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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Data Cloud Consultant |
| Exam Number: | Data-Con-101 |
| Exam Duration: | 105 minutes |
| Exam Price: | USD 200 |
| Available Languages: | English |
| Related Certifications: | Salesforce Certified AI Associate |
| Real Exam Qty: | 60 (plus up to 5 unscored questions) |
| Passing Score: | 62% |
| Exam Format: | Multiple-select, Multiple-choice |
| Certificate Validity Period: | Maintenance required annually (Trailhead modules) |
| Sample Questions: | Salesforce Data-Con-101 Sample Questions |
| Exam Way: | Online (Webassessor/PSI) or Onsite (Pearson VUE) |
| Pre Condition: | No hard prerequisites; recommended 2+ years of experience in data strategy and data modeling |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials/datacloudconsultant |
>> Data-Con-101 Prüfungsfrage <<
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10. Frage
What is a reason to create a formula when ingesting a data stream?
Antwort: C
Begründung:
Creating a formula during data stream ingestion is often done to manipulate or transform data fields to meet specific requirements. In this case, the most common reason is to transform a date-time field into a date field for use in data mapping . Here's why:
Understanding the Requirement
When ingesting data into Salesforce Data Cloud, certain fields may need to be transformed to align with the target data model.
For example, a date-time field (e.g., "2023-10-05T14:30:00Z") may need to be converted into a date field (e.
g., "2023-10-05") for proper mapping and analysis.
Why Transform a Date-Time Field into a Date Field?
Data Mapping Compatibility :
Some data models or downstream systems may only accept date fields (without the time component).
Transforming the field ensures compatibility and avoids errors during ingestion or activation.
Simplified Analysis :
Removing the time component simplifies analysis and reporting, especially when working with daily trends or aggregations.
Standardization :
Converting date-time fields into consistent date formats ensures uniformity across datasets.
Steps to Implement This Solution
Step 1: Identify the Date-Time Field
During the data stream setup, identify the field that contains the date-time value (e.g., "Order_Date_Time").
Step 2: Create a Formula Field
Use the Formula Field option in the data stream configuration to create a new field.
Apply a transformation function (e.g., DATE() or equivalent) to extract the date portion from the date-time field.
Step 3: Map the Transformed Field
Map the newly created date field to the corresponding field in the target data model (e.g., Unified Profile or Data Lake Object).
Step 4: Validate the Transformation
Test the data stream to ensure the transformation works correctly and the date field is properly ingested.
Why Not Other Options?
A). To concatenate files so they are ingested in the correct sequence :Concatenation is not a typical use case for formulas during ingestion. File sequencing is usually handled at the file ingestion level, not through formulas.
B). To add a unique external identifier to an existing ruleset :Adding a unique identifier is typically done during data preparation or identity resolution, not through formulas during ingestion.
D). To remove duplicate rows of data from the data stream :Removing duplicates is better handled through deduplication rules or transformations, not formulas.
Conclusion
The primary reason to create a formula when ingesting a data stream is to transform a date-time field into a date field for use in data mapping . This ensures compatibility, simplifies analysis, and standardizes the data for downstream use.
11. Frage
Which two requirements must be met for a calculated insight to appear in the segmentation canvas?
Choose 2 answers
Antwort: A,D
Begründung:
A calculated insight is a custom metric or measure that is derived from one or more data model objects or data lake objects in Data Cloud. A calculated insight can be used in segmentation to filter or group the data based on the calculated value. However, not all calculated insights can appear in the segmentation canvas. There are two requirements that must be met for a calculated insight to appear in the segmentation canvas:
The calculated insight must contain a dimension including the Individual or Unified Individual Id. A dimension is a field that can be used to categorize or group the data, such as name, gender, or location. The Individual or Unified Individual Id is a unique identifier for each individual profile in Data Cloud. The calculated insight must include this dimension to link the calculated value to the individual profile and to enable segmentation based on the individual profile attributes.
The primary key of the segmented table must be a dimension in the calculated insight. The primary key is a field that uniquely identifies each record in a table. The segmented table is the table that contains the data that is being segmented, such as the Customer or the Order table. The calculated insight must include the primary key of the segmented table as a dimension to ensure that the calculated value is associated with the correct record in the segmented table and to avoid duplication or inconsistency in the segmentation results.
Create a Calculated Insight, Use Insights in Data Cloud, Segmentation
12. Frage
A consultant is reviewing a recent activation using engagement-based related attributes but is not seeing any related attributes in their payload for the majority of their segment members.
Which two areas should the consultant review to help troubleshoot this issue?
Choose 2 answers
Antwort: C,D
Begründung:
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
13. Frage
A customer needs to integrate in real time with Salesforce CRM.
Which feature accomplishes this requirement?
Antwort: A
Begründung:
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.
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
14. Frage
During discovery, which feature should a consultant highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile?
Antwort: B
Begründung:
The feature that the consultant should highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile is D. Identity Resolution. Identity Resolution is the process of identifying, matching, and reconciling data about individuals across different data sources and creating a unified profile that represents a single view of the customer. Identity Resolution uses various methods and rules to determine the best match and reconciliation of data, such as deterministic matching, probabilistic matching, reconciliation rules, and identity graphs. Identity Resolution enables the customer to have a complete and accurate understanding of their customers and their interactions across different channels and touchpoints. References: Salesforce Data Cloud Consultant Exam Guide, Identity Resolution
15. Frage
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