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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Large Data Volumes | 20% | - Describe the implications of LDV on data modeling - Describe the features and best practices for LDV - Describe the considerations for Large Data Volumes (LDV) |
| Topic 2: Data Migration | 15% | - Describe the implications of data migration on data quality - Describe the tools and best practices for data migration - Describe the considerations for data migration |
| Topic 3: Salesforce Data Management | 25% | - Describe the considerations for data governance - Describe the implications of LDV on data management - Describe the considerations for data archival - Describe the considerations for data migration |
| Topic 4: Data Governance | 10% | - Describe the elements of a data governance framework - Describe the considerations for data classification - Describe the considerations for data quality |
| Topic 5: Master Data Management | 5% | - Describe the methods for aligning data across systems - Describe the considerations for a Master Data Management strategy |
| Topic 6: Data Modeling/Database Design | 25% | - Describe the implications of LDV on data modeling - Describe the considerations when designing a data model for Salesforce - Explain the usage of external objects - Describe the options and considerations for Large Data Volumes (LDV) |
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NEW QUESTION # 21
Universal Containers (UC) is concerned that data is being corrupted daily either through negligence or maliciousness. They want to implement a backup strategy to help recover any corrupted data or data mistakenly changed or even deleted. What should the data architect consider when designing a field -level audit and recovery plan?
Answer: B
Explanation:
Option C is the best answer because reviewing projected data storage needs is an important step in designing a field-level audit and recovery plan. You need to estimate how much data storage you will need in the future and plan accordingly.
NEW QUESTION # 22
Northern Trail Outfitters (NTO) wants to capture a list of customers that have bought a particular product. The solution architect has recommended to create a custom object for product, and to create a lookup relationship between its customers and its products.
Products will be modeled as a custom object (NTO_ Product__ c) and customers are modeled as person accounts. Every NTO product may have millions of customers looking up a single product, resulting in a lookup skew.
What should a data architect suggest to mitigate Issues related to lookup skew?
Answer: B
Explanation:
Creating multiple similar products and distributing the skew across those products can be a way to mitigate issues related to lookup skew. The article explains that lookup skew happens when a very large number of records are associated with a single record in the lookup object, and this can cause record locking and performance issues. The article suggests creating multiple copies of the same product record and assigning different child records to each copy, so that the number of child records per parent record is reduced.
NEW QUESTION # 23
Northern Trail Outfitters (NTO) has implemented Salesforce for its sales users. The opportunity management in Saiesforce Is implemented as follows:
1. Sales users enter their opportunities in Salesforce for forecasting and reporting purposes.
2. NTO has a product pricing system (PPS) that is used to update the Opportunity Amount field on opportunities on a daily basis.
3. PPS is the trusted source within NTO for Opportunity Amount.
4. NTO uses Opportunity Forecast for its sales planning and management.
Sales users have noticed that their updates to the Opportunity Amount field are overwritten when PPS updates their opportunities.
How should a data architect address this overwriting issue?
Answer: B
Explanation:
Changing Opportunity Amount field access to Read Only for sales users field-level security (option C) is the best way to address the overwriting issue, as it prevents sales users from updating the field that is controlled by PPS, and ensures data consistency and accuracy.
NEW QUESTION # 24
Universal Containers (UC) is implementing a new customer categorization process where customers should be assigned to a Gold, Silver, or Bronze category if they've purchased UC's new support service. Customers are expected to be evenly distributed across all three categories.
Currently, UC has around 500,000 customers, and is expecting 1% of existing non-categorized customers to purchase UC's new support service every month over the next five years. What is the recommended solution to ensure long-term performance, bearing in mind the above requirements?
Answer: D
Explanation:
The recommended solution to ensure long-term performance for the customer categorization process is to implement a new picklist custom field in the Account object with Gold, Silver, and Bronze values. A picklist field is a simple and efficient way to store a predefined set of values for a record. A picklist field has the following benefits:
It allows you to easily filter, sort, and group records by their category values, which can help you analyze and segment your customers.
It does not require creating any additional objects or relationships, which can reduce the data model complexity and maintenance overhead.
It supports large data volumes and can handle millions of records without affecting performance or scalability.
NEW QUESTION # 25
Northern Trail Outfitters (NTO) has the following systems:
- Customer master-source of truth for customer information
- Service cloud-customer support
- Marketing cloud-marketing support
- Enterprise data warehouse--business reporting
The customer data is duplicated across all these systems and are not kept in sync. Customers are also complaining that they get repeated marketing emails and have to call into update their information.
NTO is planning to implement master data management (MDM) solution across the enterprise.
Which three data will an MDM tool solve? Choose 3 answer
Answer: A,B,C
Explanation:
According to the What is Master Data Management (MDM)? article, some of the data challenges that an MDM tool can solve are data duplication, data accuracy and quality, and data standardization. The article states that "MDM solutions comprise a broad range of data cleansing, transformation, and integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform, and repair data. Once the data meets the quality thresholds, schemas and taxonomies are created to help maintain a high-quality master reference." Therefore, an MDM tool can help NTO eliminate data duplication across different systems, improve data accuracy and quality by removing errors and inconsistencies, and standardize data formats and definitions for better integration and analysis.
NEW QUESTION # 26
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