Troubleshooting practice questions

A Salesforce admin is troubleshooting an Einstein Prediction Builder model that is not generating predictions. The model was created with a custom object 'Feedback__c'. The admin notices that the model's data source includes records with status 'In Progress' and 'Closed'. What is the most likely cause of the model not generating predictions?

Trap 1: The data source is not refreshed daily

Data refresh frequency does not prevent model from generating predictions; it affects recency.

Trap 2: The object is a custom object

Custom objects are fully supported by Prediction Builder.

Trap 3: The object has fewer than 1000 records

Prediction Builder requires at least 100 rows, not 1000.

A
The data source is not refreshed daily
Why wrong: Data refresh frequency does not prevent model from generating predictions; it affects recency.
B
The object is a custom object
Why wrong: Custom objects are fully supported by Prediction Builder.
C
The outcome field has more than two unique values
Einstein Prediction Builder requires a binary outcome; status with 'In Progress' creates three distinct values.
D
The object has fewer than 1000 records
Why wrong: Prediction Builder requires at least 100 rows, not 1000.

Question 2mediummulti select

A data analyst is troubleshooting Einstein Article Recommendations that are not showing up on the site. Which TWO checks should be performed first? (Choose 2)

Trap 1: Ensure at least 100 articles are in the knowledge base

No strict minimum; even fewer articles can work if views exist.

Trap 2: Confirm that article authors have the correct profile permissions

Author permissions do not affect recommendation display.

Trap 3: Increase the recommendation frequency from daily to hourly

Frequency is not usually the cause of missing recommendations.

A
Ensure at least 100 articles are in the knowledge base
Why wrong: No strict minimum; even fewer articles can work if views exist.
B
Confirm that article authors have the correct profile permissions
Why wrong: Author permissions do not affect recommendation display.
C
Check that article view events are being captured in the data
Without view data, the model has no basis to recommend.
D
Increase the recommendation frequency from daily to hourly
Why wrong: Frequency is not usually the cause of missing recommendations.
E
Verify that the recommendation model is published and active
An unpublished model will not serve recommendations.

Question 3hardmultiple choice

An admin notices that Einstein Opportunity Scoring is not generating scores for new opportunities created in the past week. Which troubleshooting step should the admin take first?

Trap 1: Retrain the Opportunity Scoring model

Retraining does not address missing historical data.

Trap 2: Verify that users have the 'View Einstein Scores' permission

Permissions affect visibility, not score generation.

Trap 3: Wait 48 hours for the model to update

Waiting does not resolve data insufficiency.

A
Retrain the Opportunity Scoring model
Why wrong: Retraining does not address missing historical data.
B
Verify that users have the 'View Einstein Scores' permission
Why wrong: Permissions affect visibility, not score generation.
C
Check that there are at least 50 won and 50 lost opportunities with populated fields
Einstein models require a minimum of 50 won and 50 lost records to generate scores.
D
Wait 48 hours for the model to update
Why wrong: Waiting does not resolve data insufficiency.

Question 4hardmultiple choice

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.

Trap 1: The connected email service is not authenticated for the…

Authentication error would be different.

Trap 2: The contact with ID 003B0000007v4ZW does not have a valid email…

Email exists but domain not configured.

Trap 3: The user 'admin@mycompany.com' has exceeded their email capture…

Quota warning is not the cause.

A
The connected email service is not authenticated for the organization.
Why wrong: Authentication error would be different.
B
The email domain 'acme.org' needs to be added to the Einstein Activity Capture allowed domains.
Domain not configured causes failures.
C
The contact with ID 003B0000007v4ZW does not have a valid email address.
Why wrong: Email exists but domain not configured.
D
The user 'admin@mycompany.com' has exceeded their email capture quota.
Why wrong: Quota warning is not the cause.

Question 5hardmultiple choice

A Salesforce admin is troubleshooting Einstein Object Detection in a custom object. The model is predicting values, but the confidence score remains below 80% for most predictions. What should the admin investigate first?

Trap 1: The number of fields used as input features.

Feature count is less likely the primary cause.

Trap 2: The API version used in the integration.

API version affects connectivity, not confidence.

Trap 3: The permission set for the Einstein AI feature.

Permissions affect access, not output.

A
The number of fields used as input features.
Why wrong: Feature count is less likely the primary cause.
B
The API version used in the integration.
Why wrong: API version affects connectivity, not confidence.
C
The permission set for the Einstein AI feature.
Why wrong: Permissions affect access, not output.
D
The model training data size and distribution of values.
Insufficient or unrepresentative data lowers confidence.

Question 6mediummultiple choice

An admin is troubleshooting Einstein Sentiment. The model returns high confidence but wrong sentiment (e.g., positive reviews labeled negative). What is the most likely issue?

Trap 1: The model was not retrained after the last data load.

Retraining might be needed but wrong sentiment despite high confidence suggests labeling issues.

Trap 2: The training data contains predominantly neutral examples.

Imbalanced data can cause low confidence, not high confidence wrong predictions.

Trap 3: The field mapping for the sentiment field is incorrect.

Mapping issue would likely cause errors, not confident wrong predictions.

A
The model was not retrained after the last data load.
Why wrong: Retraining might be needed but wrong sentiment despite high confidence suggests labeling issues.
B
The training data contains predominantly neutral examples.
Why wrong: Imbalanced data can cause low confidence, not high confidence wrong predictions.
C
The training data has incorrect labels for sentiment.
Garbage in, garbage out: mislabeled training data leads to confident but incorrect classifications.
D
The field mapping for the sentiment field is incorrect.
Why wrong: Mapping issue would likely cause errors, not confident wrong predictions.

Question 7mediummultiple choice

A data engineer is troubleshooting a predictive model that stopped updating. The data flow from Data Cloud shows 'Data Transform Failed' with error: 'Field Amount cannot be null'. What is the most likely cause?

Trap 1: The data transform includes a filter that removes records with null…

A filter would remove nulls, not cause a failure; the error suggests nulls are being processed.

Trap 2: The source object has a validation rule.

Validation rules apply to UI record creation/update, not data flows.

Trap 3: The data flow schedule is incorrect.

The error is data-related, not schedule-related.

A
The data transform includes a filter that removes records with null Amount.
Why wrong: A filter would remove nulls, not cause a failure; the error suggests nulls are being processed.
B
The source object has a validation rule.
Why wrong: Validation rules apply to UI record creation/update, not data flows.
C
The data flow schedule is incorrect.
Why wrong: The error is data-related, not schedule-related.
D
The target field in the model requires a non-null value but source data has nulls.
This directly matches the error: the transform requires non-null input.