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Refer to the Exhibit Practice Questions

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Scenario guide

How to approach refer to the exhibit practice questions

Practise exhibit-style questions that ask you to read a topology, table, command output or diagram before choosing the best answer.

Quick answer

Exhibit-style questions test whether you can read a topology, command output, diagram or table before choosing the best answer.

How to extract the relevant detail from an exhibit.

How topology, command output or routing information affects the answer.

How to avoid answering from memory before reading the evidence.

How to map the exhibit back to the exam objective.

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Practice set

Practice scenarios

Question 1mediummultiple choice
Full question →

Refer to the exhibit. A Salesforce admin configured the Einstein Trust Layer policy shown. What is the effect of this policy on AI model usage?

Exhibit

{
  "policy": {
    "dataMask": {
      "enabled": true,
      "fields": ["Email", "Phone", "SSN"]
    },
    "insights": {
      "featureImportance": true
    }
  }
}
Question 2mediummultiple choice
Full question →

Refer to the exhibit. Based on the JSON policy for AI fairness checks, which fairness metric is NOT enabled?

Exhibit

{
  "fairness_checks": {
    "demographic_parity": true,
    "equal_opportunity": false,
    "disparate_impact": true
  },
  "threshold": 0.8
}
Question 3hardmultiple choice
Full question →

Refer to the exhibit. A developer wrote a trigger to call an Einstein prediction API on lead insert. When new leads are created, the trigger fails with a 'Too many SOQL queries' error. What is the most likely cause?

Exhibit

Apex Trigger:
trigger LeadScoringTrigger on Lead (after insert) {
    for(Lead l : Trigger.new) {
        l.Score__c = Einstein_Prediction_API.predict('Lead_Score', l.Id);
    }
}
Question 4hardmultiple choice
Full question →

An AI Associate reviews the Lead Scoring model exhibit. What is the primary ethical concern with this model?

Exhibit

Refer to the exhibit.

```json
{
  "modelName": "LeadScoring_v2",
  "features": ["LeadSource", "Industry", "CompanySize", "EmailDomain", "NumberOfEmployees"],
  "target": "Converted",
  "trainingData": {
    "source": "Salesforce_Leads_2019-2021",
    "recordCount": 50000,
    "classBalance": {"Converted": 5000, "NotConverted": 45000}
  },
  "evaluationMetrics": {
    "accuracy": 0.92,
    "precision": 0.85,
    "recall": 0.30
  }
}
```
Question 5easymultiple choice
Full question →

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

Exhibit

{
  "predictionField": "Lead_Score__c",
  "predictionType": "BinaryClassification",
  "targetObject": "Lead",
  "recordCount": 450,
  "modelStatus": "Active"
}
Question 6easymultiple choice
Full question →

Refer to the exhibit. A Salesforce admin evaluates an Einstein Prediction Builder model for customer churn. What should be the admin's primary concern based on the exhibit?

Exhibit

{
  "predictionField": "Churn__c",
  "recordCount": 1500,
  "modelStatus": "Trained",
  "accuracy": 0.85,
  "auc": 0.72,
  "featuresUsed": ["UsageRate", "SupportTickets", "ContractLength", "Region"],
  "dataDrift": {
    "detected": true,
    "magnitude": 0.23
  }
}
Question 7mediummultiple choice
Full question →

Refer to the exhibit. A Salesforce admin sees this error when trying to enable Einstein Lead Scoring. What should the admin do to resolve the issue?

Exhibit

Error: Lead Scoring is not available. Reason: Insufficient data. Minimum required: 100 leads with activities. Current count: 87.
Question 8mediummultiple choice
Full question →

Refer to the exhibit. A data scientist sees this error when training an Einstein Discovery model for customer churn prediction. What is the most likely reason for the error?

