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NEW QUESTION # 14
Your company uses a generative AI solution. You need to improve the quality of responses by using grounding. Which statement accurately describes how grounding improves accuracy and relevancy?
Answer: D
Explanation:
Grounding is an AI solution pattern used to improve response quality by ensuring the model's output is based on trusted, relevant information provided at inference time, rather than relying only on what the model
"remembers" from training. Therefore, C is correct: grounding anchors responses in specific data sources .
In practical deployments, grounding commonly uses retrieval (often called Retrieval Augmented Generation, or RAG) where the system first finds relevant content from approved sources-such as internal policy documents, product documentation, knowledge bases, or databases-and then includes that content in the prompt context sent to the model. Microsoft's guidance describes grounding data as information supplied at inference time to help responses become more accurate and relevant because the model is guided by authoritative, up-to-date content that may not have been part of original training.
The other options do not define grounding. A relates to inclusion practices and diversity considerations, which are important for responsible AI but are not what grounding means. B describes transparency/explainability concepts. D relates to model evaluation/communication of limitations. Grounding is specifically about tying outputs to known sources, which reduces hallucinations and improves business trust in the generated responses.
NEW QUESTION # 15
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 # 16
You need to create a custom Azure Machine Learning model. The data used to train the model is consistent and uniform.
What should you do first?
Answer: D
Explanation:
The first step in creating a custom Azure Machine Learning model trained on your data is to acquire and prepare the data. This involves activities such as:
Data Collection: Gathering the relevant data from its sources, such as databases, streaming sources, or Azure Blob storage.
Data Cleaning and Preprocessing: Even with consistent and uniform data, you will need to perform steps like handling missing values, removing duplicates, and ensuring standardization.
Data Transformation and Feature Engineering: Converting the raw data into a format suitable for the chosen machine learning algorithm and creating new features that can improve model performance.
Data Splitting: Dividing the dataset into separate training, validation, and testing sets so the model can be trained on one portion and evaluated on data it hasn't seen before.
Note:
Once the data is prepared and ready, the subsequent steps in Azure Machine Learning typically involve:
1. Setting up an Azure Machine Learning workspace if you don't already have one.
2. Creating a data asset within the workspace that points to your data in Azure storage.
3. Configuring compute resources for training the model.
4, Selecting an appropriate model algorithm and writing a training script (or using automated ML features).
5. Training and tuning the model using the prepared data and compute resources Reference:
https://medium.com/@offpagework1.datatrained/building-custom-r-models-in-azure-machine- learning-is-easy-e548598c6325
NEW QUESTION # 17
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is the process of ___________________.
Prompt engineering is the process of crafting, evaluating, and improving prompts to gain more accurate outputs from an AI model. Factors that improve prompts include the LLM's preferred format, specificity of language, appropriately identifying the audience's expectations, and making function calls for external data.
At its core, prompt engineering is about reducing ambiguity so the model doesn't have to "guess" what you want. It's the bridge between a vague idea and a high-quality output.
Beyond just clarity, modern prompting often involves specific frameworks like Chain-of-Thought (asking the AI to think step-by-step) or Few-Shot Prompting (providing examples) to significantly improve reasoning and accuracy.
Reference:
https://www.linkedin.com/pulse/using-prompt-engineering-optimize-genai-models-iabac-nfa9c
NEW QUESTION # 18
- Select the answer that correctly completes the sentence.
When you use Microsoft 365 Copilot connectors to connect external content to __________, your users can find, summarize, and learn from line-of-business (LOB) data by using natural language prompts.
Answer:
Explanation:
Explanation:
Microsoft Graph
Microsoft 365 Copilot connectors (built on Microsoft Graph connectors) are used to bring external, line-of- business content into the Microsoft 365 ecosystem by ingesting it into Microsoft Graph . Once connected, the content can be indexed and made discoverable through Microsoft Search and available for Copilot experiences, enabling users to use natural language prompts to find and summarize relevant LOB information-subject to permissions and governance controls.
The other choices don't match how Copilot connectors are positioned. Azure AI Search is an Azure indexing
/retrieval service used in custom RAG solutions, but Microsoft 365 Copilot connectors are specifically designed to surface external content through Microsoft 365 experiences via Graph. Microsoft Purview focuses on data governance, compliance, and risk management rather than being the primary ingestion target for Copilot connector content. SharePoint can store content, but the connector model is about indexing external systems into Microsoft Graph so the content becomes searchable and usable across Microsoft 365, not merely placing it into SharePoint as the destination.
So the correct completion is Microsoft Graph because that is the foundational data and indexing fabric Copilot uses to reason over organizational content with appropriate permission trimming.
NEW QUESTION # 19
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