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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Layer | 24% | - Incremental sync configuration - Understanding denormalized sequential datasets - Data extraction from sources - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs |
| Administration | 16% | - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce - SAQL query language for dashboard JSON and lens queries - Understanding recipes (Data Prep) for visual ETL interface |
| Security | 16% | - App sharing based on user and group requirements - Implementing security settings for users, groups, and profiles - Dataset security settings using sharing inheritance and security predicates |
| Einstein Discovery | 12% | - Adjusting data parameters and adding/removing data or columns to improve models - Monitoring and interpreting Model Cards to improve or maintain model performance - 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) |
| Analytics Dashboard Design | 13% | - Creating dashboards following CRM Analytics best practices and UX design principles - Scoping, validating, and prioritizing dashboard design requirements - Identifying appropriate use and configuration of standard CRM Analytics templated apps |
| Analytics Dashboard Implementation | 19% | - Making dashboards actionable and accessible in Lightning pages - Monitoring and optimizing query performance using Dashboard Inspector - Configuring dashboards using accurate query types and widget-level parameters - Using advanced functionality such as windowing and time series analysis in compare tables - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Developing selection/result interactions with different query types |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
1. Einstein Discoveryは、いくつかの異なる種類の推奨される洞察を提供します。これらの洞察のうち、説明的なものはどれですか？
A) 予測と改善
B) なぜそれが起こったのか
C) 何が起こったのか
D) 違いは何ですか
2. データ探索プロセスのステップは何ですか？
A) 表示、探索、削減、保存、共有
B) ダウンロード、表示、絞り込み、保存、共有
C) 表示、探索、絞り込み、保存、共有
3. アナリティクスアプリとは何ですか？
A) 組織のEinsteinAnalytics実装の青写真
B) iPad用アナリティクス
C) Salesforceの外部で実行されるEinsteinAnalyticsプラットフォームの特殊バージョン
D) データセット、ダッシュボード、レンズ、およびデータフローのコレクション
4. 会社のEinsteinAnalyticsダッシュボードを更新および保守するように求められます。
さまざまなデータセットからのステップを含むダッシュボードの1つに対するリクエストが届きます。要求は、あるデータセットのテーブルを別のデータセットのグラフの結果でフィルタリングできるようにすることです。解決策は、結果バインディングを作成することです。
バインディングを作成するには、どの3つのステップを実装する必要がありますか？
A) フィルタリングフィールドのAPI名を検索します
B) ダッシュボードJSONのターゲットステップにバインドする結果を構成します。
C) ソースフィールドのAPI名を検索します
D) ソースとターゲットのステップ名を検索する
5. 構成ウィザードは、測定方法について何を質問しますか？
A) ケース期間
B) 顧客の地域
C) 上記のすべて
D) 顧客満足度
E) ケースの解決
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: A,B,D | Question # 5 Answer: C |
