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| Section | Weight | Objectives |
|---|---|---|
| Data Governance | 10% | - Describe the considerations for data quality - Describe the considerations for data classification - Describe the elements of a data governance framework |
| Large Data Volumes | 20% | - Describe the implications of LDV on data modeling - Describe the features and best practices for LDV - Describe the considerations for Large Data Volumes (LDV) |
| Salesforce Data Management | 25% | - Describe the implications of LDV on data management - Describe the considerations for data migration - Describe the considerations for data governance - Describe the considerations for data archival |
| Master Data Management | 5% | - Describe the methods for aligning data across systems - Describe the considerations for a Master Data Management strategy |
| Data Modeling/Database Design | 25% | - Describe the options and considerations for Large Data Volumes (LDV) - Describe the implications of LDV on data modeling - Explain the usage of external objects - Describe the considerations when designing a data model for Salesforce |
| Data Migration | 15% | - Describe the considerations for data migration - Describe the tools and best practices for data migration - Describe the implications of data migration on data quality |
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NEW QUESTION # 35
Which field is automatically indexed?
Answer: C
Explanation:
Salesforce automatically indexes primary keys.
NEW QUESTION # 36
Universal Containers (UC) is launching an RFP to acquire a new accounting product available on AppExchange. UC is expecting to issue 5 million invoices per year, with each invoice containing an average of 10 line items. What should UC's Data Architect recommend to ensure scalability?
Answer: C
Explanation:
A sound data archiving strategy is essential for ensuring scalability and performance of any application that deals with large volumes of data. You need to consider factors like data retention policies, storage limits, backup and recovery options, and data access requirements when designing a data archiving solution. You can use tools like OwnBackup, Odaseva, or ArchiveIt to archive your data from Salesforce to external systems
NEW QUESTION # 37
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 # 38
Universal Containers (UC) has several custom Visualforce applications have been developed in which users are able to edit Opportunity records. UC struggles with data completeness on their Opportunity records and has decided to make certain fields required that have not been in the past. The newly required fields are dependent on the Stage of the Opportunity, such that certain fields are only required once an Opportunity advances to later stages. There are two fields. What is the simplest approach to handle this new requirement?
Answer: A
Explanation:
Using a validation rule for each field that takes the Stage into consideration is the simplest approach to handle this new requirement. A validation rule can enforce the field requirements based on the logic and criteria that you define, and display an error message when users try to save a record that does not meet the requirements. Updating the Opportunity page layout to mark these fields as required will not work because page layouts do not support conditional field requirements. Updating these Opportunity field definitions in Setup to be required will not work because it will apply to all stages and records. Writing an Apex trigger that checks each field when records are saved is not the simplest approach because it requires coding and testing
NEW QUESTION # 39
Universal Containers (UC) has implemented Salesforce, UC is running out of storage and needs to have an archiving solution, UC would like to maintain two years of data in Saleforce and archive older data out of Salesforce. Which solution should a data architect recommend as an archiving solution?
Answer: B
Explanation:
The data architect should recommend building a batch job to move two-year-old records off platform, and delete records from Salesforce as an archiving solution. A batch job is a process that runs in the background and performs operations on large volumes of data in Salesforce. By building a batch job that moves two-year-old records off platform to an external storage system, such as Amazon S3 or Google Cloud Storage, and deletes them from Salesforce, the data architect can reduce the storage consumption and improve the performance of Salesforce org.
NEW QUESTION # 40
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