Data-Cloud-Consultant日本語無料試験問題と解答PDF最新問題2024年11月
最新Data-Cloud-Consultant日本語試験問題集で最近更新された140問題
質問 # 69
顧客は、放棄された閲覧動作のジャーニーをトリガーするための要件の概要を説明しました。要件に基づいて、コンサルタントは、ストリーミング分析情報を使用して、Journey Builder に対するデータ アクションを 1 時間ごとにトリガーすることを決定します。
データアクションが必要な頻度で確実にトリガーされるように、コンサルタントはソリューションをどのように構成すればよいでしょうか?
- A. ジャーニー エントリ スケジュールを 1 時間ごとに実行するように設定します。
- B. 時間ごとのバッチで取り込まれるデータを構成します。
- C. アクティブ化スケジュールを時間ごとに設定します。
- D. インサイトの集計時間枠を 1 時間に設定します。
正解：D
解説：
Streaming insights are computed from real-time engagement events and can be used to trigger data actions based on pre-set rules. Data actions are workflows that send data from Data Cloud to other systems, such as Journey Builder. To ensure that the data action is triggered every hour, the consultant should set the insights aggregation time window to 1 hour. This means that the streaming insight will evaluate the events that occurred within the last hour and execute the data action if the conditions are met. The other options are not relevant for streaming insights and data actions. References: Streaming Insights and Data Actions Limits and Behaviors, Streaming Insights, Streaming Insights and Data Actions Use Cases, Use Insights in Data Cloud, 6 Ways the Latest Marketing Cloud Release Can Boost Your Campaigns
質問 # 70
セグメントを構築するときに、新しくモデル化されたデータからの推奨値がユーザーに表示されません。
この問題の原因は何ですか?
- A. 値の提案は直接の属性に対してのみ機能し、関連する属性に対しては機能しません。
- B. 値の提案は、特定の属性の最初の 50 個の値の結果のみを返します。
- C. 値の提案はまだ処理中であり、利用可能になるまでに最大 24 時間かかります。
- D. 値の提案には、少なくともデータ認識スペシャリストの権限が必要です。
正解：C
解説：
The most likely cause of this issue is that value suggestion is still processing and takes up to 24 hours to be available. Value suggestion is a feature that enables you to see suggested values for data model object (DMO) fields when creating segment filters. However, this feature needs to be enabled for each DMO field, and it can take up to 24 hours for the suggested values to appear after enabling the feature1. Therefore, if a user is not seeing suggested values from newly-modeled data, it could be that the data has not been processed yet by the value suggestion feature. References:
* Use Value Suggestions in Segmentation
質問 # 71
Cumulus Financial は、2 つ以上の投資信託に投資した個人を含む「Multiple Investments」というセグメントを作成しました。
同社は、新しい投資信託の募集に関する電子メールをこの層に送信する予定で、各顧客の現在の投資信託投資に関する情報を含む電子メールの内容をパーソナライズしたいと考えています。
データ クラウド コンサルタントはこのアクティベーションをどのように構成する必要がありますか?
- A. ターゲット システムでの後処理のために、デフォルトでファンド名とファンド タイプを含めます。
- B. [複数投資] セグメントを選択し、[電子メール] 連絡先を選択して、関連属性 [ファンド タイプ] を追加します。
- C. 関連属性として「投資信託」に等しいファンド タイプを含めます。追加の属性を持たない新しいセグメントに基づいてアクティベーションを構成します。
- D. [複数の投資] セグメントを選択し、[電子メール] コンタクト ポイントを選択し、関連属性 [ファンド名] を追加し、[投資信託] に等しいファンド タイプの関連属性フィルターを追加します。
正解：D
解説：
To personalize the email content with information about each customer's current mutual fund investments, the Data Cloud consultant needs to add related attributes to the activation. Related attributes are additional data fields that can be sent along with the segment to the target system for personalization or analysis purposes. In this case, the consultant needs to add the Fund Name attribute, which contains the name of the mutual fund that the customer has invested in, and apply a filter for Fund Type equal to "Mutual Fund" to ensure that only relevant data is sent. The other options are not correct because:
* A. Including Fund Type equal to "Mutual Fund" as a related attribute is not enough to personalize the email content. The consultant also needs to include the Fund Name attribute, which contains the specific name of the mutual fund that the customer has invested in.
