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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Einstein Discovery | 12% | - Analyzing model results and proposing data improvements - 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) - Adjusting data parameters and adding/removing data or columns to improve models - Monitoring and interpreting Model Cards to improve or maintain model performance |
| 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 |
| Analytics Dashboard Implementation | 19% | - Configuring dashboards using accurate query types and widget-level parameters - Using advanced functionality such as windowing and time series analysis in compare tables - Making dashboards actionable and accessible in Lightning pages - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Monitoring and optimizing query performance using Dashboard Inspector - Developing selection/result interactions with different query types |
| Security | 16% | - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles - App sharing based on user and group requirements |
| Administration | 16% | - SAQL query language for dashboard JSON and lens queries - Embedding analytics in Salesforce - Managing CRM Analytics environment and configurations - Understanding recipes (Data Prep) for visual ETL interface |
| Data Layer | 24% | - Troubleshooting failed dataflow jobs - Data transformation and loading into Einstein datasets - Data extraction from sources - Dataflow configuration for multi-object scenarios - Incremental sync configuration - Understanding denormalized sequential datasets |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
1. 1時間あたりのユーザーあたりの最大アナリティクスAPI呼び出し
A) 100,000
B) 50,000
C) 10,000
D) 5,000
2. 作成されたストーリーの AUC は 0.62 です。
この AUC スコアを改善するためにできることを 2 つ挙げてください。
2 つの答えを選択してください
A) ストーリー インサイトの順序を降順に変更します。
B) トレーニング セットをセグメント化します。
C) ストーリーを作成する前に、データフローで感情の検出を実行します。
D) 関連するデータの行をさらに追加します。
3. 検出の行の上限と下限。
A) 最小00行、最大20,000,000
B) 最小1000行、最大15,000,000行
C) 最小3000行、最大20,000,000行
D) 最小500行、最大25,000,000
4. 顧客は、300 を超える一意の製品名で構成されるデータセットを持っています。製品名を含む予測モデルを要求します。
アインシュタイン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) モデルを調整して、結果変数の極値を排除します。
B) 分析を複数のモデルに分割すると、それぞれのモデルの製品数が少なくなります。
C) 明確にするために、製品名ではなく SKU 番号を使用します。
D) Product オブジェクトのデフォルト変数を使用してモデルを実行します。
5. 組織ごとの最大同時API分析呼び出し
A) 500
B) 100
C) 50
D) 200
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,C | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: B |
