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 |
|---|---|---|
| Topic 1: Analytics Dashboard Implementation | 19% | - Making dashboards actionable and accessible in Lightning pages - Developing selection/result interactions with different query types - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Using advanced functionality such as windowing and time series analysis in compare tables - Monitoring and optimizing query performance using Dashboard Inspector - Configuring dashboards using accurate query types and widget-level parameters |
| Topic 2: 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 3: 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 4: Analytics Dashboard Design | 13% | - Creating dashboards following CRM Analytics best practices and UX design principles - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Scoping, validating, and prioritizing dashboard design requirements |
| Topic 5: Einstein Discovery | 12% | - 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 - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Monitoring and interpreting Model Cards to improve or maintain model performance |
| Topic 6: Data Layer | 24% | - Understanding denormalized sequential datasets - Troubleshooting failed dataflow jobs - Incremental sync configuration - Dataflow configuration for multi-object scenarios - Data extraction from sources - Data transformation and loading into Einstein datasets |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
コンサルタントは、運送会社向けに Tableau CRM ダッシュボードを構築しました。コンサルタントはデータ同期 (レプリケーション) を有効にして、データセットの更新速度を向上させました。
ダッシュボード上のデータはどのくらいの頻度で更新されますか?
- A. ユーザーがダッシュボードを開くたび
- B. レシピが完了まで実行され、その後データ同期が完了まで実行される場合。
- C. ダッシュボード ビューアが [更新] ボタンをクリックしたとき。
- D. データ同期が完了まで実行され、次にレシピが完了まで実行された場合。
Correct Answer: B 🗳️
テーブルの比較式では、次のコマンドで他の列を参照できます。
- A. 上記のすべて
- B. 文字（A..Z）
- C. 彼らの名前
- D. 数字（1..9）
Correct Answer: B 🗳️
コンサルタントは、製品ファミリごとの予測売上を確認するために、売上のダッシュボードを作成するように求められます。要件を収集した後、コンサルタントは時系列SAQLステートメントを利用して売上を予測することを決定します。
SAQLクエリを作成するときに実行する必要がある2つのアクションはどれですか。
2つの答えを選択してください
- A. 時系列に必要なパラメーターである予測区間を80にするか95にするかを決定します。
- B. 不足している日付を埋めるためにfillSAQLステートメントを含めます。
- C. 適切な予測を行うのに十分なデータがデータセットにあることを検証します。
- D. 利用可能なデータに最適な予測モデルを調査します。
Correct Answer: B,C 🗳️
これらの標準偏差のうち、平均から最も離れた値を持つ曲線の標準偏差はどれですか？
- A. 4
- B. 16
- C. 8
- D. 1
Correct Answer: B 🗳️
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. 結果のバインド
- B. 説明バインディング
- C. セレクションバインディング
- D. データバインディング
Correct Answer: A,C 🗳️
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