* C. Adding related attribute Fund Type is not enough to personalize the email content. The consultant also needs to add the Fund Name attribute, which contains the specific name of the mutual fund that the customer has invested in, and apply a filter for Fund Type equal to "Mutual Fund" to ensure that only relevant data is sent.
* D. Including Fund Name and Fund Type by default for post processing in the target system is not a valid option. The consultant needs to add the related attributes and filters during the activation configuration in Data Cloud, not after the data is sent to the target system. References: Add Related Attributes to an Activation - Salesforce, Related Attributes in Activation - Salesforce, Prepare for Your Salesforce Data Cloud Consultant Credential
質問 # 72
コンサルタントは、クエリ API を使用して取得できるセグメント データを Audience DMO に公開する必要があります。
アクティベーション ターゲットを作成するときに、コンサルタントはどのタイプのターゲットを選択する必要がありますか?
- A. データクラウド
- B. マーケティングクラウド
- C. マーケティングクラウドパーソナライゼーション
- D. 外部アクティベーションターゲット
正解：D
解説：
Purpose of Activation Targets:
* Activation targets define where and how segment data is published for use in various applications and platforms.
質問 # 73
Cumulus Financial は、Data Cloud を使用して銀行顧客をセグメント化し、Cloud File Storage アクティベーションを介してダイレクト メール用に顧客をアクティベートします。同社は、過去 2 年以内にこのセグメントに所属していた個人も分析したいと考えています。
どのデータ クラウド コンポーネントがこれを可能にしますか?
- A. ネストされたセグメント
- B. 計算された洞察
- C. セグメントの除外
- D. セグメント メンバーシップ データ モデル オブジェクト
正解：D
解説：
Data Cloud allows customers to analyze the segment membership history of individuals using the Segment Membership data model object. This object stores information about when an individual joined or left a segment, and can be used to create reports and dashboards to track segment performance over time. Cumulus Financial can use this object to filter individuals who have been in the segment within the last 2 years and compare them with other metrics.
The other options are not Data Cloud components that allow for this analysis. Segment exclusion is a feature that allows customers to remove individuals from a segment based on another segment. Nested segments are segments that are created from other segments using logical operators. Calculated insights are derived attributes that are created from existing data using formulas.
References:
* Segment Membership Data Model Object
* Data Cloud Reports and Dashboards
* Create a Segment in Data Cloud
質問 # 74
Cumulus Financial は、サービス エージェントが連絡先レコード上の Unified Individual に関連付けられたすべてのケースの表示を閲覧できるようにしたいと考えています。
このユースケースでコンサルタントが考慮すべき 2 つの機能はどれですか?
2 つの答えを選択してください
- A. データアクション
- B. APL のクエリ
- C. Lightning Web コンポーネント
- D. プロファイル API
正解：C、D
解説：
A Unified Individual is a profile that combines data from multiple sources using identity resolution rules in Data Cloud. A Unified Individual can have multiple contact points, such as email, phone, or address, that link to different systems and records. A consultant can use the following features to display all cases associated with a Unified Individual on a contact record:
Profile API: This is a REST API that allows you to retrieve and update Unified Individual profiles and related attributes in Data Cloud. You can use the Profile API to query the cases that are related to a Unified Individual by using the contact point ID or the unified ID as a filter. You can also use the Profile API to update the Unified Individual profile with new or modified case information from other systems.
Lightning Web Components: These are custom HTML elements that you can use to create reusable UI components for your Salesforce apps. You can use Lightning Web Components to create a custom component that displays the cases related to a Unified Individual on a contact record. You can use the Profile API to fetch the data from Data Cloud and display it in a table, list, or chart format. You can also use Lightning Web Components to enable actions, such as creating, editing, or deleting cases, from the contact record.
The other two options are not relevant for this use case. A Data Action is a type of action that executes a flow, a data action target, or a data action script when an insight is triggered. A Data Action is used for activation and personalization, not for displaying data on a contact record. A Query APL is a query language that allows you to access and manipulate data in Data Cloud. A Query APL is used for data exploration and analysis, not for displaying data on a contact record. References: Profile API Developer Guide, Lightning Web Components Developer Guide, Create Unified Individual Profiles Unit
質問 # 75
データの取り込みとアクティベーション用に個別の Amazon S3 バケットをサポートする構成はどれですか?
