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Salesforce Tableau-CRM-Einstein-Discovery-Consultant日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Layer | 24% | - Troubleshooting failed dataflow jobs - Incremental sync configuration - Data transformation and loading into Einstein datasets - Dataflow configuration for multi-object scenarios - Data extraction from sources - Understanding denormalized sequential datasets |
| 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 |
| Administration | 16% | - Managing CRM Analytics environment and configurations - SAQL query language for dashboard JSON and lens queries - Understanding recipes (Data Prep) for visual ETL interface - Embedding analytics in Salesforce |
| Security | 16% | - App sharing based on user and group requirements - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles |
| Einstein Discovery | 12% | - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - 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 - 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 - Monitoring and optimizing query performance using Dashboard Inspector - Making dashboards actionable and accessible in Lightning pages - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Using advanced functionality such as windowing and time series analysis in compare tables - Developing selection/result interactions with different query types |
Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版) Sample Questions:
Question #1
edgemartトランスフォーメーションにより、Salesforceデータ、外部データ、またはその両方の組み合わせを含むことができる既存の登録済みデータセットへのデータフローアクセスが可能になります。
エッジマート変換はどこで指定されますか？
A. 設定
B. データフローエディタ
C. ステップエディター
D. レシピエディター
Question #2
Einstein Discoveryが既知のパターンのみを検出したことにクライアントが失望した場合の適切な対応は何ですか？
A. Einstein Analyticsなどの視覚化ツールを使用して、詳細を明らかにすることをお勧めします。
B. ユースケースが正確でない可能性があることを伝えます。
C. アインシュタインが洞察までの時間を短縮することを強調します。これは、経験から学ぶよりもはるかに高速です。
D. テクノロジーはデータと同じくらい優れていることを彼らに思い出させてください。
Question #3
Universal Containers社は、外部ツールからEinstein Analyticsにターゲットデータをアップロードして、営業チームのターゲット達成度を計算できるようにすることを計画しています。
ターゲットデータは毎月変更されるため、データセットは毎月更新する必要があります。ターゲットデータは、営業担当者のSalesforce ID、ターゲット金額、およびターゲットの月を含むCSVファイルです。営業担当者ごとに、ファイルには、今年のすべての月と過去のすべての年の目標が含まれています。
この情報に基づいて、コンサルタントはファイルをアップロードするためにAnalytics External Data APIでどの操作を使用する必要がありますか？
A. 上書き
B. 更新
C. 追加
D. アップサート
Question #4
1つの変数が結果変数の変動をどのように説明するかを調べる洞察のタイプの別名は何ですか？
A. スペクトル分析
B. 二次分析
C. オブジェクト指向分析
D. 一次分析
E. 3次分析
Question #5
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？
A. 期間を計算するためのオフセットパラメーターを使用したcomputeRelative変換
B. 期間を計算するためのSAQL式を使用したcomputeRelative変換
C. 期間を計算するためのSAQL式を使用したcomputeExpression変換
D. オフセット式を使用して変換をフラット化し、期間を計算します
Solutions:
| Question #1 Answer: B | Question #2 Answer: D | Question #3 Answer: A | Question #4 Answer: D | Question #5 Answer: B |
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