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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Data Layer | 24% | - Data extraction from sources - Dataflow configuration for multi-object scenarios - Incremental sync configuration - Understanding denormalized sequential datasets - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs |
| Topic 3: Administration | 16% | - SAQL query language for dashboard JSON and lens queries - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce |
| Topic 4: Analytics Dashboard Design | 13% | - Creating dashboards following CRM Analytics best practices and UX design principles - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Scoping, validating, and prioritizing dashboard design requirements |
| Topic 5: Analytics Dashboard Implementation | 19% | - Developing selection/result interactions with different query types - Configuring dashboards using accurate query types and widget-level parameters - Monitoring and optimizing query performance using Dashboard Inspector - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Making dashboards actionable and accessible in Lightning pages - Using advanced functionality such as windowing and time series analysis in compare tables |
| Topic 6: Einstein Discovery | 12% | - Monitoring and interpreting Model Cards to improve or maintain model performance - Adjusting data parameters and adding/removing data or columns to improve models - Analyzing model results and proposing data improvements - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
1. Einsteinコンサルタントは、EinsteinDiscoveryがお客様に提供した「WhyitHappened」インサイトを確認しています。顧客は結果を検証したいと考えています。コンサルタントはどのような行動を取るべきですか?
A) Rコードモデルの検証結果をエクスポートして確認する方法をお客様に示します
B) p値と標準偏差を確認します
C) データサイエンティストに相談して、調査結果を検証します
D) 共有およびエクスポート機能を使用して、調査結果が論理的に意味があるかどうかを顧客が判断できるようにします
2. Einsteinコンサルタントは、EinsteinAnalyticsの実装を希望するクライアントから新しいプロジェクトを受け取ります。現在、Einstein Analyticsはありませんが、ユーザーが正しくアクセスできるようにする方法についてのガイダンスが必要です。
彼らには1000人のユーザーがいて、データセットとダッシュボードを作成する3人の小さなチームがいます。さらに15人がダッシュボードを作成できるはずです。残りのユーザーは、ダッシュボードのみを表示できるようにする必要があります。
コンサルタントはどの推奨事項をクライアントに提供する必要がありますか?
A) 定義された3種類のロールに従って、Salesforce権限セットを作成して割り当てます。
B) ダッシュボードのみを表示する必要があるユーザーに「EinsteinAnalytics Explorer」ライセンスを割り当て、データセットとダッシュボードを作成できる必要があるユーザーに「EinsteinAnalyticsDeveloper」ライセンスを割り当てます。
C) 定義された3種類の役割に、アプリの権限「viewer /」、「editor」、「manager」を割り当てます。
D) 定義された3種類のロールに従って、3つの新しいSalesforceプロファイルを作成して割り当てます。
3. ある会社は、EinsteinAnalyticsに保存されているデータセットの値から導出できるKPIを表示するWebページを構築したいと考えています。Einstein Analyticsコンサルタントは、これらのデータセットからこれらのKPIをどのように導き出すことができますか?
A) Analytics REST APIを使用し、-query"リソースを活用します。
B) Analytics External Data APIを使用して、「クエリ」リソースを活用します。
C) データセットをXLSファイルとしてエクスポートし、Einstein Analytics ConnectorforExcelを使用します。
D) データセットをCSVファイルとしてエクスポートし、外部データベースにロードします。
4. 次のレベルを除くすべてのレベルでデータへのアクセスを構成できます。
A) 記録
B) ページレイアウト
C) オブジェクト
D) 組織
5. 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、データセットが保存されるアプリを決定します。
B) 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。
C) 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。
D) 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。
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: B |
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