- A. データ クラウド設定内の複数の S3 コネクタ
- B. アクティベーション設定の専用 S3 データ ソース
- C. データ クラウド設定の専用 S3 データ ソース
- D. データ ストリームとアクティベーション ターゲットの個別のユーザー資格情報
正解：C
解説：
To support separate Amazon S3 buckets for data ingestion and activation, you need to configure dedicated S3 data sources in Data Cloud setup. Data sources are used to identify the origin and type of the data that you ingest into Data Cloud1. You can create different data sources for each S3 bucket that you want to use for ingestion or activation, and specify the bucket name, region, and access credentials2. This way, you can separate and organize your data by different criteria, such as brand, region, product, or business unit3. The other options are incorrect because they do not support separate S3 buckets for data ingestion and activation. Multiple S3 connectors are not a valid configuration in Data Cloud setup, as there is only one S3 connector available4. Dedicated S3 data sources in activation setup are not a valid configuration either, as activation setup does not require data sources, but activation targets5. Separate user credentials for data stream and activation target are not sufficient to support separate S3 buckets, as you also need to specify the bucket name and region for each data source2. References: Data Sources Overview, Amazon S3 Storage Connector, Data Spaces Overview, Data Streams Overview, Data Activation Overview
質問 # 76
ノーザン トレイル アウトフィッターズはデータ クラウドの実装を希望しており、いくつかのユースケースを念頭に置いています。
Data Cloud に適していると考えられる 2 つのユースケースはどれですか?
2 つの答えを選択してください
- A. 個別のビジネス インテリジェンス ツールと IT データ管理ツールの必要性を排除するため
- B. 調和されたデータを使用して、顧客とビジネスへの影響をより正確に理解するため
- C. クロスチャネル マーケティング メッセージを作成および調整するため
- D. 顧客のアイデンティティを照合するために、さまざまなソースからデータを取り込んで統合するため
正解：B、D
解説：
Data Cloud is a data platform that can help customers connect, prepare, harmonize, unify, query, analyze, and act on their data across various Salesforce and external sources. Some of the use cases that are considered a good fit for Data Cloud are:
* To ingest and unify data from various sources to reconcile customer identity. Data Cloud can help customers bring all their data, whether streaming or batch, into Salesforce and map it to a common data model. Data Cloud can also help customers resolve identities across different channels and sources and create unified profiles of their customers.
* To use harmonized data to more accurately understand the customer and business impact. Data Cloud can help customers transform and cleanse their data before using it, and enrich it with calculated insights and related attributes. Data Cloud can also help customers create segments and audiences based on their data and activate them in any channel. Data Cloud can also help customers use AI to predict customer behavior and outcomes.
The other two options are not use cases that are considered a good fit for Data Cloud. Data Cloud does not provide features to create and orchestrate cross-channel marketing messages, as this is typically handled by other Salesforce solutions such as Marketing Cloud. Data Cloud also does not eliminate the need for separate business intelligence and IT data management tools, as it is designed to work with them and complement their capabilities.
References:
* Learn How Data Cloud Works
* About Salesforce Data Cloud
* Discover Use Cases for the Platform
* Understand Common Data Analysis Use Cases
質問 # 77
顧客は Salesforce CRM とリアルタイムで統合する必要があります。
この要件を満たす機能はどれですか?
- A. データアクションと Lightning Web コンポーネント
- B. 販売とサービスのバンドル
- C. ストリーミング変換
- D. データ モデル トリガー
正解：C
解説：
The correct answer is A. Streaming transforms. Streaming transforms are a feature of Data Cloud that allows real-time data integration with Salesforce CRM. Streaming transforms use the Data Cloud Streaming API to synchronize micro-batches of updates between the CRM data source and Data Cloud in near-real time1. Streaming transforms enable Data Cloud to have the most current and accurate CRM data for segmentation and activation2.
The other options are incorrect for the following reasons:
* B. Data model triggers. Data model triggers are a feature of Data Cloud that allows custom logic to be executed when data model objects are created, updated, or deleted3. Data model triggers do not integrate data with Salesforce CRM, but rather manipulate data within Data Cloud.
* C. Sales and Service bundle. Sales and Service bundle is a feature of Data Cloud that allows pre-built data streams, data model objects, segments, and activations for Sales Cloud and Service Cloud data sources4. Sales and Service bundle does not integrate data in real time with Salesforce CRM, but rather ingests data at scheduled intervals.
