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| Section | Weight | Objectives |
|---|
| Topic 1: 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
|
| Topic 2: Data Migration | 15% | - Migration strategy and planning
- 1. Assessment, mapping, validation and rollback planning
- Migration execution and tools
- 1. Data loader, ETL tools and bulk API usage
- Data quality and validation
- 1. Cleaning, deduplication and post-migration verification
|
| Topic 3: Master Data Management | 5% | - MDM implementation approaches
- 1. Harmonization, consolidation, survivorship rules and thresholds
- 2. Canonical modeling and hierarchy management
- Golden record and system of truth
- 1. Establishing single source of truth in single org
|
| Topic 4: Large Data Volumes and Performance | 20% | - Storage and scalability planning
- 1. Data volume growth planning and storage limits
- Query and performance optimization
- 1. Indexing, selective queries and avoiding performance bottlenecks
- LDV architecture design
- 1. Data distribution, archiving and partitioning strategies
|
| Topic 5: Data Modeling and Database Design | 25% | - Data skew prevention
- 1. Avoid record locking, sharing issues and excessive parent-child relationships
- Metadata management
- 1. Business dictionary, data lineage, taxonomy and classification
- Object type selection
- 1. Standard/custom objects vs Big Objects: use cases, pros and cons
- Data model design for Customer 360
- 1. Objects, fields, relationships and object features
- 2. Scalable design aligned with security and sharing model
|
| Topic 6: 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. APIs, middleware and external systems integration
- 2. Synchronous vs asynchronous, batch vs real-time
|
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Salesforce Certified Platform Data Architect Sample Questions (Q240-Q245):
NEW QUESTION # 240
North Trail Outfitters (NTD) is in the process of evaluating big objects to store large amounts of asset data from an external system. NTO will need to report on this asset data weekly. Which two native tools should a data architect recommend to achieve this reporting requirement?
- A. Standard reports and dashboards
- B. Einstein Analytics
- C. Async SOQL with a custom object
- D. Standard SOQL queries
Answer: B,C
Explanation:
Async SOQL with a custom object (option B) and Einstein Analytics (option D) are the two native tools that can be used to report on big object data. Async SOQL allows querying big object data and storing the results in a custom object, which can then be used for reporting. Einstein Analytics can connect to big object data sources and provide advanced analytics and visualization features.
NEW QUESTION # 241
Universal Containers (UC) has a complex system landscape and is implementing a data governance program for the first time Which two first steps would be appropriate for UC to initiate an assessment of data architecture? Choose 2 answer
- A. Engage with executive sponsorship to assess enterprise data strategy and goals.
- B. Engage with business units and IT to assess current operational systems and data models.
- C. Engage with IT program managers to assess current velocity of projects in the pipeline.
- D. Engage with database administrators to assess current database performance metrics.
Answer: A,B
Explanation:
Engaging with executive sponsorship to assess enterprise data strategy and goals, and engaging with business units and IT to assess current operational systems and data models are two first steps that would be appropriate for UC to initiate an assessment of data architecture. These steps will help to understand the current state of data management, the business needs and expectations, and the gaps and opportunities for improvement. Engaging with IT program managers to assess current velocity of projects in the pipeline, and engaging with database administrators to assess current database performance metrics are not relevant steps for assessing data architecture, as they are more related to project management and technical performance.
NEW QUESTION # 242
UC is having issues using Informatica Cloud Louder to export +10MOrder records. Each Order record has 10 Order Line Items. What two steps can you take to help correct this? Choose two answers.
- A. Limit Batch to 10K records
- B. Export in multiple batches
- C. Export Bulk API in parallel mode
- D. Use PK Chunking
Answer: B,D
Explanation:
Exporting in multiple batches and using PK Chunking are two steps that can help correct the issues with exporting large volumes of Order records using Informatica Cloud Loader. Exporting in multiple batches can reduce the load on the system and avoid timeouts or errors. Using PK Chunking can split a large data set into smaller chunks based on the record IDs and enable parallel processing of each chunk.
NEW QUESTION # 243
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. Virtualization
- C. Unidirectional integration
- D. Bidirectional integration
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 # 244
As part of a phased Salesforce rollout. there will be 3 deployments spread out over the year. The requirements have been carefully documented. Which two methods should an architect use to trace back configuration changes to the detailed requirements? Choose 2 answer
- A. Use the Force.com IDE to save the metadata files in source control.
- B. Review the setup audit trail for configuration changes.
- C. Put the business purpose in the Description of each field.
- D. Maintain a data dictionary with the justification for each field.
Answer: A,C
Explanation:
Option B is correct because putting the business purpose in the Description of each field is a method that an architect can use to trace back configuration changes to the detailed requirements. The Description of each field provides a brief explanation of what the field is used for and why it is needed. Option D is correct because using the Force.com IDE to save the metadata files in source control is another method that an architect can use to trace back configuration changes to the detailed requirements. The Force.com IDE is an integrated development environment that allows developers to work with Salesforce metadata files and Apex code. Source control is a system that tracks and manages changes to code and configuration files.
NEW QUESTION # 245
......
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