P.S. TopexamがGoogle Driveで共有している無料かつ新しいAnalytics-Con-201ダンプ：https://drive.google.com/open?id=1reg2Dg7nfU_-qDiQI6ELQZlxyYXK8oYU
Analytics-Con-201情報通信技術の進歩は、ビジネスと生産をバリューチェーンに引き上げ、市民の生活の質を向上させる大きな可能性を生み出します。 そして、Salesforceサイバースペースであらゆる種類の情報を今すぐ入手できることは間違いありません。Analytics-Con-201最新の急流も例外ではありません。 私たちの会社がまとめたAnalytics-Con-201学習教材を強くお勧めします。Analytics-Con-201試験問題の利点は多すぎて列挙できません。 また、Analytics-Con-201試験問題をお試しになりたい場合は、ぜひSalesforce Certified CRM Analytics and Einstein Discovery Consultant購入してください。
| Section | Objectives |
|---|---|
| Topic 1: Security and Access | - Row-level security and sharing rules - App and dataset permissions |
| Topic 2: Dashboard and App Development | - Lens creation and dashboard design - Interactive filters and user experience optimization |
| Topic 3: Einstein Discovery | - Model building and predictions - Insights interpretation and recommendations |
| Topic 4: Data Integration and Connectivity | - Connecting external data sources - Data synchronization and refresh strategies |
| Topic 5: Data Management and Preparation | - Dataset creation and configuration in CRM Analytics - Data transformation and dataflows |
簡単にSalesforceのAnalytics-Con-201認定試験に合格したいか。TopexamのSalesforceのAnalytics-Con-201試験トレーニング資料は欠くことができない学習教材です。TopexamのSalesforceのAnalytics-Con-201試験トレーニング資料は豊富な経験を持っているIT専門家が研究したもので、問題と解答が緊密に結んでいるものです。他のネットでの資料はそれと比べるすらもできません。Topexamは君のもっと輝い将来に助けられます。
質問 # 57
A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "* & **( & *(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?
正解：A
解説：
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules.
Headers containing non-alphanumeric characters, such as " & **( & (%", will automatically be adjusted.
Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with "X" to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.
Reference: You can find the CSV upload rules, including naming conventions and auto-formatting behaviors, outlined in theSalesforce CRM Analyticsdocumentation provided during training, particularly under the module "Wave Analytics Explorer."
質問 # 58
Universal Containers (UC) is rolling out CRM Analytics to its field sales that include dashboards withorder data from an external source.
UC has a well-defined role hierarchy where everyone is assigned to an appropriate node on the hierarchy. In addition, the order data has a reference to a Salesforce opportunity.
An individual sales rep should be able to view all orders that they own or as part of the account team or opportunity team. The sales manager should be able to view all orders for the entire sales team. Similarly, the VP of sales should be able to view orders for everyone who rolls up in that hierarchy.
The dataset has a field called Ownerld which represents the order owner.
Given this information, how should a CRM Analytics consultant implement the above requirements?
正解：B
解説：
In addressing the requirements of Universal Containers to ensure proper visibility of order data across different levels of the sales hierarchy, the use of a security predicate based on role hierarchies is paramount.
Here's why Option B is the ideal approach:
* Flatten Operation on Role Hierarchy: This operation is essential as it allows for the creation of a simplified or "flattened" view of the hierarchical relationships within the organization. This flattened view enables the dataset to understand and respect the hierarchical structure in security implementations.
* Creating a Multi-value Attribute ('ParentRoleIDs'): By creating this attribute, the recipe can hold multiple role IDs that a particular user has visibility permissions for. This is crucial in a hierarchical organization like UC where data visibility needs to cascade down the hierarchy.
* Security Predicate: The predicate ('ParentRoleIDs' == "$User.UserRoleld" || 'TeamMember.Id' ==
'$User.Id' || 'Ownerld' == "$User.Id") effectively enforces that:
* A user can see all orders where their role matches any of the role IDs in the 'ParentRoleIDs' list (hierarchical visibility).
* A user can see all orders where they are specifically listed as a team member.
* A user can see all orders where they are the owner.
This approach aligns with best practices for implementing row-level security in CRM Analytics, ensuring data visibility is managed correctly according to the defined organizational hierarchy and individual data ownership.
質問 # 59
Universal Containers (UC) creates a dataset, "Book11", containing a budget per region per month for the first
6 months of the year, as shown in the graphic below. Now, UC wants to create a lens showing the total budget for each region for each month. Every combination of region and month must be shown in the lens, even if there is no data.
How should a CRM Analytics consultant help UC build this lens?
正解：C
質問 # 60
A consultant creates a CRM Analytics dashboard in a sandbox and it needs to be migrated into production.
What should the consultant use to complete the migration?
正解：A
質問 # 61
A team of CRM Analytics developers has been working on an existing recipe to add new derived fields. The edited version has been failing ever since, and management is requesting that the dashboard show refreshed data while they work on the edits.
How can the developers add new fields while keeping the dataset refreshed?
正解：C
解説：
When faced with the need to continue refreshing data while developing new features in a recipe, the best practice is:
* Clone the Existing Recipe: By cloning the recipe, developers can experiment with adding new fields and transformations without affecting the production data flow. This allows for testing and development in a sandbox-like environment.
* Roll Back to a Stable Version: Rolling back the original recipe to the last stable version ensures that the production dashboards continue to receive refreshed data, maintaining business operations without disruption.
This approach not only ensures data continuity but also provides a safe environment to address any issues that may arise from new developments.
質問 # 62
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