* D. Data actions and Lightning web components. Data actions and Lightning web components are features of Data Cloud that allow custom user interfaces and workflows to be built and embedded in Salesforce applications5. Data actions and Lightning web components do not integrate data with Salesforce CRM, but rather display and interact with data within Salesforce applications.
References:
* 1: Load Data into Data Cloud
* 2: [Data Streams in Data Cloud]
* 3: [Data Model Triggers in Data Cloud] unit on Trailhead
* 4: [Sales and Service Bundle in Data Cloud] unit on Trailhead
* 5: [Data Actions and Lightning Web Components in Data Cloud] unit on Trailhead
* : [Data Model in Data Cloud] unit on Trailhead
* : [Create a Data Model Object] article on Salesforce Help
* : [Data Sources in Data Cloud] unit on Trailhead
* : [Connect and Ingest Data in Data Cloud] article on Salesforce Help
* : [Data Spaces in Data Cloud] unit on Trailhead
* : [Create a Data Space] article on Salesforce Help
* : [Segments in Data Cloud] unit on Trailhead
* : [Create a Segment] article on Salesforce Help
* : [Activations in Data Cloud] unit on Trailhead
* : [Create an Activation] article on Salesforce Help
質問 # 78
信頼ベースのファーストパーティ データ資産を構築するとはどういう意味ですか?
- A. データの使用に同意し、その対価として価値を受け取る個人から収集したデータの透明性とセキュリティを提供するため
- B. すべてのコンプライアンス規制に従って、信頼できるファーストパーティ データを Data Cloud Marketplace に提供するため
- C. インタビュー、調査、世論調査を通じて信頼できる情報源から競合データを入手するため
- D. 法律で義務付けられているすべての電子メール マーケティングに対してオプトイン同意が確実に収集されるようにするため
正解：A
解説：
Building a trust-based, first-party data asset means collecting, managing, and activating data from your own customers and prospects in a way that respects their privacy and preferences. It also means providing them with clear and honest information about how you use their data, what benefits they can expect from sharing their data, and how they can control their data. By doing so, you can create a mutually beneficial relationship with your customers, where they trust you to use their data responsibly and ethically, and you can deliver more relevant and personalized experiences to them. A trust-based, first-party data asset can help you improve customer loyalty, retention, and growth, as well as comply with data protection regulations and standards. References: Use first-party data for a powerful digital experience, Why first-party data is the key to data privacy, Build a first-party data strategy
質問 # 79
顧客は、統合率が最近増加していることに気づきました。彼らはコンサルタントに連絡して理由を尋ねます。
この増加について考えられる 2 つの説明は何ですか?
2 つの答えを選択してください
- A. ソース システム データ ストリームから重複が削除されました。
- B. 一致するプロファイルの数を減らすために、ID 解決ルールが削除されました。
- C. 一致するプロファイルの数を増やすために、ID 解決ルールがルールセットに追加されました。
- D. 既存のプロファイルと大部分が重複する新しいデータ ソースがデータ クラウドに追加されました。
正解：C、D
解説：
The consolidation rate is a metric that measures the amount by which source profiles are combined to produce unified profiles in Data Cloud, calculated as 1 - (number of unified profiles / number of source profiles). A higher consolidation rate means that more source profiles are matched and merged into fewer unified profiles, while a lower consolidation rate means that fewer source profiles are matched and more unified profiles are created. There are two likely explanations for why the consolidation rate has recently increased for a customer:
New data sources have been added to Data Cloud that largely overlap with the existing profiles. This means that the new data sources contain many profiles that are similar or identical to the profiles from the existing data sources. For example, if a customer adds a new CRM system that has the same customer records as their old CRM system, the new data source will overlap with the existing one.
When Data Cloud ingests the new data source, it will use the identity resolution ruleset to match and merge the overlapping profiles into unified profiles, resulting in a higher consolidation rate.
Identity resolution rules have been added to the ruleset to increase the number of matched profiles.
This means that the customer has modified their identity resolution ruleset to include more match rules or more match criteria that can identify more profiles as belonging to the same individual. For example, if a customer adds a match rule that matches profiles based on email address and phone number, instead of just email address, the ruleset will be able to match more profiles that have the same email address and phone number, resulting in a higher consolidation rate.
