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| Section | Objectives |
|---|---|
| Data Architecture and Design | - Conceptual and logical data modeling - Handling large data volume and scalability considerations - Data modeling best practices in Salesforce |
| Data Management | - Data governance principles - Master data management (MDM) concepts - Data quality management |
| Data Integration | - API-based integration approaches - Integration patterns in Salesforce - ETL vs ELT strategies |
| Data Migration | - Data migration strategies and planning - Data cleansing and transformation |
| Salesforce Data Model and Platform Capabilities | - Relationships and schema design - Reporting and analytics considerations - Standard and custom objects |
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NEW QUESTION # 196
To avoid creating duplicate Contacts, a customer frequently uses Data Loader to upsert Contact records into Salesforce. What common error should the data architect be aware of when using upsert?
Answer: A
Explanation:
Data Loader uses external Id fields to match records in the CSV file with records in Salesforce during an upsert operation. If the CSV file contains duplicate external Id values within the same file, Data Loader will throw an error saying "Duplicate Id Specified" and will not process those records. Therefore, it is important to ensure that the CSV file does not have any duplicate external Id values before using Data Loader to upsert records.
NEW QUESTION # 197
A data architect has been tasked with optimizing a data stewardship engagement for a Salesforce instance Which three areas of Salesforce should the architect review before proposing any design recommendation? Choose 3 answer
Answer: A,C,D
Explanation:
Determining if any integration points create records in Salesforce, running key reports to determine what fields should be required, and reviewing the sharing model to determine impact on duplicate records are three areas of Salesforce that the architect should review before proposing any design recommendation. These areas will help to identify the sources and quality of data, the business rules and validations for data entry, and the access and visibility of data across users and roles. Reviewing the metadata xml files for redundant fields to consolidate, and exporting the setup audit trail to review what fields are being used are not necessary steps for optimizing a data stewardship engagement, as they are more related to metadata management and audit tracking.
NEW QUESTION # 198
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?
Answer: B,C,E
Explanation:
According to the Salesforce documentation, 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.
NEW QUESTION # 199
Universal Containers has a custom object with millions of rows of data. When executing SOQL queries, which three options prevent a query from being selective? (Choose three.)
Answer: A,C,D
Explanation:
Using leading % wildcards, performing large loads and deletions, and using NOT and != operators are three options that prevent a query from being selective. These options can cause the query to scan a large number of records or indexes, which can negatively impact the performance and scalability of the query. Using a custom index on a deterministic formula field, on the other hand, can improve the query selectivity by allowing the query to use the index instead of scanning all the records6.
NEW QUESTION # 200
NTO has a loyalty program to reward repeat customers. The following conditions exists:
1. Reward levels are earned based on the amount spent during the previous 12 months.
2. The program will track every item a customer has bought and grant them points for discount.
3. The program generates 100 million records each month.
NTO customer support would like to see a summary of a customer's recent transaction and reward level(s) they have attained.
Which solution should the data architect use to provide the information within the salesforce for the customer support agents?
Answer: A
Explanation:
According to the Get Started with Big Objects unit on Trailhead, one of the use cases for custom big objects is to store and manage loyalty program data for customers. The unit states that "From loyalty programs to transactions, order, and billing information, use a custom big object to keep track of every detail." Therefore, a custom big object can be used to capture the reward program data and display it on the contact record. Additionally, according to the Big Objects Implementation Guide, big objects can handle massive amounts of data (up to billions of records) and can be updated nightly from external systems using Bulk API or batch Apex. Therefore, a custom big object can meet the requirements of NTO's loyalty program scenario.
NEW QUESTION # 201
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