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| Section | Weight | Objectives |
|---|---|---|
| Data Modeling | 25% | - Indexing strategies - Big Objects and external objects - Large Data Volumes (LDV) design considerations - Skinny tables for performance optimization - Normalization and denormalization strategies |
| Data Governance | 20% | - Compliance and regulatory considerations - Data quality standards and metrics - Data stewardship roles and responsibilities - Data retention and archiving policies |
| Data Architecture | 20% | - Performance tuning and optimization - Architecture patterns and design principles - Governor limits and bulk processing - Archiving and data lifecycle management |
| Master Data Management | 20% | - Data duplication and matching rules - Data cleansing techniques - Identity resolution |
| Data Integration | 15% | - Handling locks and parallelism during migration - Sharing rules impact on data integration - Real-time vs batch integration patterns - ETL tools and data migration strategies - API-based integration (REST, SOAP, Bulk API) |
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NEW QUESTION # 134
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: C
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 # 135
UC has a roll-up summary field on Account to calculate the count of contacts associated with an account. During the account load, SF is throwing an "Unable to lock a row" error.
Which solution should a data architect recommend, to resolve the error?
Answer: D
NEW QUESTION # 136
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: A,B,D
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 # 137
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,C
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 # 138
UC recently migrated 1 Billion customer related records from a legacy data store to Heroku Postgres. A subset of the data need to be synchronized with salesforce so that service agents are able to support customers directly within the service console. The remaining non- synchronized set of data will need to be accessed by salesforce at any point in time, but UC management is concerned about storage limitations.
What should a data architect recommend to meet these requirements with minimal effort?
Answer: B
Explanation:
Virtualizing the remaining set of data with salesforce connect and external objects is the best way to meet the requirements with minimal effort, as it allows salesforce to access data stored in Heroku Postgres without storing it in salesforce. This reduces the storage limitations and avoids data duplication. Heroku connect can bi-directionally sync data between systems, but it requires more configuration and maintenance. Making callouts to Heroku Postgres and persisting the data in salesforce may not be feasible for 1 billion records. Migrating the data to big objects may incur additional costs and require custom code to use async SOQL
NEW QUESTION # 139
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