References: Identity Resolution Calculated Insight: Consolidation Rates for Unified Profiles, Configure Identity Resolution Rulesets
質問 # 80
Data Cloud はどのようにしてデータのプライバシーとセキュリティを確保しますか?
- A. オフサイトサーバーにデータを安全に保存することで
- B. 保存時および転送中のデータを暗号化することで
- C. 同意の参照を強制および制御することにより
- D. データアクセスを許可された管理者に制限することで
正解：B
質問 # 81
コンサルタントはエンゲージメントベースの関連属性を使用して最近のアクティベーションをレビューしていますが、セグメント メンバーの大部分のペイロードに関連属性が表示されません。
この問題のトラブルシューティングを行うためにコンサルタントが確認すべき 2 つの領域はどれですか?
2 つの答えを選択してください
- A. アクティベーションは、エンゲージメント データではなくプロファイル データに基づいてセグメント化されたセグメントを参照しています。
- B. 関連するエンゲージメント イベントは過去 90 日以内に発生しました。
- C. アクティブ化されたプロファイルには統合連絡先があります。
- D. 関連する属性に対して正しいパスが選択されています。
正解：B、D
解説：
Engagement-based related attributes are attributes that describe the interactions of a person with an email message, such as opens, clicks, unsubscribes, etc. These attributes are stored in the Engagement data model object (DMO) and can be added to an activation to send more personalized communications. However, there are some considerations and limitations when using engagement-based related attributes, such as:
* For engagement data, activation supports a 90-day lookback window. This means that only the attributes from the engagement events that occurred within the last 90 days are considered for activation. Any records outside of this window are not included in the activation payload. Therefore, the consultant should review the event time of the related engagement events and make sure they are within the lookback window.
* The correct path to the related attributes must be selected for the activation. A path is a sequence of DMOs that are connected by relationships in the data model. For example, the path from Individual to
* Engagement is Individual -> Email -> Engagement. The path determines which related attributes are available for activation and how they are filtered. Therefore, the consultant should review the path selection and make sure it matches the desired related attributes and filters.
The other two options are not relevant for this issue. The activations can reference segments that segment on profile data rather than engagement data, as long as the activation target supports related attributes. The activated profiles do not need to have a Unified Contact Point, which is a unique identifier for a person across different data sources, to activate engagement-based related attributes. References: Add Related Attributes to an Activation, Related Attributes in Data Cloud activation have no values, Explore the Engagement Data Model Object
質問 # 82
コンサルタントがセグメント エラーのトラブルシューティングを行っています。
ネストされたセグメントの代わりに計算されたインサイトを使用すると、どのエラー メッセージが解決されますか?
- A. 複数の人口カウントが進行中です。
- B. セグメントを公開できません。
- C. セグメントが複雑すぎます。
- D. セグメントのポピュレーションカウントに失敗しました。
正解：C
解説：
Segment Errors in Data Cloud: Segments in Salesforce Data Cloud can encounter errors due to various reasons, including complexity and nested segments.
Calculated Insights vs. Nested Segments:
* Complex Segments: If a segment is too complex due to extensive nesting or numerous conditions, it can lead to errors.
* Simplification with Calculated Insights: Using calculated insights can simplify segment creation by pre-computing and storing complex logic or aggregations, which can then be referenced directly in the segment.
Solution:
* Step 1: Identify the segment causing the "Segment is too complex" error.
* Step 2: Break down complex logic into calculated insights.
* Step 3: Use these calculated insights in segment definitions to reduce complexity.
References:
* Salesforce Data Cloud Calculated Insights
* Salesforce Data Cloud Segment Creation
質問 # 83
データ クラウド コンサルタントは最近、新しいデータ ソースを追加し、セグメントの作成に使用する新しいカスタム データ モデル オブジェクト (DMO) にデータの一部をマッピングしました。ただし、新しいセグメントを作成しようとすると、新しく作成された DMO を表示できません。
この問題の原因は何ですか?
