idea and a high-quality output.
Beyond just clarity, modern prompting often involves specific frameworks like Chain-of-Thought (asking the AI to think step-by-step) or Few-Shot Prompting (providing examples) to significantly improve reasoning and accuracy.
Reference:
https://www.linkedin.com/pulse/using-prompt-engineering-optimize-genai-models-iabac-nfa9c
NEW QUESTION # 33
- For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
Answer Area
* 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 # 34
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
* Microsoft Copilot provides a single AI app that has identical features and experiences across all Microsoft products. Answer: No
* Microsoft 365 Copilot delivers AI capabilities for business users that use Microsoft 365 apps. Answer:
Yes
* Microsoft Security Copilot helps companies understand risks and the organizational security posture.
answer: Yes
* No - "Copilot" is an umbrella brand across Microsoft, but the experiences are not identical . Different Copilots target different workloads (productivity, security, development, business apps) and therefore expose different capabilities, connectors, permissions models, and admin controls. For example, Microsoft 365 Copilot is embedded in Word/Excel/PowerPoint/Outlook/Teams, while Security Copilot is built for SOC workflows and integrates with security tooling; they are intentionally not the same app with the same features.
* Yes - Microsoft 365 Copilot is specifically designed to deliver generative AI assistance for users working in Microsoft 365 applications . It enhances common business tasks such as drafting, summarizing, meeting recap, creating presentations, and working with documents and communications-directly inside the Microsoft 365 productivity suite.
* Yes - Microsoft Security Copilot is focused on security operations and helps analysts understand threats, investigate incidents, and improve visibility into security posture. Its purpose is aligned with helping organizations interpret security signals and risk context more efficiently, which supports understanding organizational risk and posture.
NEW QUESTION # 35
- Select the answer that correctly completes the sentence.
The cost of using generative AI language models is based typically on the number of __________ processed.
Answer:
Explanation:
Explanation:
Most generative AI language model pricing is based on token consumption , which measures the amount of text processed by the model. Tokens are sub-word units used internally by language models (for example, parts of words, whole words, or punctuation). When you send a prompt, the model consumes input tokens (your prompt + any system instructions + retrieved grounding context). When it generates a response, it consumes output tokens (the generated completion). Costs typically scale with the total input + output tokens processed, which is why long prompts, large grounding passages, and lengthy responses increase spend. This also explains why prompt optimization, response length limits, caching, and careful grounding are common cost-control techniques in enterprise solutions.
By contrast, "documents" is too coarse (a document can be 1 page or 500 pages). "Requests" is not the primary unit for most LLM pricing models because request sizes vary dramatically. "Words" is not used because the model's actual compute unit is tokens, and tokenization differs across languages and text patterns.
Therefore, the most accurate completion is tokens .
NEW QUESTION # 36
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:
* A text-to-image generator can be used to translate content into other languages. Answer: No
* A predictive analytics model can generate new marketing content for a company's online ads. Answer:
No
* A generative AI chatbot can engage customers in personalized conversations and recommend products.Answer: Yes
* No - A text-to-image generator's primary function is to create images from text prompts , not translate text between languages. Translation is a natural language processing task typically handled by language models or dedicated translation services. A text-to-image model could illustrate translated content (e.g., generate an image based on a translated prompt), but it is not the tool used to perform the translation itself.
* No - Predictive analytics models are designed to predict outcomes (forecasts, probabilities, classifications) from historical patterns, such as predicting click-through rate, churn, or next-quarter demand. They are not designed to create new ad copy or marketing creatives. Generating new marketing content is a generative AI capability (text generation), not predictive analytics.
* Yes - A generative AI chatbot is well-suited to interactive, natural-language conversations . With access to product catalogs and business rules, it can ask clarifying questions, tailor responses to customer needs, and recommend products (for example, suggesting tents based on group size, season, and budget). This combines conversational generation with retrieval/recommendation logic behind the scenes, enabling personalized customer engagement at scale.
NEW QUESTION # 37
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