Exam Name: Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版)
Certification Provider: Salesforce
Corresponding Certification: Salesforce Consultant
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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Einstein Discovery | 12% | - Analyzing model results and proposing data improvements - 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 - Adjusting data parameters and adding/removing data or columns to improve models |
| Security | 16% | - App sharing based on user and group requirements - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles |
| Data Layer | 24% | - Understanding denormalized sequential datasets - Incremental sync configuration - Data extraction from sources - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs |
| 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 |
| Analytics Dashboard Implementation | 19% | - Configuring dashboards using accurate query types and widget-level parameters - Using advanced functionality such as windowing and time series analysis in compare tables - Making dashboards actionable and accessible in Lightning pages - Developing selection/result interactions with different query types - Monitoring and optimizing query performance using Dashboard Inspector - Implementing delivery management strategies using versioning and/or Dashboard Publisher |
| Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - SAQL query language for dashboard JSON and lens queries - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
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？
- A. ETLツールを使用してケースオブジェクトとアカウントオブジェクトからデータを抽出し、ETLツールを使用してアカウントとケースのデータをy / Q結合してから、データをEinsteinAnalyticsにアップロードします。ETLで生成されたデータセットを使用してダッシュボードを構築する
- B. 「sfdcDigest」変換を使用してケースおよびアカウントオブジェクトからデータを抽出し、Q「append」変換を使用してアカウントデータを含むケースデータを追加し、データセットとして保存し、生成されたデータセットを使用してダッシュボードを構築します
- C. 「sfdcDigest」変換を使用してケースおよびアカウントオブジェクトからデータを抽出します。y/ Qは「augment」変換を使用してアカウントデータを含むケースデータを追加し、データセットとして保存し、生成されたデータセットを使用してダッシュボードを構築します
- D. 「dataloader」を使用してケースおよびアカウントオブジェクトからデータを抽出し、アカウントおよびケースデータを含むCSVファイルQを作成してから、CSVをデータセットとしてEinsteinAnalyticsにアップロードします。そのデータセットを使用してダッシュボードを構築する
レンズの視覚化で5つのメジャーをレンダリングするのに適したチャートタイプはどれですか？
- A. 積み上げ棒グラフ
- B. メトリックレーダーチャート
- C. ツリーマップチャート
- D. 散布図
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？
- A. セールスクラウドデータログイン
- B. イベントログファイルデータ
- C. フィールド監査証跡データ
- D. フォレンジックデータ
Universal Containers社は、外部ツールからEinstein Analyticsにターゲットデータをアップロードして、営業チームのターゲット達成度を計算できるようにすることを計画しています。
ターゲットデータは毎月変更されるため、データセットは毎月更新する必要があります。ターゲットデータは、営業担当者のSalesforce ID、ターゲット金額、およびターゲットの月を含むCSVファイルです。営業担当者ごとに、ファイルには、今年のすべての月と過去のすべての年の目標が含まれています。
この情報に基づいて、コンサルタントはファイルをアップロードするためにAnalytics External Data APIでどの操作を使用する必要がありますか？
- A. アップサート
- B. 更新
- C. 追加
- D. 上書き
示す：
UniversalContainersにはSalesManagerダッシュボードがあります。図に示すようにテーブルを作成し、比較テーブルの最初の行として要約を計算する必要があります。
要約はどのように計算できますか？
- A. 比較テーブルの[概要の表示]オプションを使用します。
- B. 要約は標準インターフェースでは使用できないため、SAQLクエリを使用してください。
- C. データフローにcomputeExpressionフィールドを作成して、要約を計算します。
- D. 数式フィールドを追加して要約を計算します。
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