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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Discovery | 12% | - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Analyzing model results and proposing data improvements - Monitoring and interpreting Model Cards to improve or maintain model performance - Adjusting data parameters and adding/removing data or columns to improve models |
| Topic 2: Administration | 16% | - Managing CRM Analytics environment and configurations - SAQL query language for dashboard JSON and lens queries - Understanding recipes (Data Prep) for visual ETL interface - Embedding analytics in Salesforce |
| 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 - Scoping, validating, and prioritizing dashboard design requirements - Identifying appropriate use and configuration of standard CRM Analytics templated apps |
| Topic 5: Data Layer | 24% | - Data extraction from sources - Incremental sync configuration - Understanding denormalized sequential datasets - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs - Dataflow configuration for multi-object scenarios |
| Topic 6: Analytics Dashboard Implementation | 19% | - Configuring dashboards using accurate query types and widget-level parameters - 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 - Using advanced functionality such as windowing and time series analysis in compare tables |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
1. コンサルタントは、進行中の不正取引の支援を必要とするクレジットカード会社と協力しています。同社は、コンサルタントがEinsteinDiscoveryで分析するための代表的なサンプルデータセットを提供しています。ストーリーの最初の評価は、サードパーティの支払いアプリがこれらの不正な取引の原因であることを示しています。ただし、同社はこの評価結果を拒否し、懸念されるほど長い間、この支払いアプリとのパートナーシップを結んでいないと述べています。
ストーリーの結果を改善するために推奨される次のステップは何ですか？
A) クレジットカード会社のドメイン知識を使用し、サードパーティの支払いアプリをストーリーから除外します。
B) サードパーティの支払いアプリが原因であることをより明確に示すために、ストーリーを調整します。
C) 分析するより包括的なデータセットをクレジットカード会社に依頼します。
D) ストーリーが偏りのない結果を返し、最初の評価が正確であることを会社に説明します。
2. Einstein Discovery ストーリー用にトレーニング データセットが準備されています。1 つは外れ値で偏っています。アインシュタインのコンサルタントはどのような行動をとるべきでしょうか?
A) フィールドは結果変数ではないため、何もありません。
B) ビニング方法を固定幅に変更します。
C) 不正なデータがあるため、フィールドを削除します。
D) 外れ値の行を削除します。
3. 顧客は大量のデータを表示し、ユーザーの注意を適切につかむために棒グラフの特定の値を動的に強調する方法を望んでいます。
アインシュタインコンサルタントは、このタスクを実行するために何を使用できますか？
A) 比較表
B) XMD
C) チャートマーカー
D) ツールチップをカスタマイズする
4. トレンドデータの制限：ユーザーあたりのトレンドデータセットの最大数とスナップショットあたりの行数：
A) ユーザーあたり10、スナップショットあたり100,000
B) ユーザーあたり5、スナップショットあたり100,000
C) ユーザーあたり5、スナップショットあたり200,000
D) ユーザーあたり10、スナップショットあたり200,000
5. アインシュタインディスカバリーストーリーとは何ですか？
A) 複数の結果変数とそれらの変数に影響を与える要因との関係を理解するのに役立つグラフ、統計分析、およびテキストのコレクション
B) 娯楽のために語られた架空または実在の人々とイベントの説明
C) 結果の原因を正確に証明するグラフ、統計分析、およびテキストのコレクション
D) 単一の結果変数とその変数に影響を与える要因との関係を理解するのに役立つグラフ、統計分析、およびテキストのコレクション
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: D |
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