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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance | 20% | - Data stewardship roles and responsibilities - Data retention and archiving policies - Data quality standards and metrics - Compliance and regulatory considerations |
| Topic 2: Data Integration | 15% | - API-based integration (REST, SOAP, Bulk API) - Sharing rules impact on data integration - Real-time vs batch integration patterns - ETL tools and data migration strategies - Handling locks and parallelism during migration |
| Topic 3: Master Data Management | 20% | - Data duplication and matching rules - Identity resolution - Data cleansing techniques |
| Topic 4: Data Modeling | 25% | - Skinny tables for performance optimization - Big Objects and external objects - Large Data Volumes (LDV) design considerations - Normalization and denormalization strategies - Indexing strategies |
| Topic 5: Data Architecture | 20% | - Archiving and data lifecycle management - Architecture patterns and design principles - Performance tuning and optimization - Governor limits and bulk processing |
>> Data-Architect New Questions <<
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NEW QUESTION # 103
Northern Trail Outfitters is concerned because some of its data is sensitive and needs to be identified for access.
What should be used to provide ways to filter and identify the sensitive data?
Answer: B
NEW QUESTION # 104
DreamHouse Realty has a legacy system that captures Branch Offices and Transactions. DreamHouse Realty has 15 Branch Offices. Transactions can relate to any Branch Office. DreamHouse Realty has created hundreds of thousands of Transactions per year.
A Data Architect needs to denormalize this data model into a single Transaction object with a Branch Office picklist.
What are two important considerations for the Data Architect in this scenario? (Choose two.)
Answer: A,B
Explanation:
The Data Architect should consider the limitations on Org data storage and the Bulk API limitations on picklist fields when denormalizing the data model into a single Transaction object with a Branch Office picklist. The Org data storage limit is the total amount of data that can be stored in a Salesforce Org, and it depends on the edition and license type of the Org 1 . The Bulk API limit on picklist fields is the maximum number of values that can be imported or exported using the Bulk API, and it is 1,000 values per picklist field
2 . These limitations could affect the performance and scalability of the data model, and the Data Architect should plan accordingly.
NEW QUESTION # 105
UC has the following system:
Billing system.
Customer support system.
CRM system.
US has been having trouble with business intelligence across the different systems. Recently US implemented a master data management (MDM) solution that will be the system of truth for the customer records.
Which MDM data element is needed to allow reporting across these systems?
Answer: B
NEW QUESTION # 106
UC has a variety of systems across its technology landscape, including SF, legacy enterprise resource planning (ERP) applications and homegrown CRM tools. UC has decided that they would like to consolidate all customer, opportunity and order data into Salesforce as part of its master data management (MDM) strategy.
What are the 3 key steps that a data architect should take when merging data from multiple systems into Salesforce? Choose 3 answers:
Answer: B,C,E
Explanation:
The three key steps that a data architect should take when merging data from multiple systems into Salesforce are:
Analyze each system's data model and perform gap analysis. This step involves understanding the structure and meaning of the data in each system, identifying the common and unique data elements, and mapping the data fields between the systems. This step also involves assessing the quality and consistency of the data, and identifying any data cleansing or transformation needs.
Utilize an ETL tool to merge, transform, and de-duplicate data. This step involves using an ETL tool to connect to the source systems, extract the data, apply any data transformations or validations, and load the data into Salesforce. This step also involves applying de-duplication rules or algorithms to avoid creating duplicate records in Salesforce.
Work with stakeholders to define record and field survivorship rules. This step involves collaborating with the business users and owners of the data to determine which records and fields should be retained or overwritten in case of conflicts or discrepancies. This step also involves defining the criteria and logic for record and field survivorship, and implementing them in the ETL tool or in Salesforce.
Creating new fields to store additional values from all the systems is not a key step, but rather a possible outcome of the gap analysis. It may not be necessary or desirable to create new fields for every value from every system, as it may result in redundant or irrelevant data. Installing a 3rd party AppExchange tool to handle the merger is not a key step, but rather a possible option for choosing an ETL tool. It may not be the best option depending on the requirements, budget, and preferences of the organization.
NEW QUESTION # 107
Universal Containers (UC) has implemented Sales Cloud and it has been noticed that Sales reps are not entering enough data to run insightful reports and dashboards. UC executives would like to monitor and measure data quality metrics. What solution addresses this requirement?
Answer: C
Explanation:
Using third-party AppExchange tools to monitor and measure data quality can address the requirement of UC executives by providing features such as data cleansing, deduplication, validation, enrichment, and scoring. These tools can help improve the accuracy, completeness, and consistency of the data entered by sales reps .
NEW QUESTION # 108
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