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 - Monitoring and optimizing query performance using Dashboard Inspector - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Configuring dashboards using accurate query types and widget-level parameters - Using advanced functionality such as windowing and time series analysis in compare tables |
| 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: Data Layer | 24% | - Understanding denormalized sequential datasets - Dataflow configuration for multi-object scenarios - Troubleshooting failed dataflow jobs - Incremental sync configuration - Data extraction from sources - Data transformation and loading into Einstein datasets |
| Topic 4: 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 |
| Topic 5: Einstein Discovery | 12% | - Analyzing model results and proposing data improvements - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Adjusting data parameters and adding/removing data or columns to improve models - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Monitoring and interpreting Model Cards to improve or maintain model performance |
| Topic 6: Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations - SAQL query language for dashboard JSON and lens queries - Embedding analytics in Salesforce |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
1. ファセットの魔法は何ですか？
A) データのすべての面をひし形のグラフで表示するグラフを作成します。
B) 1つのグラフで何かを選択すると、その選択によって他のグラフがフィルタリングされるように、グラフがバインドされます。
C) 表示しているチャートの期間をシフトします。
D) データフローのオンとオフを切り替えます。
2. 1つの変数が結果変数の変動をどのように説明するかを調べる洞察のタイプの別名は何ですか？
A) 3次分析
B) 二次分析
C) オブジェクト指向分析
D) 一次分析
E) スペクトル分析
3. Einstein Discovery ストーリーの作成には次のことが含まれます。
A) Microsoft PowerPoint プレゼンテーションのアップロード
B) データに関するメモの入力
C) 企業レポートからテキストをコピーして Einstein Discovery に貼り付ける
D) Einstein Analytics でデータセットを選択し、[ストーリーの作成] をクリックします。
4. データセットビルダーを使用するときに、適切なルートオブジェクトを選択することが重要なのはなぜですか？
A) ルートオブジェクトは、データセットを表示できるユーザーを決定します。
B) ルートオブジェクトのデータのみをデータセットに含めることができます。
C) ルートオブジェクトは、データセットに追加できる関連オブジェクトを決定します。
D) ルートオブジェクトは、データセットが保存されるアプリを決定します。
5. Einstein Discoveryストーリーを構築するためのデータセットには、潜在的に関連する予測子である72のフィールドが含まれています。
意味のある堅牢なモデルを取得するために、上位の予測子を評価するためのベストプラクティスと見なされるアプローチはどれですか。
A) すべての予測子を使用してストーリーを構築し、EinsteinDiscoveryが上位の予測子を表示する必要があることを示します。
B) 最初の一連の予測子を使用してストーリーを作成し、ストーリーにとってどの予測子が重要であるかを評価します。次に、重要度の低いものを削除し、最初の実行で省略された予測子を追加して、それらの影響を評価します。
C) ストーリーを作成するために、データの準備に戻り、フィールドの数を30未満に減らします。
D) このデータセットは大きすぎるため、EinsteinDiscoveryでは使用できません。予測子が少ない新しいデータセットをリクエストします。
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: B |
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