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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling | 25% | - Big Objects and external objects - Skinny tables for performance optimization - Large Data Volumes (LDV) design considerations - Indexing strategies - Normalization and denormalization strategies |
| Topic 2: Data Architecture | 20% | - Performance tuning and optimization - Archiving and data lifecycle management - Governor limits and bulk processing - Architecture patterns and design principles |
| Topic 3: Data Governance | 20% | - Data quality standards and metrics - Data retention and archiving policies - Compliance and regulatory considerations - Data stewardship roles and responsibilities |
| Topic 4: Master Data Management | 20% | - Data duplication and matching rules - Identity resolution - Data cleansing techniques |
| Topic 5: Data Integration | 15% | - Handling locks and parallelism during migration - Sharing rules impact on data integration - API-based integration (REST, SOAP, Bulk API) - ETL tools and data migration strategies - Real-time vs batch integration patterns |
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NEW QUESTION # 177
Northern trail Outfitters (NTO) runs its entire out of an enterprise data warehouse (EDW), NTD's sales team starting to use Salesforce after a recent implementation, but currently lacks data required to advanced and opportunity to the next stage.
NTO's management has research Salesforce Connect and would like to use It to virtualize and report on data from the EDW within Salesforce. NTO will be running thousands of reports per day across 10 to 15 external objects.
What should a data architect consider before implementing Salesforce Connect for reporting?
Answer: C
Explanation:
According to the Salesforce Connect Reporting blog post, one of the considerations for using Salesforce Connect for reporting is the OData callout limits per day. The blog post states that "Salesforce Connect has a limit of 100,000 callouts per day. This limit is shared across all external data sources in your org. If you exceed this limit, you will receive an error message and no more callouts will be allowed until the next day." Therefore, a data architect should consider this limit before implementing Salesforce Connect for reporting.
NEW QUESTION # 178
Universal Containers (UC) has a data model as shown in the image. The Project object has a private sharing model, and it has Roll -Up summary fields to calculate the number of resources assigned to the project, total hours for the project, and the number of work items associated to the project. What should the architect consider, knowing there will be a large amount of time entry records to be loaded regularly from an external system into Salesforce.com?
Answer: C
NEW QUESTION # 179
Universal Containers is setting up an external Business Intelligence (BI) system and wants to extract 1,000,000 Contact records. What should be recommended to avoid timeouts during the export process?
Answer: C
NEW QUESTION # 180
A Customer is migrating 10 million order and 30 million order lines into Salesforce using Bulk API. The Engineer is experiencing time-out errors or long delays querying parents order IDs in Salesforce before importing related order line items. What is the recommended solution?
Answer: C
Explanation:
Leverage an External ID from source system orders to import related order lines. This is the recommended solution because it allows you to use the upsert operation to match records based on the External ID field, which is indexed and unique. This avoids the need to query the parent order IDs in Salesforce before importing the order line items, which can cause time-out errors or long delays1.
NEW QUESTION # 181
NTO need to extract 50 million records from a custom object everyday from its Salesforce org. NTO is facing query timeout issues while extracting these records.
What should a data architect recommend in order to get around the time out issue?
Answer: C
Explanation:
The best solution to extract 50 million records from a custom object everyday from Salesforce org without facing query timeout issues is to use an ETL tool for extraction of records. ETL stands for extract, transform, and load, and it refers to a process of moving data from one system to another. An ETL tool is a software application that can connect to various data sources, perform data transformations, and load data into a target destination. ETL tools can handle large volumes of data efficiently and reliably, and they often provide features such as scheduling, monitoring, error handling, and logging5. Using a custom auto number and formula field and use that to chunk records while extracting data is a possible workaround, but it requires creating additional fields and writing complex queries. The REST API can extract data as it automatically chunks records by 200, but it has some limitations, such as a maximum of 50 million records per query job6. Asking SF support to increase the query timeout value is not feasible because query timeout values are not configurable
NEW QUESTION # 182
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