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NEW QUESTION # 75
Your company uses generative AI to assist with content creation and customer interactions.
You need to evaluate whether Azure Machine Learning can add value to the current customer management.
For which use case should you use Machine Learning?
Answer: D
Explanation:
The correct answer is D. predicting customer retention. Azure Machine Learning is best suited for predictive analytics scenarios where historical data is used to train a model that predicts future outcomes. Customer retention prediction typically uses customer behavior, purchase history, engagement data, service interactions, churn signals, and account attributes to estimate whether a customer is likely to stay or leave. That is a classic machine learning use case. Generating marketing campaigns and summarizing customer service transcripts are generative AI or natural language processing tasks. Creating product descriptions from images is a multimodal or generative AI scenario.
The wording "predicting" is the key indicator: when the business requirement is to forecast outcomes from patterns in data, Azure Machine Learning is the correct fit.
NEW QUESTION # 76
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: C
Explanation:
This phenomenon is known as data drift.
It is one of the top reasons model accuracy declines after deployment. In the context of a generative AI assistant, data drift occurs when the statistical properties or distributions of the input data (the prompts or context provided by users) change significantly from what the model was originally trained on.
Key Causes of Data Drift
Changing User Behavior: Users may start interacting with the assistant differently, using new slang, jargon, or evolving topics of interest.
Real-World Changes: External events (like a pandemic or economic shift) can suddenly make the model's training data outdated and irrelevant.
Data Pipeline Issues: Changes in how data is collected, such as updated sensors or modified web forms, can alter the format or scale of inputs.
Seasonality: Patterns may fluctuate based on the time of year, such as different holiday shopping behaviors.
Impact on Generative AI
For a generative assistant, data drift often manifests as:
Reduced Relevance: Outputs feel outdated or fail to address contemporary topics.
Increased Hallucinations: When faced with unfamiliar input patterns, the model may produce factually incorrect or nonsensical responses.
Loss of Quality: Outputs may become less creative, more repetitive, or exhibit biased behavior.
Reference:
https://nexla.com/ai-infrastructure/data-drift
NEW QUESTION # 77
Your company manages a website that publishes daily news articles. You need to recommend an AI solution that can analyze text and identify the main people, locations, and companies mentioned in the articles. What should you include in the recommendation?
Answer: C
Explanation:
The requirement is to analyze text and identify "people, locations, and companies" mentioned in news articles. This is a classic Named Entity Recognition (NER) / entity extraction scenario, which falls under natural language processing. Azure Language in Foundry Tools is the correct choice because it provides text analytics capabilities that detect and categorize entities in unstructured text-commonly including Person, Location, and Organization. This enables downstream experiences such as topic tagging, search filters (e.g.,
"all articles mentioning Company X"), trend dashboards (top people/places mentioned this week), and improved content discovery.
The other options do not match the requirement. Content Safety focuses on moderating harmful or policy- violating content (for example, hate, violence, self-harm, sexual content) and is not the primary tool for extracting named entities. Azure Vision is for analyzing images and performing OCR; it would only be relevant if the articles were images or scans, but the task here is entity extraction from text articles. Azure Speech is for speech-to-text, text-to-speech, and audio analysis; it would be used if your input were audio recordings rather than written articles.
Therefore, to identify key entities (people, locations, companies) from daily news article text, the best recommendation is Azure Language in Foundry Tools.
NEW QUESTION # 78
Your company plans to use generative AI to help build a website that will showcase various existing products. Which capability best describes a benefit of using generative AI for this project?
More than one answer choice may achieve the goal. Select the BEST answer.
Answer: A
Explanation:
Generative AI (GenAI) can significantly streamline the creation and localization of a product showcase website. Beyond standard word-for-word translation, GenAI tools--particularly those powered by Large Language Models (LLMs)--provide context-aware localization that adapts product descriptions to specific cultural nuances and brand tones.
Key Benefits of GenAI for Product Descriptions
Context-Aware Translation: Unlike older machine translation, GenAI understands the "intent" behind phrases, correctly handling idioms and technical terminology relevant to the product category.
Tone & Style Adaptation: You can instruct AI to translate a description into a "professional,"
"creative," or "technical" tone depending on the target audience.
Scalability: GenAI can process thousands of product listings simultaneously, reducing time-to- market by up to 90% compared to manual translation.
SEO Optimization: Some tools can automatically incorporate localized keywords into the translated description to improve search visibility in different regions.
Reference:
https://amplience.com/blog/create-personalized-product-descriptions-that-convert-with-ai
NEW QUESTION # 79
Your company purchases Microsoft 365 Copilot for its sales department.
The sales department needs to find and summarize information across internal documents quickly.
From which two data sources can the sales department obtain results by default? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
With Microsoft 365 Copilot, the two primary data sources used to ground data with internal documents are:
SharePoint
OneDrive
These sources allow Copilot to access, analyze, and summarize files (such as Word documents, PDFs, Excel files, and PowerPoint presentations) stored within your organization's Microsoft 365 tenant. Other sources mentioned in the context of grounding include Microsoft Teams chat history and emails.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-copilot-studio
NEW QUESTION # 80
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