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| Section | Weight | Objectives |
|---|---|---|
| Analytics Dashboard Implementation | 19% | - Using advanced functionality such as windowing and time series analysis in compare tables - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Monitoring and optimizing query performance using Dashboard Inspector - Configuring dashboards using accurate query types and widget-level parameters - Developing selection/result interactions with different query types - Making dashboards actionable and accessible in Lightning pages |
| Data Layer | 24% | - Dataflow configuration for multi-object scenarios - Understanding denormalized sequential datasets - Troubleshooting failed dataflow jobs - Incremental sync configuration - Data transformation and loading into Einstein datasets - Data extraction from sources |
| Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce - SAQL query language for dashboard JSON and lens queries |
| Security | 16% | - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles - App sharing based on user and group requirements |
| Einstein Discovery | 12% | - Analyzing model results and proposing data improvements - Adjusting data parameters and adding/removing data or columns to improve models - Monitoring and interpreting Model Cards to improve or maintain model performance - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics |
| 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 |
1. Tableau CRMコンサルタントは、レシピを使用して新しいデータセットを作成することにしました。
レシピに使用できる2つのソースタイプはどれですか?
2つの答えを選択してください
A) データセットレンズ
B) 既存のデータセット
C) 接続データ(同期)
D) Salesforceオブジェクトからのレコード
2. Tableau CRM コンサルタントは、というダッシュボードを開発します。
sardbox 環境の SalesPerformance_l。レシピをデプロイするために、コンサルタントはワークベンチでパッケージを作成します。 XML ファイル内の正しいメタ データ参照は何ですか?
A) SalesPerformance_1 EADashboard
B) SalesPerformance_1 waveDashboard
C) SalesPefformance_1
D) SalesPerformance_1 EdgeOashboard
3. ある企業が、Tableau CRM コンサルタントにローカル データ同期のパフォーマンスのレビューを依頼しました。
未使用のオブジェクトとフィールドを接続から削除した後
データ、データ同期のパフォーマンスを向上させるためにコンサルタントが他にできることは何ですか?」*
A) 同期されたオブジェクトを結合します
B) 分析設定で高速同期を有効にする
C) 同期速度を上げるには、Salesforce サポートにお問い合わせください。
D) 接続されたオブジェクトごとに接続モードを評価します
4. バインディングの2つのタイプは何ですか?2を選択します。
A) 説明バインディング
B) セレクションバインディング
C) 結果のバインド
D) データバインディング
5. Einstein Discoveryを使用してCSVファイルのデータをどのように分析しますか?
A) CSVファイルをEinsteinDiscoveryにインポートします。
B) CSVファイルからデータをインポートすることはできません
C) CSVファイルを最も類似したSalesforceオブジェクトにインポートしてから、そのオブジェクトでレポートを実行します。
D) CSVファイルをEinstein Analyticsデータセットにインポートしてから、ストーリーを作成します。
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: B,C | Question # 5 Answer: D |
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