Analytics-Con-201模擬問題、Analytics-Con-201実際試験

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現在、どの領域にでも勉強して努力する必要があります。IT業界でも同じです。Salesforceに関する仕事をしている人たちはさまざまな認証試験に参加して自分の知識を補充し、よく働く必要があります。Analytics-Con-201試験に合格するのはあなたの能力を証明して、質素を高めることができます。

Salesforce Analytics-Con-201 Exam Syllabus Topics:

SectionWeightObjectives
Data Layer25%- Implement and configure the right data transformations
  • 1. Use Data Prep actions
  • 2. Identify and resolve data sync issues
- Identify the right data sources and data flows for CRM Analytics
  • 1. Define datasets
  • 2. Describe how Salesforce objects map to CRM Analytics
  • 3. Describe use cases for Data Prep (Dataflows and Recipes)
Analytics Dashboard Design20%- Design and build CRM Analytics dashboards
  • 1. Use Lenses and Dashboards
  • 2. Apply design best practices
  • 3. Describe the use cases for Visualforce and Lightning components
Security10%- Determine security and sharing settings
  • 1. Describe how Salesforce security is applied in CRM Analytics
  • 2. Implement row-level security
Analytics Dashboard Configuration25%- Configure dashboards
  • 1. Apply query filters
  • 2. Configure bindings
  • 3. Set up dynamic dashboards
Einstein Discovery20%- Describe Einstein Discovery concepts
  • 1. Identify the use cases for Einstein Discovery
  • 2. Interpret model results
  • 3. Explain the model building process
- Implement Einstein Discovery
  • 1. Configure and deploy Einstein Discovery models
  • 2. Integrate predictions into CRM Analytics dashboards

>> Analytics-Con-201模擬問題 <<

Analytics-Con-201実際試験 & Analytics-Con-201試験解説問題

「私はだめです。」という話を永遠に言わないでください。これは皆さんのためのアドバイスです。難しいSalesforceのAnalytics-Con-201認定試験に合格する能力を持たないと思っても、あなたは効率的な骨の折れないトレーニングツールを選んで試験に合格させることができます。Fast2testのSalesforceのAnalytics-Con-201試験トレーニング資料はとても良いトレーニングツールで、１００パーセントの合格率を保証します。それに、資料の値段は手頃です。Fast2testを利用したらあなたはきっと大いに利益を得ることができます。ですから、「私はだめです。」という話を言わないでください。諦めないのなら、希望が現れています。あなたの希望はFast2testのSalesforceのAnalytics-Con-201試験トレーニング資料にありますから、速く掴みましょう。

Salesforce Certified CRM Analytics and Einstein Discovery Consultant 認定 Analytics-Con-201 試験問題 (Q97-Q102):

質問 # 97
Universal Containers' CRM Analytics team is building a dashboard with two widgets.
1. List widget associated to the query "Type_2" and grouped by the dimension "Type" (multi-selection)
2. Donut chart widget associated to the query "Step_pie_3" and grouped by the dimension "Type" The team wants to use bindings/interactions so any selection in the List widget will filter the Donut chart. The queries use different datasets, and users should be able to choose more than one Type (multi-selection).
What is the right syntax for the binding/interaction?

正解：C

解説：
For the given requirement where a selection in a list widget needs to filter data displayed on a donut chart, and considering the list allows for multi-selection of the 'Type', the correct binding/interaction would be to use a syntax that captures the multi-select aspect and passes it appropriately. The right syntax, as indicated in Option A, looks like this:
"{{column(Type_2.selection, [\"Type\"]).asObject()}}"
This syntax ensures:
* Multi-selection: The column() function in combination with .asObject() ensures that multiple selected values from the 'Type_2' query can be passed as an object, which the donut chart can utilize to filter its content.
* Correct Data Type Handling: By using .asObject(), the binding ensures the data passed between widgets maintains the correct structure expected by the CRM Analytics dashboard, thereby ensuring accurate filtering.


質問 # 98
The below image shows a numeric outcome being deployed (Regression).

Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?

正解：B

解説：
In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:
* Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.
* Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors- essentially, the average squared difference between the estimated values and what is estimated.
These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model's predictions in real-world applications.


質問 # 99
A consultant runs the sharing inheritance coverage assessment for the Opportunity object and finds that some records exceed 400 sharing descriptors.
What should the consultant do?

正解：C

解説：
When a record exceeds 400 sharing descriptors, it can cause performance issues or sharing rule complications in CRM Analytics. In such cases, the recommended solution is to usesecurity predicates, which allow fine- tuned control over which data is visible to users based on their sharing rules and permissions. Security predicates reduce the number of sharing descriptors by enforcing security at the dataset level rather than relying solely on record-sharing mechanisms.
Increasing the sharing descriptor limit is not an available option, and Salesforce Support does not typically increase this limit, making the use of security predicates the best approach.
Reference:Security predicatesand their use to optimize data visibility and sharing within CRM Analytics are explained in theCRM Analyticsdocumentation related to security and performance.


質問 # 100
Exhibit.

Given that the queries are using different datasets, which change should a CRM Analytics consultant make to solve this issue?

正解：C


質問 # 101
A CRM Analytics consultant has been asked to add a custom object to existing recipe. When trying to locate the object, the consultant can see only Direct Data and NOT the SFDC Local data sync.
How should the consultant resolve this?

正解：B


質問 # 102
......

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