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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Discovery | 12% | - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Monitoring and interpreting Model Cards to improve or maintain model performance - Adjusting data parameters and adding/removing data or columns to improve models - Analyzing model results and proposing data improvements |
| Topic 2: Analytics Dashboard Design | 13% | - Scoping, validating, and prioritizing dashboard design requirements - Creating dashboards following CRM Analytics best practices and UX design principles - Identifying appropriate use and configuration of standard CRM Analytics templated apps |
| Topic 3: Administration | 16% | - SAQL query language for dashboard JSON and lens queries - Embedding analytics in Salesforce - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations |
| Topic 4: Data Layer | 24% | - Understanding denormalized sequential datasets - Incremental sync configuration - Dataflow configuration for multi-object scenarios - Data extraction from sources - Troubleshooting failed dataflow jobs - Data transformation and loading into Einstein datasets |
| Topic 5: Analytics Dashboard Implementation | 19% | - Implementing delivery management strategies using versioning and/or Dashboard Publisher - 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 - Configuring dashboards using accurate query types and widget-level parameters |
| Topic 6: Security | 16% | - Dataset security settings using sharing inheritance and security predicates - App sharing based on user and group requirements - Implementing security settings for users, groups, and profiles |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
図を参照してください。
示されているモデルの強度に関して、どの結論を下すことができますか？
- A. モデルは非常に弱く、しきい値が低いため、有用な予測を提供しません。
- B. モデルは非常に強力です。.535のGINI係数は、このモデルが非常に効果的であることを示しています。
- C. モデルの強度は、表示されているメトリックでは判断できません。
- D. モデルはかなり良いです。.7597の精度評価は、かなり高いレートで勝ち負けの両方を予測できることを意味します。
組織では、一部のアクティビティレコードに関連するオポチュニティレコードがありません。さらに、関連するアクティビティレコードがないオポチュニティレコードがあります。
関連するアクティビティがない場合でも、すべてのオポチュニティをフェッチし、関連するアクティビティデータが存在する場合は、どのSAQLステートメントがフェッチしますか？
- A. q = cogroup Opportunity by'Id' right、Activities by'Opportunityld';
- B. q = cogroup Opportunity by'Id' left、Activities by'Opportunityld';
- C. q = cogroup Opportunity by'Id'、Activities by'Opportunityld';
- D. q ='Id'によるコグループOpportunity、'Opportunityld'によるアクティビティ左;
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
データセットを作成するための2つの主な手順は何ですか？
- A. 抽出して準備する
- B. 構築して探索する
- C. 計画と地図
- D. 実行および監視
UniversalContainersにはSalesManagerダッシュボードがあり、顧客数と年間およびアカウントタイプごとの年間収益の合計を表示する比較テーブルがあります。コンサルタントは、顧客に関する詳細を強化するために、さらにグループを追加するように求められます。
SAQLを使用してクエリを作成するのではなく、比較テーブルを引き続き使用するためにコンサルタントが認識しなければならないグループ化列の最大数はいくつですか。
- A. 無制限
- B. 10
- C. 4
- D. 8
Universal Containersには、セールスマネージャー用のダッシュボードがあります。彼らは、月、四半期、または年ごとにクローズドウォンの機会の数を視覚化し、その結果を単一のチャートに表示する機能を必要としています。Einsteinコンサルタントは、ClosedDate_month、ClosedDate_quarter、およびClosedDate_yearの3つの値を表示する静的ステップを作成しました。
コンサルタントは次に何をすべきですか？
- A. ネストされたバインディングを使用して、グラフのグループ化を更新します。
- B. 選択バインディングを使用して、チャートのメジャーを更新します。
- C. 選択バインディングを使用して、チャートのグループ化を更新します。
- D. 結果バインディングを使用して、グラフのメジャーを更新します。
