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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Einstein Discovery | 12% | - 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) - 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 Implementation | 19% | - Configuring dashboards using accurate query types and widget-level parameters - Making dashboards actionable and accessible in Lightning pages - Monitoring and optimizing query performance using Dashboard Inspector - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Developing selection/result interactions with different query types - Using advanced functionality such as windowing and time series analysis in compare tables |
| Data Layer | 24% | - Dataflow configuration for multi-object scenarios - Troubleshooting failed dataflow jobs - Data transformation and loading into Einstein datasets - Incremental sync configuration - Understanding denormalized sequential datasets - Data extraction from sources |
| 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 |
| Analytics Dashboard Design | 13% | - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Creating dashboards following CRM Analytics best practices and UX design principles - Scoping, validating, and prioritizing dashboard design 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 |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
1. ストーリーは、予測フィールド「予測金額」を含む Opportunity オブジェクトにデプロイされています。
すべての Opportunity レコードが、Predicted Amount フィールドに書き戻される予測になるにはどうすればよいでしょうか?
A) すべての商談レコードを開きます。
B) 予測サービス API を使用して予測を取得し、[予測金額] フィールドに値を入力します。
C) [予測金額] フィールドに値を入力して、すべてのレコードを一括編集します。
D) 予測定義で一括スコアリング ジョブを有効にします。
2. アナリティクスアプリまたはダッシュボードを構築する際に考慮すべき2つの主要な設計原則は何ですか？2つの答えを選択してください
A) 明確さ：それらが整頓されていて、解釈しやすいことを確認してください。
B) 一貫性：ユーザーの使いやすさを強化するために、ユーザーが親しみやすさを持っていることを確認します。
C) 強調：見出しやキーチャートなどの重要な情報のためのスペースがあることを確認します。
D) バランス：より興味深いデザインを得るために、異なるチャートのバランスが取れていることを確認してください。
3. Universal Containersは、リストウィジェットでの選択が、EinsteinAnalyticsダッシュボードの1つにある円グラフに影響を与えていないことを報告します。リストウィジェットと円グラフに関連付けられているステップオプションが図に示されています。
ステップが異なるデータセットを使用していると仮定します。アインシュタインコンサルタントがこの問題を解決するために行うことができる2つの変更はどれですか？2つの答えを選択してください
A) 「データソースの接続」を使用して、2つのウィジェットを接続するための接続を作成します。
B) ステップ「Type_l」のフィルターセクションで選択バインディングを使用します。
C) Cheステップ「Step_pie_2」のフィルターセクションで選択バインディングを使用します。
D) 「データソースの接続」を使用して、2つのデータセットを接続するための接続を作成します。
4. 比較テーブルのデフォルトの＃行。
A) 2000
B) 1500
C) 3000
D) 1000
5. SAQL のフィルター、制限、順序、およびオフセット関数の順序は何ですか?
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A,B | Question # 3 Answer: C,D | Question # 4 Answer: A | Question # 5 Answer: Only visible for members |
