CRM Analytics and Einstein Discovery Consultant サンプル問題プレビュー（抜粋）
収録内容から抜粋したサンプル問題です。「解答・解説を表示」をクリックすると正解を確認できます。
サンプル問題 1： CRM Analytics team plans to enable data sync. Which limit specific to data syne should the team consider before enabling the feature because it may impact existing jobs?
A: Maximum number of data sync jobs cannot exceed the limit
B: Maximum number of Full Sync connection mode enabled
C: Maximum number of objects that can be enabled for data sync
正解：C
Explanation: In CRM Analytics, when planning to enable data sync, one of the critical considerations is the limit on the number of objects that can be enabled for data sync. This limit is essential because it determines how many different Salesforce objects (like Accounts, Opportunities, etc.) can be synchronized concurrently. Exceeding this limit could impact the performance of existing sync jobs or prevent new sync jobs from being configured. Key points to consider include: Performance Impact: Syncing too many objects simultaneously can lead to increased load times and potential delays in data availability, impacting users' ability to access up-to-date information. Resource Allocation: CRM Analytics allocates resources based on the number of objects being synchronized, and there are practical limits to these resources to ensure stable and efficient operation. For a more detailed understanding and to manage these limits effectively, Salesforce provides documentation and guidelines within the CRM Analytics resources, which can be further explored in the Trailhead modules specifically focusing on data management and synchronization practices.
サンプル問題 2： A dashboard designer at Cloud Kicks creates a dashboard in CRM Analytics. The designer notices fields display on the dashboard with their API labels, such as "AccountId.Industry", and wants to change this behavior. The designer also notices that the fields and their order appear to randomly change when a values table is created. What should the CRM Analytics consultant explain to help the designer?
A: The default fields in a values table can be changed by reordering how fields appear in the JSON of the value table.
B: The default fields In a values table and the field labels can be modified in the dataset explorer.
C: The field labels can only be changed in the widget properties in the dashboard edit mode.
正解：B
Explanation: For the scenario at Cloud Kicks where fields display with their API labels and the fields in a values table seem to change order randomly, the correct approach is to modify these settings in the dataset explorer within CRM Analytics. This allows for a more intuitive display and control over how data is presented in dashboards. Here’s how these adjustments help: Modifying Field Labels: Changing the field labels from their API names to more user-friendly names enhances readability and user experience. This can be done directly in the dataset explorer, which affects how fields appear across all dashboards utilizing that dataset. Controlling Field Order: The order of fields in a values table can seem random if not explicitly set. By using the dataset explorer, a designer can specify the order in which fields appear, which then reflects consistently in the dashboard’s values table. This functionality is part of CRM Analytics' aim to provide flexible and customizable data visualization tools. Training on these features is available through various Salesforce Trailhead modules that discuss dashboard and dataset customization techniques, providing practical insights and guided tutorials to enhance dashboard design and user interaction. Both these explanations are consistent with best practices as outlined in Salesforce’s CRM Analytics documentation and the Trailhead educational content, ensuring that users are well-equipped to leverage the full capabilities of CRM Analytics for effective data management and presentation.
サンプル問題 3： 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?
A: As part of the recipe, use a formula on the Roleid fild to create an attribute called 'ParentRolelDs' on the dataset, and apply the following security predicate: 'ParentRolelDs' == ‘’$UserRoleId’’ || Owned\ == ‘$User,id\\,
B: As part of the recipe, use the flatten operation on the role hierarchy, create a multi-value attribute called 'ParentRoleIDs' on the dataset, and apply the following security predicate: 'ParentRoleIDs' == "$User.UserRoleld" || 'TeamMember.Id’ '$User, Id" || 'Ownerld’ == "$User.Id".
C: As part of the recipe, use a multi row formula on the Roleld field to create an attribute called 'ParentRoleIDs' on the dataset, and apply the following security predicate: “$User.UserRoleld” || ‘Ownerld’ == “$User.Id".
正解：B
Explanation: In addressing the requirements of Universal Containers to ensure proper visibility of order data across different levels of the sales hierarchy, the use of a security predicate based on role hierarchies is paramount. Here’s why Option B is the ideal approach: Flatten Operation on Role Hierarchy: This operation is essential as it allows for the creation of a simplified or "flattened" view of the hierarchical relationships within the organization. This flattened view enables the dataset to understand and respect the hierarchical structure in security implementations. Creating a Multi-value Attribute ('ParentRoleIDs'): By creating this attribute, the recipe can hold multiple role IDs that a particular user has visibility permissions for. This is crucial in a hierarchical organization like UC where data visibility needs to cascade down the hierarchy. Security Predicate: The predicate ('ParentRoleIDs' == "$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.
Salesforce CRM Analytics and Einstein Discovery Consultant試験の準備に向けた復習資料としてCRM Analytics and Einstein Discovery Consultant問題集を提供しています。Salesforce Certified CRM Analytics and Einstein Discovery Consultant Exam試験の主要な知識ポイントをカバーしています。ページ上部の「CRM Analytics and Einstein Discovery Consultant 試験を開始する」ボタンからオンライン無料サンプル問題を確認できます。
CRM Analytics and Einstein Discovery Consultant問題集の収録内容と更新状況はどうなっていますか？
答え：現在提供されているCRM Analytics and Einstein Discovery Consultant問題集には、CRM Analytics and Einstein Discovery Consultant試験対策に向けた全115問の練習問題と解答が収録されています。Salesforce Certified CRM Analytics and Einstein Discovery Consultant Exam試験に向けた復習に利用できます。
どうやってCRM Analytics and Einstein Discovery Consultant問題集を使って復習しますか？
答え：時間の許す限り、内容を理解しながら計画的に復習することをお勧めします。CRM Analytics and Einstein Discovery Consultant問題集の多くの設問と知識ポイントを一度の学習ですべて定着させるのは難しいため、3回に分けて反復する方法が効果的です。1回目は全体を通して解説とあわせて理解し、2回目は分野ごとに自分の言葉で説明できるか確認し、3回目は試験直前に間違えやすい設問を中心に総復習します。
学習時間が限られている場合は、CRM Analytics and Einstein Discovery Consultant問題集の選択問題を中心に重要キーワードと正答の根拠を優先して整理してください。また、ドラッグ＆ドロップ問題やシミュレーション形式の設問については、手順や前提条件を含めて仕組みを理解しておくことが重要です。
