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NEW QUESTION # 62
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: A
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Real-World Changes: External events (like a pandemic or economic shift) can suddenly make the model's training data outdated and irrelevant.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Reduced Relevance: Outputs feel outdated or fail to address contemporary topics.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 # 63
Your company is building a portfolio of AI-powered business solutions.
Company executives want to understand how Microsoft responsible AI principles can support the company's long-term goals.
Which benefit best demonstrates the importance of responsible AI? More than one answer choice may achieve the goal. Select the BEST answer.
Answer: B
Explanation:
Microsoft's Responsible AI principles serve as a strategic framework that aligns artificial intelligence development with a company's long-term business goals. By adhering to these principles, organizations can transform ethical guidelines into practical governance that supports sustainable growth.
Enhancing Stakeholder Trust
Responsible AI principles directly bolster stakeholder trust--a critical asset for long-term stability-- by ensuring transparency and accountability.
Transparency: Making AI operations and decisions understandable to users and stakeholders ensures people can see the "how" and "why" behind AI outputs.
Fairness and Inclusiveness: Treating all users equally and mitigating bias prevents discriminatory outcomes, protecting the organization's reputation.
Privacy and Security: Protecting user data through platform-level controls builds a foundation of safety that is essential for customer retention and regulatory compliance.
Reference:
https://www.preciofishbone.com/knowledge-hub/responsible-ai-in-the-ai-era-why-it-has-become-a-business-imperative
NEW QUESTION # 64
- Select the answer that correctly completes the sentence.
To ensure that your organization follows trustworthy AI principles, the organization should establish an AI governance council to __________.
Answer:
Explanation:
Explanation:
guide AI strategy, ensure responsible AI oversight, and promote alignment across business units.
A trustworthy AI program requires more than technical implementation; it requires enterprise governance :
clear ownership, risk management, policy, and cross-functional coordination. An AI governance council's primary role is to provide strategic direction and oversight so AI initiatives align with business goals while meeting Responsible/Trustworthy AI expectations (fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness).
Therefore, the best completion is that the council should guide AI strategy, ensure responsible AI oversight, and promote alignment across business units . This captures why councils are created: to avoid siloed deployments, define guardrails and approval processes, standardize evaluation/monitoring practices, and coordinate stakeholders such as legal, compliance, security, data governance, HR, and business leadership. The council also helps prioritize use cases, establish policies for data use and access, set documentation requirements, and require ongoing monitoring and incident response for AI systems in production.
The other options are narrower operational responsibilities. Configuring and deploying models in Azure is typically owned by engineering/cloud teams. Day-to-day model training and labeling is handled by data science/ML teams. The council sits above those activities to ensure the organization's AI work is consistent, accountable, and strategically aligned .
NEW QUESTION # 65
Your company uses a fine-tuned generative AI solution trained on data that is representative of the general population.
You discover that some of the generated responses include inappropriate or exclusionary language based on ableist assumptions.
You need to prevent the inappropriate responses. Your solution must minimize costs.
What should you do?
Answer: B
Explanation:
Implementing a content moderation filter is a critical strategy for fine-tuned generative AI to prevent the output of inappropriate or exclusionary language, including content rooted in ableist assumptions. These filters serve as a "digital safety layer" that intercepts biased or harmful material before it reaches the user.
How Filters Address Ableism and Exclusion
Contextual Detection: Modern filters using Large Language Models (LLMs) and Natural Language Processing (NLP) can detect subtle discriminatory phrases and slurs that traditional keyword filters might miss.
Policy-Driven Guardrails: Developers can use tools like the Lakera Guard Content Safety template to apply specific policies that flag and block ableist speech in real-time.
Customizable Classifiers: Services such as Azure AI Content Safety allow for the detection of
"abusive, derogatory, or discriminatory language" through trained statistical models.
Personalized Moderation: Emerging tools are being designed specifically to help disabled users filter out ableist hate and harassment tailored to their unique experiences.
Reference:
https://www.lakera.ai/blog/content-moderation
NEW QUESTION # 66
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
An AI champions program includes users who advocate for AI by learning, sharing, and identifying use cases.
Champions are not primarily the infrastructure team, analytics team, or executive strategy group. Their value is adoption enablement. They help colleagues understand AI tools, share practical usage patterns, collect feedback, and discover department-specific opportunities where AI can improve productivity or decision- making. Strategy and governance are usually handled by leadership, AI councils, legal, risk, compliance, security, and technical stakeholders. Infrastructure is handled by IT and engineering teams. Champions sit closer to end users and help translate the AI strategy into practical daily usage. This makes them critical for scaling proficiency and adoption across the organization.
NEW QUESTION # 67
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