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| Section | Weight | Objectives |
|---|---|---|
| Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - Embedding analytics in Salesforce - SAQL query language for dashboard JSON and lens queries - Managing CRM Analytics environment and configurations |
| Analytics Dashboard Design | 13% | - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Scoping, validating, and prioritizing dashboard design requirements - Creating dashboards following CRM Analytics best practices and UX design principles |
| Data Layer | 24% | - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs - Data extraction from sources - Understanding denormalized sequential datasets - Dataflow configuration for multi-object scenarios - Incremental sync configuration |
| 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 |
| Einstein Discovery | 12% | - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Monitoring and interpreting Model Cards to improve or maintain model performance - 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 |
| Analytics Dashboard Implementation | 19% | - Using advanced functionality such as windowing and time series analysis in compare tables - Developing selection/result interactions with different query types - Making dashboards actionable and accessible in Lightning pages - Configuring dashboards using accurate query types and widget-level parameters - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Monitoring and optimizing query performance using Dashboard Inspector |
コンサルタントは、進行中の不正取引の支援を必要とするクレジットカード会社と協力しています。同社は、コンサルタントがEinsteinDiscoveryで分析するための代表的なサンプルデータセットを提供しています。ストーリーの最初の評価は、サードパーティの支払いアプリがこれらの不正な取引の原因であることを示しています。ただし、同社はこの評価結果を拒否し、懸念されるほど長い間、この支払いアプリとのパートナーシップを結んでいないと述べています。
ストーリーの結果を改善するために推奨される次のステップは何ですか?
Correct Answer: D 🗳️
Tableau CRMコンサルタントは、レシピを使用して新しいデータセットを作成することにしました。
レシピに使用できる2つのソースタイプはどれですか?
2つの答えを選択してください
Correct Answer: B,C 🗳️
ある会社は、EinsteinAnalyticsを使用して販売パイプラインの傾向を分析したいと考えています。同社は販売プロセスを加速したいと考えています。そのためには、機会が各段階で費やす平均時間を知る必要があります。この値はSalesforceで事前に計算されていないため、コンサルタントはデータフローを使用して計算することをお勧めします。
データフローを使用して、商談が各ステージで費やす平均時間をどのように計算できますか?
Correct Answer: B 🗳️
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AnalyticsのCSVアップロードのステータスはどこで確認できますか?
Correct Answer: D 🗳️
リストウィジェットは、既存のチャートとテーブルを含むダッシュボードに追加されます。リストウィジェットがウィジェットのプロパティを使用してダッシュボードのチャートとテーブルをファセット化するには、何が当てはまる必要がありますか?
Correct Answer: D 🗳️
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