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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Architecture | 20% | - Architecture patterns and design principles - Performance tuning and optimization - Archiving and data lifecycle management - Governor limits and bulk processing |
| Topic 2: Master Data Management | 20% | - Identity resolution - Data cleansing techniques - Data duplication and matching rules |
| Topic 3: Data Modeling | 25% | - Skinny tables for performance optimization - Big Objects and external objects - Large Data Volumes (LDV) design considerations - Indexing strategies - Normalization and denormalization strategies |
| Topic 4: Data Integration | 15% | - ETL tools and data migration strategies - Sharing rules impact on data integration - Real-time vs batch integration patterns - Handling locks and parallelism during migration - API-based integration (REST, SOAP, Bulk API) |
| Topic 5: Data Governance | 20% | - Data quality standards and metrics - Compliance and regulatory considerations - Data stewardship roles and responsibilities - Data retention and archiving policies |
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NEW QUESTION # 36
UC has millions of case records with case history and SLA data. UC's compliance team would like historical cases to be accessible for 10 years for Audit purpose.
What solution should a data architect recommend?
Answer: D
Explanation:
The best solution to store historical cases for 10 years for audit purpose is to use a custom Big object to store archived case data. Big objects are a type of custom object that can store massive amounts of data on the Salesforce platform without affecting performance or storage limits. They also support point-and-click tools, triggers, and Apex code. Big objects can be used for archiving historical data that needs to be retained for compliance or analytics purposes3. Archiving case data using Salesforce Archiving process is not a good option because it only supports archiving cases that are closed for more than one year, and it does not allow customizing the archival criteria or accessing the archived data via Apex or APIs4. Purchasing more data storage to support case object is expensive and may impact performance. Using a custom object to store archived case data is not scalable and may consume a lot of storage space.
NEW QUESTION # 37
Universal Container (US) is replacing a home grown CRM solution with Salesforce, UC has decided to migrate operational (Open and active) records to Salesforce, while keeping historical records in legacy system, UC would like historical records to be available in Salesforce on an as needed basis.
Which solution should a data architect recommend to meet business requirement?
Answer: B
NEW QUESTION # 38
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: D
NEW QUESTION # 39
NTO need to extract 50 million records from a custom object everyday from its Salesforce org. NTO is facing query timeout issues while extracting these records.
What should a data architect recommend in order to get around the time out issue?
Answer: D
Explanation:
The best solution to extract 50 million records from a custom object everyday from Salesforce org without facing query timeout issues is to use an ETL tool for extraction of records. ETL stands for extract, transform, and load, and it refers to a process of moving data from one system to another. An ETL tool is a software application that can connect to various data sources, perform data transformations, and load data into a target destination. ETL tools can handle large volumes of data efficiently and reliably, and they often provide features such as scheduling, monitoring, error handling, and logging5. Using a custom auto number and formula field and use that to chunk records while extracting data is a possible workaround, but it requires creating additional fields and writing complex queries. The REST API can extract data as it automatically chunks records by 200, but it has some limitations, such as a maximum of 50 million records per query job6. Asking SF support to increase the query timeout value is not feasible because query timeout values are not configurable
NEW QUESTION # 40
US has released a new disaster recovery (DR)policy that states that cloud solutions need a business continuity plan in place separate from the cloud providers built in data recovery solution.
Which solution should a data architect use to comply with the DR policy?
Answer: A
NEW QUESTION # 41
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