- Exam Code: Tableau-CRM-Einstein-Discovery-Consultant-JPN
- Exam Name: Salesforce Tableau CRM Einstein Discovery Consultant (Tableau-CRM-Einstein-Discovery-Consultant日本語版)
- Updated: Aug 06, 2026
- Q & A: 244 Questions and Answers
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| Section | Weight | Objectives |
|---|---|---|
| Einstein Discovery | 12% | - Monitoring and interpreting Model Cards to improve or maintain model performance - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Analyzing model results and proposing data improvements - Adjusting data parameters and adding/removing data or columns to improve models - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics |
| Data Layer | 24% | - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs - Dataflow configuration for multi-object scenarios - Data extraction from sources - Understanding denormalized sequential datasets - Incremental sync configuration |
| Analytics Dashboard Implementation | 19% | - Developing selection/result interactions with different query types - Using advanced functionality such as windowing and time series analysis in compare tables - 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 |
| Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce - SAQL query language for dashboard JSON and lens queries |
| Analytics Dashboard Design | 13% | - Creating dashboards following CRM Analytics best practices and UX design principles - Scoping, validating, and prioritizing dashboard design requirements - Identifying appropriate use and configuration of standard CRM Analytics templated apps |
| Security | 16% | - Implementing security settings for users, groups, and profiles - Dataset security settings using sharing inheritance and security predicates - App sharing based on user and group requirements |
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) 注文はありません
B) 地理空間
C) 時間
D) 統計的有意性
E) アルファベット順
2. UniversalContainersにはSalesManagerダッシュボードがあり、顧客数と年間およびアカウントタイプごとの年間収益の合計を表示する比較テーブルがあります。コンサルタントは、顧客に関する詳細を強化するために、さらにグループを追加するように求められます。
SAQLを使用してクエリを作成するのではなく、比較テーブルを引き続き使用するためにコンサルタントが認識しなければならないグループ化列の最大数はいくつですか。
A) 10
B) 8
C) 無制限
D) 4
3. アインシュタインディスカバリーストーリーとは何ですか?
A) 単一の結果変数とその変数に影響を与える要因との関係を理解するのに役立つグラフ、統計分析、およびテキストのコレクション
B) 娯楽のために語られた架空または実在の人々とイベントの説明
C) 複数の結果変数とそれらの変数に影響を与える要因との関係を理解するのに役立つグラフ、統計分析、およびテキストのコレクション
D) 結果の原因を正確に証明するグラフ、統計分析、およびテキストのコレクション
4. コンサルタントは、店舗での消費者製品の 1 日の販売数量を最大化するためのストーリーを作成しました。ストーリーを作成した後、コンサルティングには Einstein Discovery によってこのデータアラートが表示されます (図を参照)。
取るべき適切な行動を 2 つ挙げてください。
2 つの答えを選択してください
A) Einstein によって提案された外れ値を削除し、モデル メトリクスとストーリー インサイトを使用して品質が向上したかどうかを検証します。
B) Einstein の提案に従って外れ値を削除し、モデルをデプロイします。
C) 0 未満の売上を手動で削除します (マイナスの売上はデータの問題である必要があります)。ただし、大きな値は維持します (データが多ければ多いほど、モデルはより良くなります)。
D) 0 未満および 2,489 を超える値が非常に珍しいため、ストーリーから除外する必要があるかどうか、クライアントと話し合ってください。
5. Analyticsのクエリ内の3つの基本的なアクションは何ですか?3を選択します。
A) 視覚化するメジャーごとに行をカウントします。
B) メジャーで集計
C) 結果をグラフィカルに表示する
D) 結果を絞り込むためにフィルタリングします。
E) 次元によるグループ化
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: B,D | Question # 5 Answer: B,D,E |
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