Number of joins used in report query.

Number of records returned by report query.

Explanation:

Universal Containers manages over 200 million opportunities (10 million accounts × 20 opportunities) and needs a daily report for a specific opportunity stage. With such large data volumes, report performance is critical. Key considerations must focus on minimizing processing complexity and data retrieval to ensure fast, efficient reporting, avoiding delays that could frustrate the Sales Executive’s daily workflow.

Correct Option: 🅱️ Number of joins used in report query
Joins in report queries, like those linking Opportunities to Accounts, increase processing time, especially with 200 million opportunities. Minimizing joins by focusing only on necessary fields (e.g., stage) reduces complexity. For example, avoiding unrelated objects in the query keeps the report lean, ensuring faster performance. Salesforce recommends optimizing joins for large data volumes to prevent timeouts or sluggish reports.

Correct Option: 🅲 Number of records returned by report query
The number of records returned directly impacts report performance. With 200 million opportunities, filtering to a specific stage (e.g., “Closed Won”) reduces the result set, speeding up processing. Large result sets strain Salesforce’s resources, causing delays. Using selective filters ensures only relevant data is retrieved, aligning with best practices for efficient reporting in high-volume environments.

Incorrect Option: 🅰️ Number of queries running at a time
The number of concurrent queries isn’t a direct consideration for a single report’s performance. While org-wide query volume can affect overall system performance, the question focuses on the report itself. Factors like joins and record count within the report query have a greater impact, as concurrent queries are managed by Salesforce’s governor limits, not report design.

Incorrect Option: 🅳 Number of characters in report query
The number of characters in a report query doesn’t significantly affect performance. Salesforce processes queries based on data volume and complexity, not character count. For example, a short query with many joins or large record sets will perform worse than a longer, optimized query. This option is irrelevant to ensuring efficient reporting for UC’s large opportunity dataset.

Reference
Salesforce Help: Reports and Dashboards
Salesforce Architect Guide: Large Data Volumes

Integrate Salesforce with ERP, and make ERP as system of truth.

Implement a de-dupe solution and establish account ownership in Salesforce

Explanation:

Data issues often stem from lacking a clear system of truth and missing governance. ERP should own customer master data, while Salesforce manages sales activities. Integrating the two ensures consistency. On top of that, duplicates need active prevention and resolution inside Salesforce, with deduplication rules and ownership structures to keep the data clean long-term. Nightly batch jobs alone won’t solve root causes.

✅ Correct Option: B
Making ERP the system of truth and integrating it into Salesforce ensures consistency for account records. The ERP master manages the canonical customer record, while Salesforce consumes the data to support the sales process.

✅ Correct Option: D
Deduplication and ownership inside Salesforce directly tackle complaints. By setting dedupe rules and assigning ownership, Salesforce becomes cleaner, reducing user frustration and preventing the same errors from repeating.

❌ Incorrect Option: A
Nightly deduplication jobs may clean accounts temporarily, but they don’t solve the structural issue. Without ownership rules and ERP integration, duplicates will just reappear.

❌ Incorrect Option: C
A nightly sync from ERP to Salesforce introduces delays and may cause outdated records to persist during the day. It also doesn’t prevent duplicates that originate in Salesforce itself.

🔗 Reference:
Salesforce Data Quality Best Practices
Integration Design: Salesforce as a Consumer of Master Data

Make sure the query doesn't contain NULL in any filter criteria.

Create custom indexes on the fields used in the filter criteria.

Explanation:

✅ A. NULL in filter criteria
Queries using WHERE field = NULL or WHERE field != NULL are problematic because they bypass indexes and require full table scans, especially on large datasets like 12 million records.
Such filters are not selective, which contributes to query timeouts.

✅ C. Custom indexes
Indexes improve query performance by allowing Salesforce to efficiently retrieve relevant records.
If fields used in WHERE clauses are not selectively indexed, the query can exceed governor limits or time out.
Data Architects should evaluate selectivity and whether custom indexes (skinny tables or external indexes) are needed.

Why Not the Others?

❌ B. Create a formula field instead of multiple filter criteria
Formula fields are not indexed by default, and using them in WHERE clauses can actually hurt performance.
Multiple filter criteria aren't inherently problematic—how selective the filters are matters more.

❌ D. Modify the integration users' profile to have View All Data
This has no impact on query performance.
It changes access rights, not how efficiently the query runs.

Use Einstein Analytics for multi-org.

Explanation:

Option C (✔️ Best Choice) – Einstein Analytics (now Tableau CRM) is Salesforce’s native AI-powered analytics platform, designed to:
Aggregate data from multiple orgs (via connectors, ETL, or Salesforce Data Federation).
Provide a unified global view of Accounts, Opportunities, etc.
Leverage AI-driven insights (predictive analytics, anomaly detection, etc.).

Option A (❌ Limited) – Standard reports/dashboards cannot pull data from multiple orgs into a single view.

Option B (❌ Alternative, but not best) – While some AppExchange tools (e.g., Gizmo, CRM Analytics connectors) can help, they lack native AI integration and may require extra setup.

Option D (❌ Not scalable) – Custom Python scripts are manual, brittle, and unsupported for enterprise reporting needs.

Key stakeholders should review any fields that share the same purpose between systems to see how they will be used in Salesforce.

Explanation:

Option B (✔️ Best Practice) – Stakeholder alignment ensures:
1. Field-Level Ownership: Clarifies which system "owns" specific fields (e.g., "Billing Address" from System A vs. "Shipping Address" from System B).
2. Business Rules: Matches field usage to operational needs (e.g., System A’s "Customer Tier" is used for reporting, System B’s for billing).
3. MDM Integration: MDM systems enforce these rules but require human-driven decisions first.

Why Not the Others?

Option A (❌ Hands-Off Risk) – MDM systems execute rules but can’t define them without stakeholder input.
Option C (❌ Technical Overfocus) – Schema reviews are useful, but field names ≠ ownership. Business context matters more.
Option D (❌ Chaotic) – Letting the "last sync win" guarantees conflicts and data corruption.