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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analytics Dashboard Design | 13% | - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Scoping, validating, and prioritizing dashboard design requirements - Creating dashboards following CRM Analytics best practices and UX design principles |
| Topic 2: Data Layer | 24% | - Understanding denormalized sequential datasets - Data transformation and loading into Einstein datasets - Incremental sync configuration - Troubleshooting failed dataflow jobs - Data extraction from sources - Dataflow configuration for multi-object scenarios |
| Topic 3: Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - SAQL query language for dashboard JSON and lens queries - Embedding analytics in Salesforce - Managing CRM Analytics environment and configurations |
| Topic 4: Security | 16% | - Implementing security settings for users, groups, and profiles - App sharing based on user and group requirements - Dataset security settings using sharing inheritance and security predicates |
| Topic 5: Einstein Discovery | 12% | - Monitoring and interpreting Model Cards to improve or maintain model performance - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Adjusting data parameters and adding/removing data or columns to improve models - Analyzing model results and proposing data improvements - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) |
| Topic 6: Analytics Dashboard Implementation | 19% | - Making dashboards actionable and accessible in Lightning pages - Developing selection/result interactions with different query types - Monitoring and optimizing query performance using Dashboard Inspector - Using advanced functionality such as windowing and time series analysis in compare tables - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Configuring dashboards using accurate query types and widget-level parameters |
Event Monitoring Analyticsアプリは、組織内の既存のデータの種類に事前に構築された洞察と分析をもたらしますか?
ファセットの魔法は何ですか?
Universal Containers Einstein Analytics チームは、次の 2 つのウィジェットを備えたダッシュボードを構築しました。
1. ステップ「Type_2」に関連付けられ、ディメンション「Type」によってグループ化されたリスト ウィジェット (複数選択)
2. ステップ「Step_pie_3」に関連付けられ、ディメンション「Type」によってグループ化された円グラフ ウィジェット チームはバインディングを使用して、リスト ウィジェット内の選択によって円グラフがフィルタリングされるようにしたいと考えています。
その他の注意事項:
* ステップでは異なるデータセットを使用します。
* ユーザーは複数のタイプを選択できる必要があります (複数選択)。
バインディングの正しい構文は何ですか?




Einstein Analyticsチームは、2つの標準Salesforceオブジェクトを使用してダッシュボードを作成したいと考えています。ダッシュボードには、Caseオブジェクトのデータと、Accountオブジェクトの関連データが表示されます。これを達成できる2つのプロセスはどれですか?
コンサルタントは、ストーリーの中で重要な予測因子が何であるかを理解したいと考えています。
この情報が見つかる 2 つの場所はどこですか?
2 つの答えを選択してください
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