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| Section | Weight | Objectives |
|---|---|---|
| Identify the business value of generative AI solutions | 35โ40% | - AI value identification
|
| Identify an implementation and adoption strategy for Microsoft AI apps and services | 20โ25% | - AI adoption strategy
|
| Identify benefits, capabilities, and opportunities for Microsoft AI apps and services | 35โ40% | - Microsoft AI ecosystem
|
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NEW QUESTION # 70
Your company wants to ensure that AI solutions are used responsibly and align with company values and compliance requirements.
You need to establish governance principles for AI use.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
To establish robust governance for AI, a company must define accountability norms that bridge the gap between business objectives and technical execution. Effective frameworks ensure that specific individuals-not just committees-are responsible for AI outcomes across the entire lifecycle.
Core Accountability Strategies
Establish Clear Ownership: Assign a specific person or team as the "accountable owner" for each AI application. This includes defining decision rights for who can approve, modify, or retire a system.
To establish an effective AI governance framework, you should implement a structured review process that translates high-level ethical principles into operational practice. This process serves as a "brake pedal" for the organization, ensuring innovation remains safe and aligned with corporate values.
Reference:
https://www.databricks.com/blog/ai-governance-best-practices-how-build-responsible-and- effective-ai-programs
NEW QUESTION # 71
Which business requirement most closely relates to grounding a generative AI model?
Answer: C
NEW QUESTION # 72
You need to recommend an AI solution for each task. The solutions must use prebuilt AI capabilities to reduce development time. What should you recommend for each task?
Answer:
Explanation:
Explanation:
Extract information from invoices and credit notes: Azure Document Intelligence in Foundry Tools Identify discussion topics in customer service call recordings: Azure AI Search Detect customer sentiment and opinions: Azure AI Search To minimize development time, you should select services that provide prebuilt extraction and enrichment rather than building custom models.
For invoices and credit notes , the best fit is Azure Document Intelligence because it is purpose-built for intelligent document processing and includes prebuilt models (such as invoice-style extraction) that return structured fields (invoice number, vendor, totals, dates, line items). This directly satisfies "extract information" from financial documents without custom ML training.
For customer service call recordings , the raw input is audio, but the task is to identify "discussion topics," which is an information discovery and text-enrichment problem once the calls are transcribed. Azure AI Search is the correct recommendation because it can index large collections of content and apply built-in AI enrichment (for example, key phrase extraction and entity extraction) to help derive and query "topics" across many records.
For sentiment and opinions , Azure AI Search is again the best choice in this constrained list because it can run built-in enrichment skills over ingested text (such as transcripts or notes) to annotate content with sentiment-related metadata and then make that enriched data searchable and filterable at scale. This approach delivers fast time-to-value: ingest # enrich # index # query, without standing up a custom training pipeline.
NEW QUESTION # 73
- Select the answer that correctly completes the sentence.
A __________ AI solution recognizes patterns in large and complex datasets to create new and original content.
Answer:
Explanation:
Explanation:
generative
The sentence describes an AI system that creates new, original content (for example, text, images, audio, code) by learning patterns from large datasets. That is the defining characteristic of generative AI , so
"generative" is the correct completion.
To clarify the contrasts: predictive AI primarily uses historical data to forecast outcomes or estimate probabilities (for example, predicting next-quarter sales, predicting equipment failure, or classifying whether a transaction is fraudulent). It focuses on what is likely to happen or which category something belongs to , rather than producing novel content. Prescriptive AI goes a step further by recommending actions or decisions (for example, "increase inventory by X," "route tickets to team Y," "schedule maintenance next week") often using optimization and business constraints.
Generative AI, by comparison, is focused on content synthesis . It identifies statistical patterns and relationships in training data and then generates new sequences that resemble the learned distribution while being responsive to prompts. In business use cases, that translates to drafting emails and documents, summarizing reports, generating meeting notes, creating product descriptions, producing marketing copy, and building chat-based assistants. While all three categories can "recognize patterns," only generative AI is explicitly characterized by the ability to produce new and original content , which is what the question is testing.
NEW QUESTION # 74
HOTSPOT - 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
* Larger datasets can increase the cost of a generative AI solution that uses an Azure Machine Learning workspace. Answer: Yes
* The cost of consuming Azure OpenAI models is primarily identified by the number of input and output tokens processed. Answer: Yes
* The cost of custom generative AI solutions always remains the same regardless of the model version or capability used. Answer: No
* Yes - In Azure Machine Learning, cost is driven by the resources you consume to store, process, and train. Larger datasets typically require more storage, more data transfer, and more compute time for preprocessing, training, evaluation, and experimentation. Even if you are not training foundation models, handling larger corpora can increase pipeline duration and the number/size of compute instances used, which increases overall cost.
* Yes - For Azure OpenAI usage under Standard (on-demand), pricing is primarily tied to token-based consumption (input tokens + output tokens). The more context you send and the longer the generated responses, the more tokens you consume, and the higher the cost. This is why prompt optimization, response length controls, and grounding strategies matter for cost management.
* No - Costs vary with model choice and capability. Different model families and versions have different price points, and larger/more capable models generally cost more per token or per unit of throughput. Additionally, architecture choices (RAG, vector search, caching), usage patterns, and throughput requirements can significantly change total cost-so it is never "always the same."
NEW QUESTION # 75
......
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