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| Section | Weight | Objectives |
|---|
| Large Data Volumes and Performance | 20% | - Storage and scalability planning
- 1. Data volume growth planning and storage limits
- LDV architecture design
- 1. Data distribution, archiving and partitioning strategies
- Query and performance optimization
- 1. Indexing, selective queries and avoiding performance bottlenecks
|
| Master Data Management | 5% | - Golden record and system of truth
- 1. Establishing single source of truth in single org
- MDM implementation approaches
- 1. Canonical modeling and hierarchy management
- 2. Harmonization, consolidation, survivorship rules and thresholds
|
| Data Governance, Quality and Security | 15% | - Data quality management
- 1. Profiling, monitoring, cleansing and enrichment
- Data governance framework
- 1. Policies, standards, ownership and stewardship
- Data security and compliance
- 1. Access control, encryption, compliance and audit
|
| Data Migration | 15% | - Data quality and validation
- 1. Cleaning, deduplication and post-migration verification
- Migration execution and tools
- 1. Data loader, ETL tools and bulk API usage
- Migration strategy and planning
- 1. Assessment, mapping, validation and rollback planning
|
| Data Integration | 20% | - Data synchronization and consistency
- 1. Conflict resolution, data consistency and idempotency
- External data sources
- 1. External Objects, OData and virtual data access
- Integration patterns and technologies
- 1. Synchronous vs asynchronous, batch vs real-time
- 2. APIs, middleware and external systems integration
|
| Data Modeling and Database Design | 25% | - Object type selection
- 1. Standard/custom objects vs Big Objects: use cases, pros and cons
- Data model design for Customer 360
- 1. Scalable design aligned with security and sharing model
- 2. Objects, fields, relationships and object features
- Metadata management
- 1. Business dictionary, data lineage, taxonomy and classification
- Data skew prevention
- 1. Avoid record locking, sharing issues and excessive parent-child relationships
|
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Salesforce Certified Platform Data Architect Sample Questions (Q43-Q48):
NEW QUESTION # 43
Universal Containers has a rollup summary field on account to calculate the number of contacts associated with an account. During the account load, Salesforce is throwing an "UNABLE
_TO_LOCK_ROW" error. Which solution should a data architect recommend to resolve the error?
- A. Perform a batch job in serial mode and reduce the batch size.
- B. Perform a batch job in parallel mode and reduce the batch size.
- C. Leverage Data Loader's platform API to load data.
- D. Defer rollup summary field calculation during data migration.
Answer: A
Explanation:
According to the Salesforce documentation, the "UNABLE _TO_LOCK_ROW" error occurs when a record is being updated or created, and another operation tries to access or update the same record at the same time. This can cause lock contention and timeout issues. To resolve the error, some of the recommended solutions are:
Perform a batch job in serial mode and reduce the batch size (option B). This means running the batch job one at a time and processing fewer records per batch. This can reduce the chances of concurrent updates and lock contention on the same records.
Use the FOR UPDATE keyword to lock records in Apex code or API calls. This means explicitly locking the records that are being accessed or updated by a transaction, and preventing other transactions from modifying them until the lock is released. This can avoid conflicts and errors between concurrent operations on the same records.
NEW QUESTION # 44
A casino is implementing Salesforce and is planning to build a customer 360 degree view for a customer who visits its resorts. The casino currently maintains the following systems that record customer activity:
1. Point-of-sale system: All purchases for a customer
2. Salesforce: Ail customer service activity and sales activities for a customer
3. Mobile app: All bookings, preferences, and browser activity for a customer
4. Marketing: All email, SMS, and social campaigns for a customer
Customer service agents using Salesforce would like to view the activities from all four systems to provide support to customers. The information has to be current and real time.
What strategy should the data architect implement to satisfy this requirement?
- A. Explore external data sources in Salesforce to build a 360-degree view of the customer.
- B. Periodically upload summary information in Salesforce to build a 360-degree view.
- C. Use a customer data mart to create the 360-degree view of the customer.
- D. Migrate customer activities fro, all four system into Salesforce.
Answer: A
Explanation:
Exploring external data sources in Salesforce to build a 360 degree view of the customer (option A) is the best strategy to satisfy this requirement, as it allows customer service agents to view the activities from all four systems in real time without storing or replicating the data in Salesforce.
NEW QUESTION # 45
DreamHouse Realty has a Salesforce deployment that manages Sales, Support, and Marketing efforts in a multi-system ERP environment. The company recently reached the limits of native reports and dashboards and needs options for providing more analytical insights. What are two approaches an Architect should recommend? (Choose two.)
- A. Weekly Snapshots
- B. AppExchange Apps
- C. Einstein Analytics
- D. Setup Audit Trails
Answer: B,C
Explanation:
Einstein Analytics can provide more analytical insights than native reports and dashboards by allowing users to explore data from multiple sources, create interactive visualizations, and apply AI-powered features. AppExchange Apps can also provide more analytical insights by offering pre-built solutions or integrations with external tools that can enhance the reporting and analytics capabilities of Salesforce6.
NEW QUESTION # 46
Which two aspects of data does an Enterprise data governance program aim to improve?
- A. Data modeling
- B. Data integrity
- C. Data usability
- D. Data distribution
Answer: B,C
Explanation:
Data integrity and data usability are two aspects of data that an Enterprise data governance program aims to improve. Data integrity refers to the accuracy, consistency, and validity of the data across the enterprise. Data usability refers to the ease of access, analysis, and interpretation of the data by the end users
NEW QUESTION # 47
UC is rolling out Sales App globally to bring sales teams together on one platform. UC expects millions of opportunities and accounts to be creates and is concerned about the performance of the application. Which 3 recommendations should the data architect make to avoid the data skew? Choose 3 answer
- A. Use picklist fields rather than lookup to custom object.
- B. Limit assigning one user 10000 records ownership.
- C. Limit associating 10000 records looking up to same records.
- D. Assign 10000 opportunities to one account.
- E. Limit associating 10000 opportunities to one account.
Answer: B,C,E
Explanation:
Data skew occurs when a large number of child records are associated with a single parent record, or when a single user owns a large number of records. This can cause performance issues and lock contention. To avoid data skew, the data architect should limit assigning one user
10,000 records ownership, limit associating 10,000 opportunities to one account, and limit associating 10,000 records looking up to the same record
NEW QUESTION # 48
......
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