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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:

SectionObjectives
Einstein Discovery- Predictive Modeling Concepts
  • 1. Story creation and interpretation
    • 2. Insights and recommendations
      Analytics Dashboarding- Lenses and Dashboards
      • 1. Creating and configuring dashboards
        • 2. Lens exploration and visualization types
          Data Preparation and Dataflows- Data Preparation Tools
          • 1. Data transformation logic
            • 2. Recipes and dataflows usage
              Data Layer and Data Management- Data Modeling and Data Sources
              • 1. Understanding CRM Analytics datasets
                • 2. Data ingestion and transformation concepts
                  Security and Access- Row-Level Security
                  • 1. Security predicates and sharing inheritance

                    Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:

                    Question #1

                    インサイトリストでは、インサイトはどのような順序でランク付けされていますか?

                    • A. 地理空間
                    • B. 注文はありません
                    • C. アルファベット順
                    • D. 統計的有意性
                    • E. 時間
                    Reveal Solution  Discussion  0

                    Correct Answer: D  🗳️

                    Question #2

                    大企業には、すべての営業担当者の達成フィールドとコミッションフィールドを含む単一のデータセットがあります。各営業担当者は、部門内の各担当者の達成データを表示できる必要があります。各担当者は、自分のコミッションデータのみを表示できる必要があります。
                    この要件を適用するには、どのオプションを使用する必要がありますか?

                    • A. 達成とコミッションのために別々のデータセットを作成し、セキュリティ述語を適用したり、継承を共有したりします。
                    • B. 既存の単一データセットにセキュリティ述語を適用します。
                    • C. 共有継承を使用します。
                    • D. 販売組織を達成データセットアクセスリストに追加します。
                    Reveal Solution  Discussion  0

                    Correct Answer: A  🗳️

                    Question #3

                    Einstein Discoveryストーリーを構築するためのデータセットには、潜在的に関連する予測子である72のフィールドが含まれています。
                    意味のある堅牢なモデルを取得するために、上位の予測子を評価するためのベストプラクティスと見なされるアプローチはどれですか。

                    • A. このデータセットは大きすぎるため、EinsteinDiscoveryでは使用できません。予測子が少ない新しいデータセットをリクエストします。
                    • B. ストーリーを作成するために、データの準備に戻り、フィールドの数を30未満に減らします。
                    • C. 最初の一連の予測子を使用してストーリーを作成し、ストーリーにとってどの予測子が重要であるかを評価します。次に、重要度の低いものを削除し、最初の実行で省略された予測子を追加して、それらの影響を評価します。
                    • D. すべての予測子を使用してストーリーを構築し、EinsteinDiscoveryが上位の予測子を表示する必要があることを示します。
                    Reveal Solution  Discussion  0

                    Correct Answer: C  🗳️

                    Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).

                    Question #4

                    比較テーブルのデフォルトの#行。

                    • A. 1000
                    • B. 1500
                    • C. 2000
                    • D. 3000
                    Reveal Solution  Discussion  0

                    Correct Answer: C  🗳️

                    Question #5

                    会社のEinsteinAnalyticsチームは、Opportunity__cカスタムオブジェクトに基づいてクラタセットを作成しました。営業担当副社長は、データセットを開いて調査するときに「結果が見つかりません」というメッセージが表示されたと報告します。ロール階層のVPの下にある他のユーザーは、同じデータセットの行を表示できます。この問題を引き起こしている可能性のある2つの問題はどれですか?

                    • A. VPのSalesforceプロファイルには、Opportunity__cカスタムオブジェクトの一部のフィールドに対する読み取り権限がありません
                    • B. データセットレベルで設定されたセキュリティ述語がVPによるデータの表示を妨げています
                    • C. データセットはSalesforceから共有を継承しており、VPは3000を超える行を表示できます
                    • D. VPのSalesforceプロファイルには、Opportunity__cカスタムオブジェクトに対する読み取り権限がありません
                    Reveal Solution  Discussion  0

                    Correct Answer: C,D  🗳️

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