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Salesforce Plat-Arch-201 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Modeling and Database Design25%- Object type selection
  • 1. Standard/custom objects vs Big Objects: use cases, pros and cons
- Data skew prevention
  • 1. Avoid record locking, sharing issues and excessive parent-child relationships
- Metadata management
  • 1. Business dictionary, data lineage, taxonomy and classification
- Data model design for Customer 360
  • 1. Scalable design aligned with security and sharing model
  • 2. Objects, fields, relationships and object features
Topic 2: Data Integration20%- Integration patterns and technologies
  • 1. Synchronous vs asynchronous, batch vs real-time
  • 2. APIs, middleware and external systems integration
- Data synchronization and consistency
  • 1. Conflict resolution, data consistency and idempotency
- External data sources
  • 1. External Objects, OData and virtual data access
Topic 3: Data Migration15%- Migration strategy and planning
  • 1. Assessment, mapping, validation and rollback planning
- Data quality and validation
  • 1. Cleaning, deduplication and post-migration verification
- Migration execution and tools
  • 1. Data loader, ETL tools and bulk API usage
Topic 4: Master Data Management5%- MDM implementation approaches
  • 1. Canonical modeling and hierarchy management
  • 2. Harmonization, consolidation, survivorship rules and thresholds
- Golden record and system of truth
  • 1. Establishing single source of truth in single org
Topic 5: Data Governance, Quality and Security15%- Data governance framework
  • 1. Policies, standards, ownership and stewardship
- Data security and compliance
  • 1. Access control, encryption, compliance and audit
- Data quality management
  • 1. Profiling, monitoring, cleansing and enrichment
Topic 6: Large Data Volumes and Performance20%- LDV architecture design
  • 1. Data distribution, archiving and partitioning strategies
- Query and performance optimization
  • 1. Indexing, selective queries and avoiding performance bottlenecks
- Storage and scalability planning
  • 1. Data volume growth planning and storage limits

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Salesforce Certified Platform Data Architect Sample Questions (Q175-Q180):

NEW QUESTION # 175
Universal Containers (UC) is implementing its new Internet of Things technology, which consists of smart containers that provide information on container temperature and humidity updated every 10 minutes back to UC. There are roughly 10,000 containers equipped with this technology with the number expected to increase to 50,000 across the next five years. It is essential that Salesforce user have access to current and historical temperature and humidity data for each container. What is the recommended solution?

Answer: C

Explanation:
The recommended solution for Universal Containers (UC) to implement its new Internet of Things technology is to create a new Container Reading custom object with a master-detail relationship to Container which is created when a new measure is received for a specific container.
Implement an archiving process that runs every hour. This solution would allow UC to store and access current and historical temperature and humidity data for each container on Salesforce, and use reports and dashboards to analyze it. However, since UC expects a large volume of data over time, they should implement an archiving process that moves data off-platform after a certain period of time to avoid hitting the Org data storage limit and maintain optimal performance. The other options are not recommended, as they would either not store the historical data on Salesforce, or create too many custom fields on the Container object that could impact performance and usability.


NEW QUESTION # 176
Universal Containers (UC) is planning to move away from legacy CRM to Salesforce. As part of one-time data migration, UC will need to keep the original date when a contact was created in the legacy system. How should an Architect design the data migration solution to meet this requirement?

Answer: C

Explanation:
Enabling "Set Audit Fields" allows the user loading the data to set the value of the standard CreatedDate field to match the original date from the legacy system. This is a one-time permission that can be revoked after the migration is completed. The other options would either not work or require additional customization


NEW QUESTION # 177
Northern Trail Outfitter has implemented Salesforce for its associates nationwide, Senior management is concerned that the executive dashboard is not reliable for their real-time decision- making. On analysis, the team the following issues with data entered in Salesforce.
- Information in certain records is incomplete.
- Incorrect entry in certain fields causes records to be excluded in
report fitters.
- Duplicate entries cause incorrect counts.
Which three steps should a data architect recommend to address the issues?

Answer: B,C,D

Explanation:
According to the Salesforce documentation, data quality is the measure of how well the data in Salesforce meets the expectations and requirements of the users and stakeholders. Data quality can be affected by various factors, such as data entry errors, data duplication, data inconsistency, data incompleteness, data timeliness, etc. To address the issues with data quality that affect the reliability of executive dashboards, a data architect should recommend:
Building a sales data warehouse with purpose-built data marts for dashboards and senior management reporting (option B). This means creating a separate database or system that stores and organizes sales data from Salesforce and other sources for analytical purposes. A data warehouse can provide a single source of truth for sales data and enable faster and more accurate reporting and analysis. A data mart is a subset of a data warehouse that focuses on a specific subject or business area, such as sales performance, customer segmentation, product profitability, etc. A data mart can provide tailored and relevant data for different users or groups based on their needs and interests.
Exploring third-party data providers to enrich and augment information entered in Salesforce (option C). This means using external services or tools that can validate, correct, update, and enhance the data that is entered or imported into Salesforce. This can help improve data quality and accuracy, and reduce data duplication and incompleteness.
Leveraging Salesforce features, such as validation rules, to avoid incomplete and incorrect records (option D). This means using features that allow defining rules and criteria to validate the data that is entered or updated by the users or integrations. This can help prevent invalid or incorrect data from being saved, and trigger actions or alerts to correct or improve the data.


NEW QUESTION # 178
A large Automobile company has implemented SF for its Sales Associates. Leads flow from its website to SF using a batch integration in SF. The Batch job connects the leads to Accounts in SF. Customer visiting their retail stores are also created in SF as Accounts.
The company has noticed a large number of duplicate accounts in SF. On analysis, it was found that certain customers could interact with its website and also visit the store. The Sales associates use Global Search to search for customers in Salesforce before they create the customers.
Which scalable option should a data Architect choose to implement to avoid duplicates?

Answer: A

Explanation:
The data architect should choose to implement duplicate rules in SF (Salesforce) to validate duplicates during the account creation process. Duplicate rules are a feature in Salesforce that allow users to define criteria and actions for detecting and preventing duplicate records. By creating duplicate rules for accounts, the data architect can ensure that any leads from the website or customers from the retail stores that match existing accounts in Salesforce are flagged or blocked before they are created as new accounts. This will help avoid duplicate accounts in Salesforce and maintain data quality.


NEW QUESTION # 179
Northern Trail Outfitter has implemented Salesforce for its associates nationwide, Senior management is concerned that the executive dashboard is not reliable for their real-time decision- making. On analysis, the team the following issues with data entered in Salesforce.
- Information in certain records is incomplete.
- Incorrect entry in certain fields causes records to be excluded in
report fitters.
- Duplicate entries cause incorrect counts.
Which three steps should a data architect recommend to address the issues?

Answer: B,C,D


NEW QUESTION # 180
......

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