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NEW QUESTION # 49
- Select the answer that correctly completes the sentence.
An organization that runs continuous, large-scale workloads with Azure OpenAI models should choose the
__________ pricing model.
Answer:
Explanation:
Explanation:
Provisioned (PTUs)
For continuous, large-scale workloads, the key needs are predictable throughput, consistent latency, and cost
/performance stability. Azure OpenAI Provisioned Throughput Units (PTUs) are designed for this scenario because you reserve model capacity to meet sustained demand. This reduces the risk of variability that can occur with purely on-demand usage during peak periods and provides a more predictable operating model when the workload is always "on" and high volume.
Standard (On-Demand) is best when usage is variable or you're starting small (PoCs, pilots, spiky workloads), because it is flexible pay-as-you-go but can be less predictable at sustained scale. Batch API is optimized for asynchronous, non-interactive processing where you can tolerate delayed results (for example, large offline summarization jobs), not for always-on, real-time interactions at scale.
Therefore, for continuous high-volume production workloads, Provisioned (PTUs) is the best pricing model choice.
NEW QUESTION # 50
- 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
* Using incomplete or poor-quality data during generative AI model training can increase costs. Answer:
Yes
* AI models rely on training data to learn patterns and identify relationships to produce outputs. Answer:
Yes
* Generative AI models trained on non-representative datasets can produce inaccurate or unbalanced results. Answer: Yes
* Yes - Poor-quality or incomplete training data increases cost because it drives more iterations:
additional data cleaning, relabeling, re-training, and re-evaluation to reach acceptable performance. It can also increase operational costs after deployment if the model produces low-quality outputs that require human rework, escalations, or incident handling. In practice, data quality debt becomes model cost debt.
* Yes - Training data is the primary mechanism by which AI models learn statistical patterns and relationships. For generative models, the training corpus shapes language fluency, factual associations, style tendencies, and the kinds of content the model can produce. Without sufficient and appropriate training signals, outputs degrade.
* Yes - If the training dataset is not representative of the real-world population or business context, the model can systematically underperform for certain groups, topics, or edge cases. This can manifest as biased language, missing perspectives, and uneven accuracy, producing "unbalanced" results. That is why Responsible AI practice emphasizes representative data, evaluation across slices, and continuous monitoring.
NEW QUESTION # 51
Your company plans to implement a proof of concept (PoC) agent that uses Azure OpenAI. The solution must start small and provide flexibility to scale usage as demand grows. Which pricing model should you use?
Answer: B
Explanation:
The correct answer is B. Pay-as-you-go. A proof of concept should start with minimum commitment, flexible usage, and the ability to scale as demand becomes clearer. Pay-as-you-go pricing is appropriate because it allows the organization to pay based on actual model consumption rather than reserving dedicated capacity upfront. This is ideal when usage is uncertain, experimental, or expected to change during testing. Provisioned Throughput Units are better for predictable, high-volume production workloads that need reserved capacity and consistent throughput. Batch API is designed for asynchronous large-scale processing, not an interactive agent PoC. Microsoft 365 Copilot is a separate productivity service and not the Azure OpenAI pricing model for a custom agent solution.
NEW QUESTION # 52
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is data security. The principle of least privilege means users, apps, and services receive only the minimum access required to perform their work. Microsoft 365 Copilot respects existing Microsoft
365 permissions and does not grant users new access to content they are not already authorized to view. That behavior is a security control because it helps prevent unauthorized access to company information. Data classification is about labeling or categorizing information by sensitivity. Data compliance focuses on meeting policy, legal, or regulatory requirements. Data loss prevention helps prevent sensitive information from being shared inappropriately. Least privilege is primarily an access-control practice, so the best category is data security.
NEW QUESTION # 53
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:
Explanation:
Box 1: No
No - A generative AI solution is well-suited to predict next-quarter sales trends.
Predictive AI, not generative AI, is designed to forecast future trends and outcomes.
While Generative AI (GenAI) and Predictive AI both use historical data and machine learning, their primary purposes are distinct: Generative AI is designed to create new, original content (text, images, code), whereas Predictive AI is designed to forecast future trends and outcomes.
Box 2: Yes
Yes - A generative AI solution is well-suited to summarize lengthy policy documents.
Generative AI solutions are exceptionally well-suited to summarize lengthy policy documents, offering a transformative approach to document management that enhances efficiency, reduces manual labor, and improves accuracy. These systems can analyze hundreds of pages in minutes, extracting key obligations, deadlines, and critical information while removing redundancy.
Box 3: Yes
Yes - A generative AI solution can create product descriptions from product specifications.
Generative AI solutions significantly streamline e-commerce by transforming raw product specifications-such as materials, dimensions, and technical features-into engaging, human- readable product descriptions. By leveraging Large Language Models (LLMs) and Natural Language Processing (NLP), these tools can automate content creation for thousands of items in minutes, ensuring consistency in brand voice and improving search engine optimization (SEO) Reference:
https://nfina.com/generative-ai-vs-predictive-ai/
https://artificio.ai/blog/generative-ai-for-document-summarization-and-insights
https://describely.ai/blog/ai-generated-product-descriptions-for-ecommerce
NEW QUESTION # 54
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