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NEW QUESTION # 62
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 # 63
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is establish a champions program for your company. A champions program helps scale AI adoption because it creates a network of motivated employees who learn AI capabilities, share practical examples, support peers, and identify meaningful use cases within their departments. This is more effective than relying only on awareness campaigns or self-paced courses because champions create continuous peer-led adoption and practical reinforcement. Performance metrics are useful for measuring adoption, but metrics alone do not build confidence or capability. A champions program supports change management by turning early adopters into local advocates who can demonstrate value in real workflows, reduce resistance, gather feedback, and help employees understand how AI tools apply to their daily work.
NEW QUESTION # 64
- Select the answer that correctly completes the sentence.
The Analyst agent in Microsoft 365 Copilot __________.
Answer:
Explanation:
Explanation:
uses structured data and provides insights by using text, charts, tables, and other visuals.
The Analyst agent in Microsoft 365 Copilot is positioned as a "data analysis" reasoning agent that helps users work through structured information (for example, tables, spreadsheets, and other dataset-like inputs) and then produces analytical outputs . The best completion is the option stating it "uses structured data and provides insights by using text, charts, tables, and other visuals," because that describes the hallmark outcome of analyst-style work: summarizing patterns, highlighting key drivers, and presenting results in formats that business users can act on. Analyst-style assistance typically includes exploring the data, identifying trends and anomalies, comparing segments, and explaining findings clearly-often accompanied by tables and visual representations that make the insights easier to consume.
The other dropdown options align to different use cases: "compiles background research for a new market or initiative" describes a research-oriented agent, "generate audio summaries" is a media summarization function, and "answer employee FAQs" describes a conversational knowledge assistant. Analyst is the one most directly associated with structured-data interpretation and producing a mix of narrative plus analytical artifacts (tables/charts) to communicate conclusions.
NEW QUESTION # 65
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: Yes
Yes - Larger datasets can increase the cost of generative AI solution that uses an Azure Machine Learning workspace.
larger datasets can increase the cost of a generative AI solution using an Azure Machine Learning (AML) workspace. While the AML workspace itself has no additional charge, larger datasets increase expenses across several underlying Azure services, particularly through higher storage, compute, and data transfer requirements.
Box 2: Yes
Yes - The cost of consuming Azure OpenAI models is primarily identified by the number of input and output tokens processed.
The cost of consuming Azure OpenAI models is primarily determined by the number of input and output tokens processed.
This consumption-based, pay-as-you-go model calculates costs based on the total volume of text (or image/audio data) sent to the model (input) and the text generated by the model (output), usually billed per 1,000 or 1 million tokens, depending on the model and pricing page updates.
Box 3: No
No - The cost of custom generative AI solutions always remains the same regardless of the model version or capability used he cost of custom generative AI solutions varies significantly based on the model's version, complexity, and capability. While a basic chatbot might range from $40,000 to $150,000, advanced enterprise-grade platforms with high-risk reasoning can exceed $400,000 to
$500,000+.
Reference:
https://www.doit.com/blog/optimizing-ml-costs-with-azure-machine-learning
https://www.finout.io/blog/azure-openai-pricing
https://medium.com/@dejanmarkovic_53716/custom-ai-solutions-cost-guide-2025-pricing- insights-revealed-cf19442261ec
NEW QUESTION # 66
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: Yes
Yes - A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
A manufacturer can use Azure Vision in Foundry Tools (formerly part of Azure AI Services, now integrated within the AI Foundry toolkit) to identify product defects on an assembly line. This solution automates visual inspections to detect anomalies such as surface scratches, cracks, misalignments, or missing components in real time.
Box 2: Yes
Yes - A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
A logistics company can use Azure Vision in Foundry Tools (part of the broader Azure AI services suite) to recognize, interpret, and digitize package shipping labels. By integrating Azure's advanced AI with Palantir Foundry, firms can automate manual data entry, track shipments, and improve operational efficiency.
Box 3: No
No - The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files.
Azure Vision in Foundry Tools is primarily designed for images, while its sibling tool, Document Intelligence, handles Microsoft Word files.
Reference:
https://datalabs.io/azure-ai-for-smart-manufacturing-defect-detection-with-computer-vision
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-ocr
NEW QUESTION # 67
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