Exhibit

{
  "model": "EinsteinDiscovery_Churn_v2",
  "status": "TRAINING_FAILED",
  "errorCode": "INSUFFICIENT_POSITIVE_EXAMPLES",
  "fieldCount": 8,
  "recordCount": 3200,
  "positiveExamples": 180
}
Question 9hardmultiple choice
Full question →

Refer to the exhibit. The fairness evaluation shows a disparate impact of 0.85, equal opportunity difference of 0.12, and demographic parity difference of 0.18. Which fairness thresholds are violated?

Exhibit

Refer to the exhibit.

```
{
  "modelVersion": "1.0",
  "features": ["age", "income", "credit_score", "zip_code"],
  "fairnessEvaluation": {
    "disparateImpact": 0.85,
    "equalOpportunityDiff": 0.12,
    "demographicParityDiff": 0.18
  },
  "thresholds": {
    "disparateImpactMin": 0.8,
    "equalOpportunityDiffMax": 0.1,
    "demographicParityDiffMax": 0.1
  }
}
```
Question 10hardmultiple choice
Full question →

Refer to the exhibit. An admin configures Einstein Next Best Action with the above JSON. The expected behavior is to recommend the top 5 actions for open leads with a score of at least 70. However, only 2 recommendations appear for some leads. Which is the most likely cause?

Exhibit

Refer to the exhibit.

{
  "type": "EinsteinNextBestAction",
  "recommendationStrategy": "Most Likely to Convert",
  "filters": {
    "object": "Lead",
    "field": "Status",
    "operator": "equals",
    "value": "Open"
  },
  "maxRecommendations": 5,
  "scoreThreshold": 70
}
Question 11hardmultiple choice
Full question →

Refer to the exhibit. A Salesforce admin is troubleshooting email capture failures. Based on the log, which configuration step is most likely missing?

Exhibit

Error log from Einstein Activity Capture:
[2024-10-20 14:23:45] ERROR: Failed to capture email for contactId=003B0000007v4ZW.
Reason: 'Email address domain not associated with any connected email service.'
[2024-10-20 14:23:46] INFO: Skipping email from noreply@acme.org (domain acme.org not configured).
[2024-10-20 14:23:47] WARN: Email capture quota at 85% for user admin@mycompany.com.
Question 12easymultiple choice
Full question →

Refer to the exhibit. A data scientist built a model using training data where 80% of leads were won. The model achieved 80% accuracy. What is the main issue with this evaluation?

Exhibit

Custom Model Configuration:
- Name: LeadScorer_v2
- Algorithm: XGBoost
- Training Data: 100,000 records (80% won, 20% lost)
- Evaluation Metric: Accuracy
Question 13mediummultiple choice
Full question →

Refer to the exhibit. A sales manager sees that an account has an Einstein Score of 78 with a confidence of 0.65. What is the most appropriate interpretation?

Exhibit

# Salesforce CLI command output
$ sfdx force:data:record:get -s Object -i 001XX000003GJYp -u myOrg
{
  "attributes": {"type": "Account", "url": "/services/data/v55.0/sobjects/Account/001XX000003GJYp"},
  "Id": "001XX000003GJYp",
  "Name": "Acme Corp",
  "Einstein_Score__c": 78,
  "Einstein_Score_Confidence__c": 0.65,
  "Last_Scored_Date__c": "2024-10-15"
}
Question 14hardmultiple choice
Full question →

Refer to the exhibit. An admin runs a preprocess script before training an Einstein model. Why is normalization applied to the 'AnnualRevenue' and 'NumberOfEmployees' columns?

Network Topology
$ einstein_preprocessinput leads.csvoutput clean.csvdrop-missingnormalizeProcessing: 10000 records, 30 columns.Output file: clean.csv
Question 15mediummultiple choice
Full question →

Refer to the exhibit. A Salesforce admin runs an audit command for an Einstein model. What conclusion can be drawn from the output?

Network Topology
Command: einstein:ai:audit-logmodel-id 42last-hourOutput: {"requests": 150,"latency_avg_ms": 350,"errors": 3,"token_usage": 45000

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