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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Exam AB-731: AI Transformation Leader |
| Exam Number: | AB-731 |
| Available Languages: | Japanese, German, Chinese (Simplified), English, French, Spanish |
| Passing Score: | 700 |
| Exam Format: | Drag and drop, Multiple response, Case studies, Yes/No, Multiple choice |
| Certificate Validity Period: | 12 months |
| Real Exam Qty: | 40–60 |
| Exam Price: | $99 USD |
| Exam Duration: | 45–65 |
| Recommended Training: | AB-731T00: AI Transformation Leader |
| Exam Registration: | Microsoft Certification Registration |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online proctored or onsite at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience in business transformation, change management, and familiarity with Microsoft 365 and Azure AI services |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
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質問 # 17
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?
正解：A
解説：
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.
質問 # 18
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Answer Area
* Microsoft 365 Copilot connectors enable you to index data from multiple sources to make the data available in Copilot. Answer: Yes
* You can build a custom Microsoft 365 Copilot connector when the available connectors do NOT meet your data integration requirements. Answer: Yes
* To use Microsoft 365 Copilot connectors, you need a Microsoft Copilot Studio license. Answer: No
* Yes - Microsoft 365 Copilot connectors (including synced connectors) are designed to bring external data into Microsoft Graph so it can be semantically indexed and surfaced in Microsoft 365 Copilot experiences. Microsoft explicitly states that synced connectors ingest/crawl content into Microsoft Graph where it's indexed and then available for Copilot prompts and citations.
* Yes - When Microsoft-provided connectors don't meet integration needs, organizations can create custom connectors (often referred to as Microsoft Graph connectors / custom connector development) to connect other data sources. This is a common extensibility path to index line-of-business repositories and make that content discoverable via Copilot and Microsoft Search.
* No - Using Microsoft 365 Copilot connectors does not require a Copilot Studio license. Connectors are generally configured and managed in Microsoft 365 admin/search experiences, and Microsoft's licensing guidance indicates that users can view connector data in Microsoft 365 Copilot and Microsoft Search with valid Microsoft 365/Office 365 licensing-Copilot Studio licensing is about building agents in Copilot Studio, not a prerequisite to use connectors.
質問 # 19
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: Yes
Yes - A barrier to AI adoption can include data limitations and lock of AI readiness and skills.
Those are two of the most significant hurdles organizations face today. In fact, a 2025 PEX Report found that 52% of professionals cite data quality and availability as their primary challenge, closely followed by a lack of internal expertise at 49%.
Box 2: Yes
Yes - Organizations often struggle with AI adoptions because they prioritize technology selection before defining clear business use cases.
It is essentially the "hammer looking for a nail" problem; organizations often get dazzled by the
"hammer" (AI) and start swinging before they even know what they are trying to build.
Starting with technology rather than business value is a primary reason why 88% to 95% of AI pilots fail to deliver measurable results. This "technology-first" trap often leads to "random acts of AI"-expensive experiments that succeed in a lab but fail to solve any real operational bottlenecks.
Box 3: Yes
Yes - A lack of cross-functional collaboration is a common barrier to AI adoption.
That is spot on-it's often the "human" silos, not the hardware, that stall AI progress. While technical hurdles like data quality are significant, research consistently identifies a lack of cross- functional collaboration as a primary "hidden" barrier to successful AI adoption.
Reference:
https://www.ibm.com/think/insights/ai-adoption-challenges
https://www.rapidops.com/blog/why-ai-fails/
https://www.gartner.com/peer-community/post/preferred-tactics-building-effective-collaboration- cross-functional-teams-involved-ai-governance-risk-management-e-g-joint
質問 # 20
For each of the following statements, select Yes if the statement is true. Otherwise, select No . NOTE: Each correct selection is worth one point.
正解：
解説：
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.
The 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.
質問 # 21
Which statement accurately describes the difference between a pretrained generative AI model and a fine- tuned generative AI model?
正解：D
解説：
A pretrained generative AI model is trained initially on a large, broad, and diverse dataset so it learns general language (or multimodal) patterns and capabilities. Fine-tuning then takes that pretrained base and performs additional training on a smaller, task- or domain-specific dataset to specialize behavior- improving performance for a particular use case, tone, style, or domain knowledge representation. That is exactly what option C states, making it the correct answer.
Option A is incorrect because both pretraining and fine-tuning may use labeled or unlabeled data depending on the technique; the distinction is not "labeled vs. unlabeled." Option B is incorrect because a pretrained model is not "faster to train" due to fewer parameters; pretraining is typically the most compute-intensive phase precisely because it's done at large scale, while fine-tuning is smaller but still trains the same model architecture. Option D is reversed: the pretrained model is the general-purpose foundation, while the fine- tuned model is the specialized variant for a specific task or dataset.
質問 # 22
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