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NEW QUESTION # 10
HOTSPOT - Select the answer that correctly completes the sentence.
You use __________ to train a model that will forecast product demand based on historical sales data.
Answer:
Explanation:
Explanation:
Azure Machine Learning
Forecasting product demand from historical sales data is a predictive analytics / machine learning use case. It typically requires selecting an appropriate forecasting approach (for example, regression, tree-based methods, or time-series models), preparing and splitting historical data, training and validating the model, tuning hyperparameters, and then deploying the model for ongoing inference. The Microsoft service designed to support that end-to-end ML lifecycle is Azure Machine Learning, which is why it correctly completes the sentence.
Azure Machine Learning provides the tooling and infrastructure to: manage datasets, run training jobs on scalable compute, track experiments, compare model performance, register models, and operationalize them through managed endpoints and pipelines. This makes it well-suited for iterative forecasting work, where you may retrain on new data regularly, monitor drift, and update models as product lines, promotions, or seasonality patterns change.
The other options do not directly fit "train a model" for forecasting. Azure AI Search is an indexing/retrieval service used to search and ground generative AI responses, not for training predictive models. Azure OpenAI provides access to large language and multimodal models for generative tasks (drafting, summarizing, Q & A) and is not the primary platform for building classical forecasting models. Microsoft Foundry is a broader platform experience for building and governing AI apps and agents, but the specific service for training a forecasting model on historical sales data is Azure Machine Learning.
NEW QUESTION # 11
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 manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
The answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
The answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
NEW QUESTION # 12
Your company is developing an AI-powered customer support agent.
You need to ensure that the solution follows Microsoft responsible AI principles.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
[B]
Under Microsoft's Responsible AI framework, this testing specifically addresses the principles of Fairness and Inclusiveness. To operationalize these, you should focus on the following testing and development areas:
Key Testing Focus Areas
*-> Cultural Sensitivity: Ensure the agent respects different values, perspectives, and international contexts to avoid causing offense or misunderstanding.
*-> Fairness and Bias Mitigation: Test the agent to ensure it treats all users equitably and does not reinforce societal stereotypes or discriminate based on protected characteristics like race, gender, or religion.
Accessibility: Validate that the agent is usable by people of all abilities, providing equal power and engagement regardless of their background.
Global Community Engagement: Involve diverse users and underserved communities in the pre- deployment validation and feedback process to identify representation gaps.
[E]
To align with Microsoft's Responsible AI principles, providing a clear disclaimer that users are interacting with an AI solution is a core requirement of the Transparency principle.
Under this principle and the Microsoft Responsible AI Standard, developers must ensure that AI systems are understandable and that users are not deceived into believing they are interacting with a human.
Reference:
https://learn.microsoft.com/en-us/legal/ai-code-of-conduct
https://www.microsoft.com/en-us/ai/principles-and-approach
NEW QUESTION # 13
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 14
Your company plans to use generative AI to help summarize and analyze internal business documents.
You need to recommend a solution to prevent generative AI from accessing confidential or classified information.
What should you include in the recommendation?
Answer: C
Explanation:
Data governance is essential to prevent generative AI (GenAI) from accessing confidential or classified information, as it provides the necessary framework, policies, and controls to manage data security throughout the AI lifecycle. Effective governance involves a combination of data classification, strict access controls, and active monitoring to ensure only authorized, non- sensitive data is used.
Reference:
https://dacodes.com/blog/data-governance-best-practices-for-generative-ai
NEW QUESTION # 15
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