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NEW QUESTION # 66
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: B
Explanation:
Even when training data is already consistent and uniform, the first step in building a custom Azure Machine Learning model is still to prepare the training data. "Consistent" data reduces the amount of cleaning you may need, but preparation is broader than cleaning: you still must confirm the schema, validate data types, handle missing values (if any), ensure label quality (for supervised learning), select/engineer features, and split data into training/validation/test sets. Those actions determine whether training will be stable and whether evaluation metrics will be meaningful.
If you skip preparation and go directly to training (C), the model might learn from the wrong columns, inconsistent labels, or poorly partitioned data, producing misleading results. Evaluation (B) comes after training because you need a trained model to score and measure. Hyperparameter tuning (D) is an optimization activity that presupposes you already have a working training pipeline and a baseline model to improve. Deployment (E) is last, after you have validated performance and selected the model candidate.
Azure Machine Learning commonly operationalizes these steps through pipelines, where data preparation is a foundational stage that precedes training and evaluation (and can also be iterated as you refine features and quality).
NEW QUESTION # 67
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: uses structured data and provides insights by using text, charts, tables, and other visuals.
The Analyst agent in Microsoft 365 Copilot _______________.
The Analyst agent in Microsoft 365 Copilot is a specialized, AI-powered "virtual data scientist" designed to transform raw, structured data into actionable insights, utilizing text, tables, charts, and graphs to present findings.
It is designed to work with structured data sources, including Excel files, CSVs, tables, and databases, to help users analyze data without needing advanced data science expertise.
Reference:
https://blog.storyals.com/sv/meet-your-new-ai-teammates-researcher-analyst
NEW QUESTION # 68
- 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 # 69
Your company purchases Microsoft 365 Copilot for its sales department. The sales department needs to find and summarize information across internal documents quickly. From which two data sources can the sales department obtain results by default? (Select TWO.)
Answer: C,E
Explanation:
By default, Microsoft 365 Copilot is grounded in your organization's Microsoft 365 data through Microsoft Graph , and it respects the user's existing permissions. For "find and summarize information across internal documents," the most direct default document repositories in Microsoft 365 are SharePoint (team sites and shared libraries) and OneDrive (a user's work files). That is why C (Microsoft SharePoint) and D (Microsoft OneDrive) are the correct selections. Microsoft explicitly describes Copilot as accessing organizational content via Microsoft Graph, including user documents and related work content.
The other options are not "by default" sources. A (on-premises file share) is not automatically part of Microsoft Graph unless you integrate/migrate content or use connectors to make it discoverable in Microsoft
365 experiences. B (custom CRM) similarly requires an integration approach (for example, Microsoft 365 Copilot connectors / Graph connectors) to index and expose that data for Copilot to use. E (Microsoft Sway) is not a primary default content source for Copilot's document grounding and is not typically referenced as a core internal document repository compared to SharePoint/OneDrive.
So, for immediate, out-of-the-box internal document discovery and summarization within Microsoft 365, SharePoint + OneDrive are the default answer.
NEW QUESTION # 70
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: A
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 # 71
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