Microsoft AB-210 Exam With Confidence Using Practice Dumps

The issue is that the sales manager already generated a Sales Research Agent blueprint but forgot to include the quarterly competitive intelligence brief. The brief is an external document and the case study states it is delivered as a PDF document , so the correct first content action is to upload the competitive intelligence brief as a PDF file , not CSV. Microsoft’s Sales Research Agent documentation states that users can select Add data to upload files including CSV, Excel, or PDF to augment research with additional data.

The second required action is to add a data source , because the competitive intelligence brief is not part of the default Dynamics 365 Sales data. Sales Research Agent connects to Dynamics 365 Sales data by default, but additional external files must be added as supporting data sources before they can influence the analysis. The documentation identifies the Data area as the place where the research data source is specified and where additional data sources can be added.

The final action is to regenerate the canvas/blueprint so the agent reprocesses the analysis using the newly added PDF. Refreshing alone is not the best answer because the problem is not stale data; it is missing context. Regeneration creates updated research output with the new source included.

References/topics: Sales Research Agent; research canvas; adding PDF data sources; blueprint regeneration; intelligent opportunity research.

Requirement

Configuration or action

Make a product available for selling

Publish product

Organize related products so they inherit shared attributes

Product family

Create a combined offering of several products

Bundle

The correct action to make a product available for selling is Publish product . In Dynamics 365 Sales, products, families, and bundles are typically created or revised before being published. Microsoft states that publishing makes products available for selling, and sellers can choose only published products and bundles when creating sales records such as opportunities and quotes.

To organize related products so they inherit shared attributes, use a Product family . Product families provide the hierarchy used to classify and manage similar products. Microsoft’s product catalog documentation explains that a product family can contain child product families, products, and bundles in a hierarchy, and the parent family is used to structure related catalog items.

To sell coffee machines, filters, and grinders together, configure a Bundle . A bundle is the correct component when several products are sold together as one combined offering. Microsoft’s product catalog guidance distinguishes bundles from product families: families organize related catalog items, while bundles package multiple sellable products together. Currency is not used here because it supports pricing localization and monetary values, not catalog structure, publication, or combined product offerings.

References/topics: Product catalog lifecycle; publish product; product families; product bundles; Dynamics 365 Sales product catalog configuration.

Requirement

Configuration

Automatically prioritize leads based on conversion probability

Predictive lead scoring

Ensure the lead scoring improvement requirement is met

Automatically retrain model

The correct capability for automatically prioritizing leads based on conversion probability is Predictive lead scoring . Dynamics 365 Sales predictive lead scoring assigns scores to leads so sellers can prioritize the leads most likely to qualify or convert. Microsoft’s Sales documentation states that predictive lead scoring helps sales teams prioritize leads based on scores and improve lead qualification outcomes. This directly matches Contoso’s requirement to guide sellers toward high-potential leads and reduce inconsistent manual prioritization across regions.

To meet the requirement that lead scoring must continuously improve based on historical sales outcomes, configure the model to automatically retrain . Microsoft’s predictive lead scoring guidance explains that retraining uses the latest leads in the organization to improve model accuracy, and automatic retraining allows the application to retrain the model every 15 days. This is the correct option because Contoso wants the scoring model to improve continuously as more closed lead outcome data becomes available.

“Lead scoring model” is too generic; the AI capability required is specifically predictive lead scoring. “Relationship intelligence” supports relationship analytics and engagement insights, but it does not score leads by conversion probability. “Configure forecast” applies to pipeline forecasting, not lead scoring accuracy.

References/topics: Predictive lead scoring; lead prioritization; scoring model accuracy; automatic model retraining; AI-driven lead qualification.