- A. 新しい DMO は個々の DMO と関係がありません
- B. データは DMO に取り込まれていません。
- C. セグメンテーションは、個別 DMO および統合個別 DMO でのみサポートされます。
- D. 新しい DMO はプロファイル カテゴリではありません。
正解：D
解説：
The cause of this issue is that the new custom data model object (DMO) is not of category Profile. A category is a property of a DMO that defines its purpose and functionality in Data Cloud. There are three categories of DMOs: Profile, Event, and Other. Profile DMOs are used to store attributes of individuals or entities, such as name, email, address, etc. Event DMOs are used to store actions or interactions of individuals or entities, such as purchases, clicks, visits, etc. Other DMOs are used to store any other type of data that does not fit into the Profile or Event categories, such as products, locations, categories, etc. Only Profile DMOs can be used for creating segments in Data Cloud, as segments are based on the attributes of individuals or entities.
Therefore, if the new custom DMO is not of category Profile, it will not appear in the segmentation canvas.
The other options are not correct because they are not the cause of this issue. Data ingestion is not a prerequisite for creating segments, as segments can be created based on the data model schema without actual data. The new DMO does not need to have a relationship to the individual DMO, as segments can be created based on any Profile DMO, regardless of its relationship to other DMOs. Segmentation is not only supported for the Individual and Unified Individual DMOs, as segments can be created based on any Profile DMO, including custom ones. References: Create a Custom Data Model Object from an Existing Data Model Object, Create a Segment in Data Cloud, Data Model Object Category
質問 # 84
コンサルタントは、以前に黒のパンツを購入した顧客向けに新製品の発売を発表するセグメントを構築しています。
この基準を満たすために、コンサルタントは Order Product オブジェクトから製品の色と製品タイプの属性をどのように配置する必要がありますか?
- A. 製品および製品タイプの属性を直接属性として配置します。
- B. 製品の色の属性を 1 つのコンテナに配置し、製品タイプの属性を別のコンテナに配置します。
- C. 動的に適用する「黒」の計算された分析情報の属性を配置します。
- D. 製品の色と製品タイプの属性を 1 つのコンテナに配置します。
正解：D
解説：
To create a segment based on the product color and product type from the Order Product object, the consultant should place the attributes for product color and product type in a single container. This way, the segment will include only the customers who have purchased black pants, and not those who have purchased black shirts or blue pants. A container is a grouping of attributes that defines a segment of individuals based on a logical AND operation. Placing the attributes in separate containers would result in a segment that includes customers who have purchased any black product or any pants product, which is not the desired criteria. Placing an attribute for the "black" calculated insight would not work, because calculated insights are based on aggregated data and not individual-level data. Placing the attributes as direct attributes would not work, because direct attributes are used to filter individuals based on their profile data, not their order data. References:
* Create a Segment in Data Cloud
* Learn About Segmentation Tools
* Salesforce Launches: Data Cloud Consultant Certification
質問 # 85
データクラウドにおける人工知能 (AI) の役割は何ですか?
- A. 動的なデータ駆動型管理ダッシュボードの作成
- B. ユースケース用のメールテンプレートの生成
- C. 洞察と予測を通じて顧客とのやり取りを強化
- D. データ検証の自動化
正解：C
解説：
Role of AI in Data Cloud: Artificial intelligence (AI) plays a crucial role in Salesforce Data Cloud by leveraging data to generate insights and predictions that enhance customer interactions.
Insights and Predictions:
* AI Algorithms: Use machine learning algorithms to analyze vast amounts of customer data.
* Predictive Analytics: Provide predictive insights, such as customer behavior trends, preferences, and potential future actions.
Enhancing Customer Interactions:
* Personalization: AI helps in creating personalized experiences by predicting customer needs and preferences.
* Efficiency: Enables proactive customer service by predicting issues and suggesting solutions before customers reach out.
* Marketing: Improves targeting and segmentation, ensuring that marketing efforts are directed towards the most promising leads and customers.
Use Cases:
* Recommendation Engines: Suggest products or services based on past behavior and preferences.
* Churn Prediction: Identify customers at risk of leaving and engage them with retention strategies.
References:
* Salesforce Data Cloud AI Capabilities
* Salesforce AI for Customer Interaction
質問 # 86
コンサルタントは、Cloud File Storage ターゲットの命名規則に一致するように属性名を変更するにはどうすればよいでしょうか?
