AI Associate AI Capabilities in CRM Practice Question
This AI Associate practice question tests your understanding of ai capabilities in crm. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Exhibit
C:\> sfdx force:data:soql:query -q "SELECT Id, Score__c FROM Lead WHERE CreatedDate = TODAY"
Rows: 2000
Time: 4.2s
C:\> sfdx force:data:soql:query -q "SELECT Id, Score__c FROM Lead WHERE CreatedDate = LAST_N_DAYS:30"
Rows: 45000
Time: 28.7s
Refer to the exhibit. An admin runs two queries on the Lead object. Both include the custom field Score__c used by Einstein Lead Scoring. The second query is significantly slower. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The query is not using a selective filter on Score__c
Option A is correct because Einstein Lead Scoring uses the Score__c field to store lead scores, and the field is not indexed by default. When a query filters on Score__c without an index, it forces a full table scan on the Lead object, which becomes significantly slower as the number of leads grows. The second query likely includes a non-selective filter on Score__c (e.g., a range or inequality), which cannot leverage any existing index and thus degrades performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
The query is not using a selective filter on Score__c
Why this is correct
Large result set without index on Score__c causes slow performance.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The query uses a date function that is not selective
Why it's wrong here
LAST_N_DAYS:30 is selective enough; the issue is row count and Score__c.
✗
The Score__c field is indexed
Why it's wrong here
Indexed fields would speed up, not slow down.
✗
The Einstein Lead Scoring job is running simultaneously
Why it's wrong here
No evidence of concurrent job.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that Einstein features automatically index their underlying fields, when in fact custom fields like Score__c are not indexed by default and require manual indexing for query performance.
Detailed technical explanation
How to think about this question
Under the hood, Salesforce uses selective filters to leverage indexed fields and avoid full table scans. A filter on Score__c is non-selective when it matches more than 30% of the first 1 million records or more than 15% of records beyond that threshold, causing the query optimizer to skip the index and perform a full scan. In practice, admins should avoid filtering on unindexed custom fields in large data volumes or use custom indexes if the field is frequently queried.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Capabilities in CRM — This question tests AI Capabilities in CRM — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The query is not using a selective filter on Score__c — Option A is correct because Einstein Lead Scoring uses the Score__c field to store lead scores, and the field is not indexed by default. When a query filters on Score__c without an index, it forces a full table scan on the Lead object, which becomes significantly slower as the number of leads grows. The second query likely includes a non-selective filter on Score__c (e.g., a range or inequality), which cannot leverage any existing index and thus degrades performance.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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