Free Access to Salesforce.Data-Architect.v2024-05-05.q153 with Valid Practice Test (Page 9)

Question 36

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?

A.Periodically export data to cleanse data and import them back into Salesforce for executive reports.
B.Build a sales data warehouse with purpose-build data marts for dashboards and senior management reporting.
C.Explore third-party data providers to enrich and augment information entered in salesforce.
D.Leverage Salesforce features, such as validate rules, to avoid incomplete and incorrect records.
E.design and implement data-quality dashboard to monitor and act on records that are incomplete or incorrect

Correct Answer: B,C,D

According to the Salesforce documentation3, 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.
Periodically exporting data to cleanse data and import them back into Salesforce for executive reports (option A) is not a good solution, as it can be time-consuming, error-prone, and inefficient. It may also cause data inconsistency and synchronization issues between Salesforce and other systems. Designing and implementing data-quality dashboard to monitor and act on records that are incomplete or incorrect (option E) is also not a good solution, as it can be complex, costly, and difficult to maintain. It may also not address the root causes of data quality issues or prevent them from occurring in the first place.

Question 37

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?

A.Use third-party AppExchange tools to monitor and measure data quality.
B.Generate reports to view the quality of sample data.
C.Use custom objects and fields to calculate data quality.
D.Export the data to an enterprise data warehouse and use BI tools for data quality.

Question 38

Universal Containers (UC) is implementing Salesforce Sales Cloud and Service Cloud. As part of their implementation, they are planning to create a new custom object (Shipments), which will have a lookup relationship to Opportunities. When creating shipment records, Salesforce users need to manually input a customer reference, which is provided by customers, and will be stored in the Customer_Reference__c text custom field. Support agents will likely use this customer reference to search for Shipment records when resolving shipping issues. UC is expecting to have around 5 million shipment records created per year. What is the recommended solution to ensure that support agents using global search and reports can quickly find shipment records?

A.Implement an archiving process for shipment records created after five years.
B.Implement an archiving process for shipment records created after three years.
C.Set Customer-Reference_c as an External ID (non-unique).
D.Set Customer-Reference_c as an External ID (unique).

Question 39

NTO processes orders from its website via an order management system (OMS). The OMS stores over 2 million historical records and is currently not integrated with SF. The Sales team at NTO using Sales cloud and would like visibility into related customer orders yet they do not want to persist millions of records directly in Salesforce. NTO has asked the data architect to evaluate SF connect and the concept of data verification. Which 3 considerations are needed prior to a SF Connect implementation?
Choose 3 answers:

A.Assess whether the external data source is reachable via an ODATA endpoint.
B.Identify the external tables to sync into external objects
C.Configure a middleware tool to poll external table data
D.Develop an object relationship strategy.
E.Create a 2nd system Admin user for authentication to the external source.

Question 40

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.Bidirectional integration
B.Unidirectional integration
C.Virtualization
D.Persistence

Question 36

Question 37

Question 38

Question 39

Question 40

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