- A. 数式フィールドを使用して、アクティベーションのフィールド名を更新します。
- B. アクティベーションを構成するときに優先される属性名を設定します。
- C. データ モデル オブジェクトのフィールド名を更新します。
- D. データ ストリーム構成内の属性名を更新します。
正解：B
解説：
A Cloud File Storage target is a type of data action target in Data Cloud that allows sending data to a cloud storage service such as Amazon S3 or Google Cloud Storage. When configuring an activation to a Cloud File Storage target, a consultant can modify the attribute names to match a naming convention by setting preferred attribute names in Data Cloud. Preferred attribute names are aliases that can be used to control the field names in the target file. They can be set for each attribute in the activation configuration, and they will override the default field names from the data model object. The other options are incorrect because they do not affect the field names in the target file. Using a formula field to update the field name in an activation will not change the field name, but only the field value. Updating attribute names in the data stream configuration will not affect the existing data lake objects or data model objects. Updating field names in the data model object will change the field names for all data sources and activations that use the object, which may not be desirable or consistent. References: Preferred Attribute Name, Create a Data Cloud Activation Target, Cloud File Storage Target
質問 # 87
Cumulus Financial は、投資残高が高い顧客と呼ばれるセグメントを作成しました。これは、マーケティング チームが一貫して使用する必要があるいくつかのセグメンテーション基準を含む基本的なセグメントです。
コンサルタントは、将来のより洗練されたセグメントを作成するときに、この一貫性を確保するためにマーケティング チームにどの機能の使用を提案する必要がありますか?
- A. ネストされたセグメントを使用して新しいセグメントを作成します。
- B. 投資残高の高い顧客のクローンを作成して、新しいセグメントを作成します。
- C. 高投資残高の計算された分析情報を作成します。
- D. 投資残高の高い顧客をデータ キットにパッケージ化します。
正解：A
解説：
Nested segments are segments that include or exclude one or more existing segments. They allow the marketing team to reuse filters and maintain consistency in their data by using an existing segment to build a new one. For example, the marketing team can create a nested segment that includes High Investment Balance Customers and excludes customers who have opted out of email marketing. This way, they can leverage the foundational segment and apply additional criteria without duplicating the rules. The other options are not the best features to ensure consistency because:
* B. A calculated insight is a data object that performs calculations on data lake objects or CRM data and returns a result. It is not a segment and cannot be used for activation or personalization.
* C. A data kit is a bundle of packageable metadata that can be exported and imported across Data Cloud orgs. It is not a feature for creating segments, but rather for sharing components.
* D. Cloning a segment creates a copy of the segment with the same rules and filters. It does not allow the marketing team to add or remove criteria from the original segment, and it may create confusion and redundancy. References: Create a Nested Segment - Salesforce, Save Time with Nested Segments (Generally Available) - Salesforce, Calculated Insights - Salesforce, Create and Publish a Data Kit Unit | Salesforce Trailhead, Create a Segment in Data Cloud - Salesforce
質問 # 88
顧客は、CRM から Data Cloud に取り込むためのマスター顧客テーブルを持っています。このテーブルには、名前とプライマリ電子メール アドレスに加えて、その他の個人を特定できる情報 (Pll) が含まれています。
ID 解決をサポートするには、フィールドをどのようにマッピングする必要がありますか?
- A. すべてのフィールドを Individual オブジェクトにマップし、電子メール アドレスのカスタム フィールドを追加します。
- B. 名前を個人オブジェクトにマップし、電子メール アドレスを連絡先電話電子メール オブジェクトにマップします。
- C. すべてのフィールドを Customer オブジェクトにマップします。
- D. 受信テーブルと直接一致するフィールドを持つ新しいカスタム オブジェクトを作成します。
正解：B
解説：
To support identity resolution in Data Cloud, the fields from the Master Customer table should be mapped to the standard data model objects that are designed for this purpose. The Individual object is used to store the name and other personally identifiable information (PII) of a customer, while the Contact Phone Email object is used to store the primary email address and other contact information of a customer. These objects are linked by a relationship field that indicates the contact information belongs to the individual. By mapping the fields to these objects, Data Cloud can use the identity resolution rules to match and reconcile the profiles from different sources based on the name and email address fields. The other options are not recommended because they either create a new custom object that is not part of the standard data model, or map all fields to the Customer object that is not intended for identity resolution, or map all fields to the Individual object that does not have a standard email address field. References: Data Modeling Requirements for Identity Resolution, Create Unified Individual Profiles
質問 # 89
コンサルタントは顧客のデータクラウド組織で働いており、既存の ID 解決ルールセットを削除するように求められました。
このアクションの結果としてコンサルタントはどの 2 つの影響を伝える必要がありますか?
