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NEW QUESTION # 116
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: B
Explanation:
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.
NEW QUESTION # 117
Your company deploys an AI-powered loan approval solution that enables applicants to request an explanation as to why their loan application was denied.
Which Microsoft responsible AI principle is this an example of?
Answer: A
Explanation:
The correct answer is A. transparency. Transparency means users and stakeholders should understand when AI is being used, what the AI system can do, what its limitations are, and how important outputs or decisions were produced. In this scenario, applicants can request an explanation for why a loan application was denied. That explanation capability makes the AI-assisted decision more understandable and reviewable, which directly supports transparency. Fairness is related to avoiding unjust bias or discriminatory outcomes. Privacy and security focus on protecting personal and organizational data. Inclusiveness focuses on ensuring AI systems work for people with different abilities, backgrounds, and experiences. Because the scenario is specifically about explaining a decision, the best answer is transparency.
NEW QUESTION # 118
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.
Answer:
Explanation:
NEW QUESTION # 119
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: Azure Machine Learning
You use _________ to train a model that will forecast product demand based on historical sales data.
Using Azure Machine Learning to forecast product demand based on historical sales data is best accomplished using Automated Machine Learning (AutoML) for Time-Series Forecasting. This approach allows you to train, evaluate, and deploy a high-quality model, often without writing extensive code, by automatically testing various algorithms and preprocessing data.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automl-forecasting-methods
NEW QUESTION # 120
- 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
* Content filtering controls can prevent AI-generated responses from exposing confidential and sensitive information. Answer: Yes
* AI-generated content can unintentionally reveal sensitive information if the generative AI model has access to unsecured data sources. Answer: Yes
* To prevent data exposure, only the prompts used by users must be protected by using policies. Answer:
No
* Yes - Content filtering (and related safety controls) can help reduce the chance that responses contain policy-violating or sensitive outputs by detecting and blocking certain categories of content. While filtering is not a perfect guarantee, it is a recognized control to prevent or reduce exposure risk in outputs (for example, blocking regulated data patterns, disallowed content categories, or unsafe disclosures).
* Yes - If a model (or the solution's retrieval layer) can access poorly governed repositories-such as broadly shared folders, misconfigured SharePoint sites, or unsecured databases-then the system can surface sensitive information in responses even without malicious intent. This is why access control, data classification, and permission hygiene are critical prerequisites for deploying AI assistants grounded in organizational content.
* No - Protecting only user prompts is insufficient. Data exposure can occur through multiple paths:
retrieved documents, generated outputs, logs/telemetry, training/fine-tuning data, and connector/index configuration. Preventing exposure requires layered controls: data governance (labels, DLP, least privilege), secure connectors, output filtering, auditing, and user training-not prompt policy alone.
NEW QUESTION # 121
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