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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analytics Dashboard Implementation | 19% | - Developing selection/result interactions with different query types - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Making dashboards actionable and accessible in Lightning pages - Configuring dashboards using accurate query types and widget-level parameters - Monitoring and optimizing query performance using Dashboard Inspector - Using advanced functionality such as windowing and time series analysis in compare tables |
| Administration | 16% | - Embedding analytics in Salesforce - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations - SAQL query language for dashboard JSON and lens queries |
| Einstein Discovery | 12% | - 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 - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Analyzing model results and proposing data improvements |
| 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 |
| Data Layer | 24% | - Troubleshooting failed dataflow jobs - Incremental sync configuration - Data transformation and loading into Einstein datasets - Dataflow configuration for multi-object scenarios - Understanding denormalized sequential datasets - Data extraction from sources |
| Security | 16% | - Implementing security settings for users, groups, and profiles - Dataset security settings using sharing inheritance and security predicates - App sharing based on user and group requirements |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
Question 1
ストーリーは、予測フィールド「予測金額」を含む Opportunity オブジェクトにデプロイされています。
すべての Opportunity レコードが、Predicted Amount フィールドに書き戻される予測になるにはどうすればよいでしょうか?
A. 予測サービス API を使用して予測を取得し、[予測金額] フィールドに値を入力します。
B. [予測金額] フィールドに値を入力して、すべてのレコードを一括編集します。
C. すべての商談レコードを開きます。
D. 予測定義で一括スコアリング ジョブを有効にします。
Question 2
Einstein Analytics チームは、データフローを開始するとエラーや警告もなく正常に実行されるが、クエリ時に項目の 1 つが値を返さないと報告しています。
この問題の原因は何でしょうか?
A. このフィールドには Salesforce のデータが含まれていません。
B. データフローを実行するユーザーにはフィールドへのアクセス権がありません。
C. 「統合ユーザー プロファイル」にはフィールドへのアクセス権がありません。
D. 「セキュリティ ユーザー プロファイル」にはフィールドへのアクセス権がありません。
Question 3
SAQL のフィルター、制限、順序、およびオフセット関数の順序は何ですか?
Question 4
コンサルタントは、セールスオペレーションチーム向けにEinsteinAnalyticsアプリを作成しました。セールスオペレーションチームは、アプリを会社の他の人と共有したいと考えています。コンサルタントは、アプリをEinsteinAnalyticsテンプレートアプリとして配布することをお勧めします。
セールスオペレーションアプリからテンプレートが作成された後、JSONファイルのセットが生成されます。テンプレートメタデータと他のすべてのテンプレート要素を管理するJSONファイルの名前は何ですか？
A. template-to-app-rules.json
B. template-info.json
C. aster- info.j son
D. ui.json
Question 5
Einstein Discoveryにデータをロードしてストーリーを作成した後、クライアントはEinsteinコンサルタントに「WhyitHappened」チャートの「UnexplainedBar」について説明するように依頼します。
どの説明が正しいですか？
A. 常に0であるか、モデルを使用しないでください。
B. 予測された結果と観察された結果の違いを示します。
C. 分析されたデータとレビューされていないデータの違いを表示します。
D. 説明のない結果にのみ表示されます。
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: Only visible for members | Question 4 Answer: B | Question 5 Answer: B |
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