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Refer to the Exhibit Practice Questions

Practise Salesforce AI Associate AI Associate practice questions — original exam-style scenarios covering every exam domain, with detailed explanations, wrong-answer analysis, and common exam traps.

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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. What is the most likely cause of the fairness issue?

Exhibit

Refer to the exhibit.

```
Model: Churn Predictor v2
Training Data: 80% male, 20% female
Accuracy: 85% overall, 90% male, 60% female
Fairness Metric: Equal Opportunity Difference = 0.3
```
Question 2easymultiple 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 3mediummultiple 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
Question 4hardmultiple 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 5mediummultiple 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 6mediummultiple choice
Full question →

Refer to the exhibit. In the JSON configuration above, which data preparation step could introduce bias?

Exhibit

{
  "dataset": "OpportunityPrediction",
  "fields": [
    {"name": "Amount", "type": "currency", "missing": "fill_with_median"},
    {"name": "Stage", "type": "picklist", "missing": "exclude"},
    {"name": "CreatedDate", "type": "date", "missing": "use_default"},
    {"name": "Description", "type": "textarea", "missing": "ignore"}
  ],
  "model": "EinsteinDiscovery",
  "target": "Won"
}
Question 7hardmultiple choice
Full question →

Refer to the exhibit. A data analyst has defined this field mapping for Einstein Prediction Builder. Which data issue would most likely arise from this mapping?

Exhibit

{
  "fieldMapping": [
    {"sourceField": "Id", "targetType": "Text"},
    {"sourceField": "AccountName", "targetType": "Text"},
    {"sourceField": "CloseDate", "targetType": "Date"},
    {"sourceField": "Amount", "targetType": "Number"},
    {"sourceField": "LeadSource", "targetType": "Category"}
  ]
}
Question 8mediummultiple choice
Full question →

Refer to the exhibit. A data scientist tries to query the dataset but receives an error. Which of the following is the most likely cause?

Exhibit

{
  "dataAccessPolicy": {
    "dataset": "customer_transactions",
    "allowedUsers": ["data_scientist", "ml_engineer"],
    "deniedUsers": ["intern"],
    "fields": ["transaction_id", "amount", "date"],
    "conditions": {
      "amount": {"gt": 0, "lt": 10000}
    }
  }
}
Question 9easymultiple choice
Full question →

Refer to the exhibit. What data quality issue does the exhibit reveal?

Exhibit

SELECT Campaign__c, COUNT(*) as Records, COUNT(Response__c) as NonNullResponse
FROM UnifiedCustomer
GROUP BY Campaign__c

Results:
Campaign__c | Records | NonNullResponse
Summer Sale  | 1000    | 750
Spring Promo | 800     | 800
Fall Clearance | 600    | 0
Question 10easymultiple choice
Full question →

Refer to the exhibit. What is the most likely cause of the pipeline failure?

Exhibit

Pipeline: MyPipeline
Status: FAILED
Error: RecordsProcessed: 5000, Errors: 120
LastError: FIELD_INTEGRITY_EXCEPTION: CustomLeadField__c: value not found in picklist values
Question 11mediummultiple 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 12hardmultiple choice
Full question →

Refer to the exhibit. A developer runs this SOQL query to prepare data for Einstein Lead Scoring. The query returns an error. What is the most likely issue?

Exhibit

SELECT AccountId, SUM(Amount) TotalAmount
FROM Opportunity
WHERE CloseDate > LAST_N_DAYS:365
GROUP BY AccountId
HAVING TotalAmount > 100000
Question 13easymultiple choice
Full question →

Refer to the exhibit. A dataflow is set up to prepare data for a prediction model. The model is expected to predict close probability for all open opportunities. What is wrong with this dataflow?

Exhibit

{
  "dataflow": "EinsteinDataPrep_Sales",
  "nodes": [
    {
      "id": 1,
      "type": "source",
      "object": "Opportunity"
    },
    {
      "id": 2,
      "type": "transform",
      "operation": "filter",
      "condition": "StageName = 'Closed Won'"
    },
    {
      "id": 3,
      "type": "output",
      "target": "EinsteinDiscovery_Stage"
    }
  ]
}
Question 14easymultiple choice
Full question →

Refer to the exhibit. An admin sees this error in the Einstein activity log. What is the most likely cause?

Exhibit

ERROR [2025-03-15 14:32:01] : Model output contains PII. Data masking is enabled but sentiment analysis is not. Check Trust Layer settings.
Question 15hardmultiple choice
Full question →

Refer to the exhibit. An AI model's accuracy is shown for four demographic groups. Which group should be investigated for potential bias?

Exhibit

Accuracy by group:
  - Group Alpha: 0.95
  - Group Beta: 0.70
  - Group Gamma: 0.93
  - Group Delta: 0.91

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