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Salesforce Data-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Salesforce Data Management	25%	- Data import and export strategies 1. Migration planning and execution 2. Tools: Data Loader, Import Wizard, Bulk API 3. Data validation and cleansing - Sharing and visibility design 1. Sharing rules, manual sharing, Apex sharing 2. Organization-wide defaults, role hierarchy 3. Data access and security model
Topic 2: Data Modeling and Database Design	25%	- Design scalable data models 1. Data model optimization for performance 2. Object relationships and cardinality 3. Custom and standard object design - Indexing and data structure 1. Skinny tables and big objects 2. Standard and custom indexes 3. Query optimization strategies
Topic 3: Large Data Volume Considerations	20%	- Performance and scalability 1. Handling millions of records 2. Avoiding and resolving data skew 3. Governor limits and bulk processing - Storage and optimization 1. Performance tuning techniques 2. Storage limits and management 3. Archiving and off-platform storage
Topic 4: Data Governance and Compliance	20%	- Data governance framework 1. Data stewardship and ownership 2. Data quality standards 3. Compliance and regulatory requirements - Data lifecycle management 1. Data classification and handling 2. Data retention and archiving policies 3. Data privacy and security
Topic 5: Data Integration and Master Data Management	10%	- Master data management 1. Single source of truth design 2. Data synchronization and consistency 3. MDM strategy and implementation - Integration patterns and tools 1. External data sources and objects 2. Real-time vs batch integration 3. REST, SOAP, and Bulk API usage

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Salesforce Certified Data Architect Sample Questions (Q169-Q174):

NEW QUESTION # 169
A company has 12 million records, and a nightly integration queries these records.
Which two areas should a Data Architect investigate during troubleshooting if queries are timing out? (Choose two.)

A. Create custom indexes on the fields used in the filter criteria.
B. Create a formula field instead of having multiple filter criteria.
C. Make sure the query doesn't contain NULL in any filter criteria.
D. Modify the integration users' profile to have View All Data.

Answer: A,C

NEW QUESTION # 170
Every year, Ursa Major Solar has more than 1 million orders. Each order contains an average of 10 line items. The Chief Executive Officer (CEO) needs the Sales Reps to see how much money each customer generates year-over-year. However, data storage is running low in Salesforce.
Which approach for data archiving is appropriate for this scenario?

A. 1. Annually export and delete orders and order line items. 2. Store them in a zip file in case the data is needed later.
B. 1. Annually delete orders and order line items. 2. Ensure the customer has order information in another system.
C. 1. Annually export and delete order line items. 2. Store them in a zip file in case the data is needed later.
D. 1. Annually aggregate order amount data to store in a custom object. 2. Delete those orders and order line items.

Answer: D

Explanation:
Option B is the most appropriate approach for data archiving in this scenario1. By aggregating order amount data at a summary level and storing it in a custom object, you can reduce data storage and still provide visibility to the Sales Reps on how much money each customer generates year-over-year. Option A is not correct because storing data in a zip file does not make it available in Salesforce. Option C is not correct because exporting and deleting orders and order line items may lose important details that are needed for analysis or reporting. Option D is not correct because deleting orders and order line items without exporting them may cause data loss or inconsistency if the customer does not have order information in another system.

NEW QUESTION # 171
Northern Trail Outfitters <NTO> is streaming IoT data from connected devices to a cloud database. Every 24 hours. 100,000 records are generated.
NIO employees will need to see these lol records within Salesforce and generate weekly reports on it.
Developers may also need to write programmatic logic to aggregate the records and incorporate them into workflows.
Which data pattern will allow a data architect to satisfy these requirements, while also keeping limits in mind?

A. Persistence
B. Bidirectional integration
C. Unidirectional integration
D. Virtualization

Answer: A

Explanation:
Persistence is the data pattern that will allow a data architect to satisfy the requirements, while also keeping limits in mind. Persistence means storing data from external sources in Salesforce objects, either standard or custom. This allows you to access the data within Salesforce and use it for reporting, analytics, workflows, and other features. Persistence also helps you avoid hitting API limits or performance issues when accessing large volumes of data from external systems. You can use various tools such as Data Loader, Bulk API, or Platform Events to persist IoT data from connected devices to a cloud database in Salesforce.

NEW QUESTION # 172
Northern Trail Outfitters Is planning to build a consent form to record customer authorization for marketing purposes.
What should a data architect recommend to fulfill this requirement?

A. Use AppExchange solution to address the requirement.
B. Create a custom object to maintain the authorization.
C. Use custom fields to capture the authorization details.
D. Utilize the Authorization Form Consent object to capture the consent.

Answer: D

NEW QUESTION # 173
UC has been using SF for 10 years. Lately, users have noticed, that the pages load slowly when viewing Customer and Account list view.
To mitigate, UC will implement a data archive strategy to reduce the amount of data actively loaded.
Which 2 tasks are required to define the strategy? Choose 2 answers:

A. Identify the data retention requirements
B. Identify the recovery point objective.
C. Identify the recovery time objective.
D. Identify how the archive data will be accessed and used.

Answer: A,D

Explanation:
The two tasks required to define the data archive strategy are to identify how the archive data will be accessed and used, and identify the data retention requirements. Data archiving is the process of moving infrequently used or historical data from active storage to a separate storage location for long-term retention. Data archiving can improve performance, reduce storage costs, and comply with legal or regulatory obligations. To define a data archive strategy, a data architect needs to consider how the archived data will be accessed and used by different users or processes in Salesforce or outside Salesforce, and how long the archived data needs to be retained based on business or legal requirements.

NEW QUESTION # 174
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