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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling | 25% | - Skinny tables for performance optimization - Normalization and denormalization strategies - Large Data Volumes (LDV) design considerations - Indexing strategies - Big Objects and external objects |
| Topic 2: Master Data Management | 20% | - Data cleansing techniques - Identity resolution - Data duplication and matching rules |
| Topic 3: Data Integration | 15% | - Real-time vs batch integration patterns - API-based integration (REST, SOAP, Bulk API) - Handling locks and parallelism during migration - Sharing rules impact on data integration - ETL tools and data migration strategies |
| Topic 4: Data Architecture | 20% | - Architecture patterns and design principles - Performance tuning and optimization - Governor limits and bulk processing - Archiving and data lifecycle management |
| Topic 5: Data Governance | 20% | - Data quality standards and metrics - Data stewardship roles and responsibilities - Compliance and regulatory considerations - Data retention and archiving policies |
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NEW QUESTION # 22
Universal Containers (UC) manages Vehicle and Service History in Salesforce. Vehicle (Vehicle__ c) and Service History (Service-History__ c) are both custom objects related through a lookup relationship.
Every week a batch synchronization process updates the Vehicle and Service History records in Salesforce. UC has two hours of migration window every week and is facing locking issues as part of the data migration process.
What should a data architect recommend to avoid locking issues without affecting performance of data migration?
Answer: B
Explanation:
According to the official Salesforce guide1, using Bulk API serial mode for data migration can help avoid locking issues by processing batches in a single thread. This mode ensures that batches are processed in the order they are received and that only one batch is processed at a time. This reduces the risk of lock contention and deadlocks, especially when updating parent and child records in a lookup relationship. Option B is the correct answer because it suggests using Bulk API serial mode for data migration. Option A is incorrect because using Bulk API parallel mode for data migration can cause locking issues by processing batches in multiple threads. This mode does not guarantee the order of batch processing and can result in concurrent updates to the same records, which can lead to lock contention and deadlocks. Option C is incorrect because inserting the order in another custom object and using Batch Apex to move the records to Service_Order__c object adds unnecessary complexity and overhead to the data migration process. Option D is incorrect because changing the lookup configuration to "Clear the value of this field" when lookup record is deleted does not address the locking issues caused by data migration, but rather by record deletion.
NEW QUESTION # 23
Universal Containers (UC) is implementing a new customer categorization process where customers should be assigned to a Gold, Silver, or Bronze category if they've purchased UC's new support service. Customers are expected to be evenly distributed across all three categories. Currently, UC has around 500,000 customers, and is expecting 1% of existing non-categorized customers to purchase UC's new support service every month over the next five years. What is the recommended solution to ensure long-term performance, bearing in mind the above requirements?
Answer: A
Explanation:
The recommended solution to ensure long-term performance for the customer categorization process is to implement a new picklist custom field in the Account object with Gold, Silver, and Bronze values. A picklist field is a simple and efficient way to store a predefined set of values for a record. A picklist field has the following benefits:
It allows you to easily filter, sort, and group records by their category values, which can help you analyze and segment your customers.
It does not require creating any additional objects or relationships, which can reduce the data model complexity and maintenance overhead.
It supports large data volumes and can handle millions of records without affecting performance or scalability.
NEW QUESTION # 24
NTO has decided that it is going to build a channel sales portal with the following requirements:
1. External resellers are able to authenticate to the portal with a login.
2. Lead data, opportunity data and order data are available to authenticated users.
3. Authenticated users many need to run reports and dashboards.
4. There is no need for more than 10 custom objects or additional file storage.
Which community cloud license type should a data architect recommend to meet the portal requirements?
Answer: A
NEW QUESTION # 25
Universal Containers has implemented Salesforce for its operations. In order for customers to be created in their MDM solution, the customer record needs to have the following attributes:
1. First Name
2. Last Name
3. Email
Which option should the data architect recommend to mandate this when customers are created in Salesforce?
Answer: A
NEW QUESTION # 26
NTO (Northern Trail Outlets) has a complex Salesforce org which has been developed over past 5 years.
Internal users are complaining abt multiple data issues, including incomplete and duplicate data in the org.
NTO has decided to engage a data architect to analyze and define data quality standards.
Which 3 key factors should a data architect consider while defining data quality standards? Choose 3 answers:
Answer: B,D,E
Explanation:
Defining data duplication standards and rules, measuring data timeliness and consistency, and measuring data completeness and accuracy are three key factors that a data architect should consider while defining data quality standards. Defining data duplication standards and rules can help prevent or reduce duplicate records in the org by specifying criteria and actions for identifying and merging duplicates. Measuring data timeliness and consistency can help ensure that the data is up-to-date, reliable, and synchronized across different sources.
Measuring data completeness and accuracy can help ensure that the data is sufficient, relevant, and correct for the intended purposes.
NEW QUESTION # 27
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