2 つの答えを選択してください
- A. すべての個別データが削除されます。
- B. このルールセットに関連付けられた統合顧客データは削除されます。
- C. データ モデル オブジェクトの依存関係が削除されます。
- D. すべてのソース プロファイル データが削除されます
正解：B、C
解説：
Deleting an identity resolution ruleset has two major impacts that the consultant should communicate to the customer. First, it will permanently remove all unified customer data that was created by the ruleset, meaning that the unified profiles and their attributes will no longer be available in Data Cloud1. Second, it will eliminate dependencies on data model objects that were used by the ruleset, meaning that the data model objects can be modified or deleted without affecting the ruleset1. These impacts can have significant consequences for the customer's data quality, segmentation, activation, and analytics, so the consultant should advise the customer to carefully consider the implications of deleting a ruleset before proceeding. The other options are incorrect because they are not impacts of deleting a ruleset. Option A is incorrect because deleting a ruleset will not remove all individual data, but only the unified customer data. The individual data from the source systems will still be available in Data Cloud1. Option D is incorrect because deleting a ruleset will not remove all source profile data, but only the unified customer data. The source profile data from the data streams will still be available in Data Cloud1. References: Delete an Identity Resolution Ruleset
質問 # 90
製品ファミリーごとに機会の収益または数量を定義するデータ モデルのサブジェクト領域はどれですか?
- A. パーティー
- B. エンゲージメント
- C. 製品
- D. 販売注文
正解：D
解説：
The Sales Order subject area defines the details of an order placed by a customer for one or more products or services. It includes information such as the order date, status, amount, quantity, currency, payment method, and delivery method. The Sales Order subject area also allows you to track the revenue or quantity for an opportunity by product family, which is a grouping of products that share common characteristics or features.
For example, you can use the Sales Order Line Item DMO to associate each product in an order with its product family, and then use the Sales Order Revenue DMO to calculate the total revenue or quantity for each product family in an opportunity. References: Sales Order Subject Area, Sales Order Revenue DMO Reference
質問 # 91
ID 解決で「空の値を無視」オプションは何をしますか?
- A. 調整ルールの実行時に空のフィールドを無視します
- B. ID 解決ルールの実行時に、フィールドが空の個々のオブジェクト レコードを無視します。
- C. 標準の一致ルールを実行するときに空のフィールドを無視します。
- D. カスタム一致ルールの実行時に空のフィールドを無視します。
正解：A
解説：
The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.
The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.
References:
* Data Cloud Identity Resolution Reconciliation Rule Input
* Configure Identity Resolution Rulesets
* Data and Identity in Data Cloud
質問 # 92
高級品小売業者は、電子メール通信のために Marketing Cloud を通じて有効化した、価値の高い顧客をターゲットとしたセグメントを作成しました。同社は、アクティブ化された数がセグメント数よりも小さいことに気づきました。
その理由は何でしょうか?
- A. Data Cloud は、Marketing Cloud のアクティベーションに対してコンタクト ポイントの存在を強制します。個人が関連する連絡先を持っていない場合、連絡先は有効になりません。
- B. Marketing Cloud のアクティベーションは、エンゲージメントを持たず、過去 6 か月間メールを開いたりクリックしたりしていない個人を自動的に抑制します。
- C. Marketing Cloud アクティベーションではフリークエンシー キャップが適用され、アクティベーションで送信できるレコードの数が制限されます。
- D. Marketing Cloud のアクティベーションは、Marketing Cloud にすでに存在する個人のみをアクティベートします。新しいレコードのアクティブ化は許可されません。
正解：A
解説：
The reason for the activated count being smaller than the segment count is A. Data Cloud enforces the presence of Contact Point for Marketing Cloud activations. If the individual does not have a related Contact Point, it will not be activated. A Contact Point is a data model object that represents a channel or method of communication with an individual, such as email, phone, or social media. For Marketing Cloud activations, Data Cloud requires that the individual has a related Contact Point of type Email, which contains a valid email address. If the individual does not have such a Contact Point, or if the Contact Point is missing or invalid, the individual will not be activated and will not receive the email communication. Therefore, the activated count may be lower than the segment count, depending on how many individuals in the segment have a valid email Contact Point. References: Salesforce Data Cloud Consultant Exam Guide, Contact Point, Marketing Cloud Activation
質問 # 93
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