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| Section | Weight | Objectives |
|---|---|---|
| Data Layer | 24% | - Understanding denormalized sequential datasets - Data transformation and loading into Einstein datasets - Dataflow configuration for multi-object scenarios - Data extraction from sources - Troubleshooting failed dataflow jobs - Incremental sync configuration |
| 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% | - Understanding recipes (Data Prep) for visual ETL interface - SAQL query language for dashboard JSON and lens queries - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce |
| 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% | - Monitoring and optimizing query performance using Dashboard Inspector - Developing selection/result interactions with different query types - Configuring dashboards using accurate query types and widget-level parameters - 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 |
| Einstein Discovery | 12% | - Analyzing model results and proposing data improvements - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Adjusting data parameters and adding/removing data or columns to improve models - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Monitoring and interpreting Model Cards to improve or maintain model performance |
1. どの明細セットが年間累積ベースで月次金額を生成しますか?
A)
B)
C)
D) 
2. Einstein Analyticsチームは、データ同期(レプリケーション)を有効にすることを計画しています。
データ同期(レプリケーション)に固有の2つの制限のうち、既存のジョブに影響を与える可能性があるため、機能を有効にする前に検討する必要があるのはどれですか?
2つの答えを選択してください
A) データフロー定義の最大数
B) データフロー変換の最大数
C) データ同期(レプリケーション)を有効にできるオブジェクトの最大数
D) スケジュールされているがまだ実行されていないELT(Extract Load Transform)ジョブのタイムアウト
3. 地域開発コーディネーターの役割が指定された地域とシードバンクの注文にのみアクセスできるようにするために使用できるセキュリティ機能はどれですか?
A) 地域ごとに個別のアプリ
B) 継承されたセキュリティ
C) セキュリティ述語
D) 共有ルール
4. セットアップメニューを使用して、UniversalContainers社はEinsteinAnalytics設定でデータ同期(レプリケーション)をアクティブにしました。同期を実行した後、データセット内のフィールドの合計が、データセットが入力されたSalesforceオブジェクトの同じデータを合計して手動で計算したものとは異なることに気付きました。
新しく同期されたデータの2つの違いのうち、これを説明できるのはどれですか?2つの答えを選択してください
A) 完全に削除されたレコードに関する違い
B) 数式フィールドに関する違い
C) トリガー更新フィールドに関する違い
D) ワークフローで更新されたフィールドに関する違い
5. 無関係なカテゴリの洞察が重要なのはなぜですか?
A) 結果は、関連する変数とはあまり関係がない可能性があります。それはグローバルな効果ともっと関係があるかもしれません。
B) 彼らは結果についてすべてを説明します。
C) それらは重要ではありません。したがって、名前は無関係です。
D) 相互作用の用語が結果にどのように影響するかを説明します。
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A,C | Question # 3 Answer: C | Question # 4 Answer: A,B | Question # 5 Answer: A |
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