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| Section | Objectives |
|---|---|
| Topic 1: Data Migration | - Data migration strategies and planning - Data cleansing and transformation |
| Topic 2: Salesforce Data Model and Platform Capabilities | - Reporting and analytics considerations - Relationships and schema design - Standard and custom objects |
| Topic 3: Data Management | - Data governance principles - Data quality management - Master data management (MDM) concepts |
| Topic 4: Data Integration | - API-based integration approaches - Integration patterns in Salesforce - ETL vs ELT strategies |
| Topic 5: Data Architecture and Design | - Data modeling best practices in Salesforce - Conceptual and logical data modeling - Handling large data volume and scalability considerations |
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NEW QUESTION # 253
Universal Containers (UC) has a very large and complex Salesforce org with hundreds of validation rules and triggers. The triggers are responsible for system updates and data manipulation as records are created or updates by users. A majority of the automation tool within UC'' org were not designed to run during a data load. UC is importing 100,000 records into Salesforce across several objects over the weekend. What should a data architect do to mitigate any unwanted results during the import?
Answer: A
Explanation:
Ensuring validation rules, triggers and other automation tools are disabled is the best way to mitigate any unwanted results during the import, as it prevents any errors or conflicts that may occur due to the existing logic. Ensuring duplication and matching rules are defined may not be sufficient or relevant for preventing unwanted results. Importing the data in smaller batches over a
24-hour period may not be necessary or efficient. Bulkifying the trigger to handle import leads may not be possible or desirable if the triggers were not designed to run during a data load.
NEW QUESTION # 254
Universal Containers has received complaints that customers are being called by multiple Sales Reps where the second Sales Rep that calls is unaware of the previous call by their coworker.
What is a data quality problem that could cause this?
Answer: B
Explanation:
A data quality problem that could cause customers to be called by multiple Sales Reps is having duplicate Contact records in the system. Duplicate records can result from data entry errors, data imports, or integrations with other systems. Duplicate records can lead to confusion, inefficiency, and customer dissatisfaction.
NEW QUESTION # 255
Universal Containers (UC) wants to capture information on how data entities are stored within the different applications and systems used within the company. For that purpose, the architecture team decided to create a data dictionary covering the main business domains within UC. Which two common techniques are used building a data dictionary to store information on how business entities are defined?
Answer: C,D
Explanation:
A data dictionary is a document that describes the structure, format, and meaning of data entities and attributes. A common technique to build a data dictionary is to use an entity relationship diagram (ERD), which shows the logical relationships between objects and fields in a graphical way. Another technique is to use the Salesforce Metadata API, which allows you to retrieve and deploy the metadata that defines your Salesforce org
NEW QUESTION # 256
UC is trying to switch from legacy CRM to salesforce and wants to keep legacy CRM and salesforce in place till all the functionality is deployed in salesforce. The want to keep data in synch b/w Salesforce, legacy CRM and SAP. What is the recommendation?
Answer: B,C
Explanation:
Integrate SAP with Salesforce, SAP to legacy CRM but not legacy CRM to Salesforce. This is a good recommendation because it allows both Salesforce and the legacy CRM to interact with the ERP system, which can be the source of truth for some data entities. However, it avoids creating a direct connection between Salesforce and the legacy CRM, which can cause data duplication and synchronization issues. Suggest MDM solution and link MDM to salesforce and SAP. This is another good recommendation because it can help manage the data lifecycle and quality across multiple systems, as well as provide a single view of the customer data
NEW QUESTION # 257
Universal Containers (UC) is a major supplier of office supplies. Some products are produced by UC and some by other manufacturers. Recently, a number of customers have complained that product descriptions on the invoices do not match the descriptions in the online catalog and on some of the order confirmations (e.g., "ballpoint pen" in the catalog and "pen" on the invoice, and item color labels are inconsistent: "what vs. "White" or "blk" vs. "Black"). All product data is consolidated in the company data warehouse and pushed to Salesforce to generate quotes and invoices. The online catalog and webshop is a Salesforce Customer Community solution. What is a correct technique UC should use to solve the data inconsistency?
Answer: D
Explanation:
A correct technique UC should use to solve the data inconsistency is to define a data taxonomy for product data and apply the taxonomy to the product data in the data warehouse. A data taxonomy is a hierarchical classification of data entities and attributes that defines their meaning, format, and relationships. A data taxonomy can help ensure consistency, accuracy, and completeness of product data across different systems and channels
NEW QUESTION # 258
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