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| Section | Objectives |
|---|---|
| Data Integration | - API-based integration approaches - ETL vs ELT strategies - Integration patterns in Salesforce |
| Data Management | - Data governance principles - Master data management (MDM) concepts - Data quality management |
| Salesforce Data Model and Platform Capabilities | - Relationships and schema design - Reporting and analytics considerations - Standard and custom objects |
| Data Architecture and Design | - Data modeling best practices in Salesforce - Conceptual and logical data modeling - Handling large data volume and scalability considerations |
| Data Migration | - Data migration strategies and planning - Data cleansing and transformation |
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NEW QUESTION # 20
To avoid creating duplicate Contacts, a customer frequently uses Data Loader to upsert Contact records into Salesforce. What common error should the data architect be aware of when using upsert?
Answer: C
Explanation:
Data Loader uses external Id fields to match records in the CSV file with records in Salesforce during an upsert operation. If the CSV file contains duplicate external Id values within the same file, Data Loader will throw an error saying "Duplicate Id Specified" and will not process those records. Therefore, it is important to ensure that the CSV file does not have any duplicate external Id values before using Data Loader to upsert records.
NEW QUESTION # 21
DreamHouse Realty has a Salesforce org that is used to manage Contacts. What are two things an Architect should consider using to maintain data quality in this situation? (Choose two.)
Answer: B,D
Explanation:
Using Salesforce duplicate management and using validation rules on new record create and edit are two things that an architect should consider using to maintain data quality for managing Contacts. Salesforce duplicate management allows the architect to create matching rules and duplicate rules to identify, prevent, or allow duplicate records based on various criteria. Validation rules allow the architect to enforce data quality standards and business logic by displaying error messages when users try to save invalid data. The other options are not relevant or helpful for maintaining data quality
NEW QUESTION # 22
Universal Containers has more than 10 million records in the Order_c object. The query has timed out when running a bulk query. What should be considered to resolve query timeout?
Answer: A
Explanation:
PK Chunking can resolve query timeout when running a bulk query on an object with more than
10 million records. PK Chunking is a feature of the Bulk API that splits a query into multiple batches based on the record IDs (primary keys) of the queried object. This can improve the query performance and avoid timeouts by reducing the number of records processed in each batch.
NEW QUESTION # 23
Universal Containers (UC) provides shipping services to its customers. They use Opportunities to track customer shipments. At any given time, shipping status can be one of the 10 values. UC has 200,000 Opportunity records. When creating a new field to track shipping status on opportunity, what should the architect do to improve data quality and avoid data skew?
Answer: D
Explanation:
To improve data quality and avoid data skew, the data architect should create a picklist field with values sorted alphabetically for tracking shipping status on opportunity. A picklist field ensures that only valid values are entered and prevents typos or variations in spelling. Sorting the values alphabetically makes it easier for users to find and select the correct value. Data skew occurs when a large number of records are owned by a single user or have a single value for a field.
Creating a picklist field with a limited number of values does not cause data skew, as long as the distribution of values is balanced and not skewed towards one value.
NEW QUESTION # 24
North Trail Outfitters (NTO) operates a majority of its business from a central Salesforce org, NTO also owns several secondary orgs that the service, finance, and marketing teams work out of, At the moment, there is no integration between central and secondary orgs, leading to data- visibility issues.
Moving forward, NTO has identified that a hub-and-spoke model is the proper architect to manage its data, where the central org is the hub and the secondary orgs are the spokes.
Which tool should a data architect use to orchestrate data between the hub org and spoke orgs?
Answer: C
Explanation:
According to the Salesforce documentation, a hub-and-spoke model is an integration architecture pattern that allows connecting multiple Salesforce orgs using a central org (hub) and one or more secondary orgs (spokes). The hub org acts as the master data source and orchestrates the data flow between the spoke orgs. The spoke orgs act as the consumers or producers of the data and communicate with the hub org.
To orchestrate data between the hub org and spoke orgs, a data architect should use:
A middleware solution that extracts and distributes data across both the hub and spokes (option A). This means using an external service or tool that can connect to multiple Salesforce orgs using APIs or connectors, and perform data extraction, transformation, and distribution operations between the hub and spoke orgs. This can provide a scalable, flexible, and reliable way to orchestrate data across multiple orgs.
NEW QUESTION # 25
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