AI Associate Practice Test 38 — 15 Questions

Question 1

A financial services firm uses Data Cloud to enrich sales data with external credit scores via an API. They set up a Data Action to call the credit bureau API for each new lead. Over time, API costs are rising, and the action is slowing down lead processing. They only need credit scores for leads with a high probability of conversion. What is the best approach to reduce costs and improve performance?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use a Calculated Insight to score leads based on internal data and only invoke the Data Action for leads with a high probability of conversion. A Calculated Insight can score leads using existing internal data, and the Data Action is then invoked only for leads that meet the high-probability-of-conversion threshold, directly reducing the number of external credit bureau API calls and therefore cost and processing latency. This conditional invocation targets the stated business need: credit scores are only required for high-conversion leads. Option A does not reduce API costs or improve performance because it removes automation and relies on manual work. Option B filters only on incomplete data, which is unrelated to conversion probability and may still call the API for many low-value leads. Option C changes timing to daily batch but still calls the API for every lead, so it does not reduce the number of paid API calls.

Answer

Remove the Data Action and manually verify credit scores for top leads

Answer

Apply a Data Transform to filter leads that have incomplete data before the Data Action

Answer

Schedule the Data Action to run daily in batch instead of real-time

Question 2

Refer to the exhibit. A data pipeline fails during the DataTransformation stage. What is the most likely root cause?

Accepted Answer

A transformation step references the 'age' column, but it is not present in the input data.. The error occurs during the DataTransformation stage, which processes data after it has been successfully ingested. If a transformation step references the 'age' column but that column is missing from the input data, the pipeline will fail with a column-not-found error. This is a common schema mismatch issue in data pipelines, distinct from connectivity or data quality problems.

Answer

The pipeline has a network connectivity issue.

Answer

The data type for 'income' is incorrect.

Answer

The 'age' column contains null values.

Question 3

A company uses Einstein Lead Scoring and finds that leads with high scores are not converting. What should the admin do to improve prediction accuracy?

Accepted Answer

Retrain the model with more recent conversion data. Retraining the model with more recent conversion data allows the Einstein Lead Scoring model to adapt to changing patterns in lead behavior and conversion criteria. When high-scoring leads fail to convert, it indicates that the historical data used to train the model no longer reflects current conversion dynamics, so refreshing the training dataset improves prediction accuracy by aligning the model with recent outcomes.

Answer

Increase the scoring model's maximum score

Answer

Disable field-level security for scoring fields

Answer

Lower the lead conversion threshold

Question 4

A data architect is designing a data model for Einstein Discovery. The data includes categorical variables with high cardinality (e.g., postal codes). What is the best practice to handle such features?

Accepted Answer

Group them into higher-level categories (e.g., region).. High-cardinality categorical variables such as postal codes create sparse, noisy features that can cause overfitting and degrade model performance in Einstein Discovery. Grouping them into higher-level categories (e.g., region, state, or country) reduces cardinality while preserving meaningful geographic signal, which is the recommended best practice. This produces more generalizable models and improves the quality of the story and predictions.

Answer

Encode them as one-hot vectors.

Answer

Exclude them from the model.

Answer

Use the raw values without transformation.

Question 5

A financial services company uses Salesforce Einstein to predict which customers are likely to default on loans. The model was trained on historical loan data that includes sensitive attributes such as race and gender. An audit reveals that the model's predictions disproportionately reject applicants from certain demographic groups. Which ethical principle is most directly violated by this outcome?

Accepted Answer

Fairness. The model's biased predictions against certain demographic groups violate the ethical principle of fairness, which demands equitable treatment and non-discrimination. In AI, fairness ensures that models do not perpetuate historical biases or create unjust outcomes. This is especially critical in loan approvals, where biased decisions can have severe financial and social consequences.

Answer

Privacy

Answer

Transparency

Answer

Accountability

Question 6

A company is configuring Einstein Lead Scoring. Which TWO statements accurately describe how the feature works?

Accepted Answer

It assigns a score between 1 and 99 indicating conversion likelihood.. Option A is correct because Einstein Lead Scoring produces a predictive score on a 1–99 scale that reflects each lead's likelihood of converting, with higher numbers indicating greater conversion probability. Option D is correct because that score is exposed as a standard field on the Lead object, so users can sort, filter, and prioritize leads in list views and reports to focus on the highest-scoring records. Option B is incorrect because Einstein Lead Scoring works on leads regardless of source, including web, manual, and imported leads, not only externally imported ones. Option C is incorrect because the feature scores leads for conversion likelihood, not opportunities by deal size. Option E is incorrect because Einstein Lead Scoring is a predictive AI model that learns from historical data rather than requiring admins to hand-build scoring rules.

Answer

It only works with leads imported from external systems.

Answer

It scores opportunities based on deal size.

Answer

It requires manual configuration of scoring rules by an admin.

Question 7

Which Einstein feature provides AI-enhanced forecast predictions that go beyond manager rollups and can compare AI forecast to rep commitments?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct answer because it uses AI to generate predictive forecasts that go beyond traditional manager rollups, and it allows users to compare the AI-generated forecast against individual rep commitments. This feature leverages historical data and patterns to provide more accurate predictions, enabling sales leaders to identify discrepancies between AI insights and human inputs.

Answer

Einstein Opportunity Scoring

Answer

Einstein Discovery

Answer

Einstein Lead Scoring

Question 8

A company uses Einstein Discovery to build a model that predicts customer lifetime value. They want to ensure the model's decisions are auditable and that they can track the business impact of AI recommendations over time. What should they implement?

Accepted Answer

Audit trail. An audit trail logs when AI recommendations are made and whether they were followed, and can be used to track outcomes over time. This enables accountability and measurement of AI impact, supporting transparency and governance.

Answer

Score factors

Answer

Grounding

Answer

Toxicity detection

Question 9

A sales team wants to use Einstein GPT for Sales to generate call summaries. Which two data sources can be used to populate the call summary prompt?

Accepted Answer

Related Opportunity or Account fields. Option C is correct because Einstein GPT for Sales can pull CRM record data such as related Opportunity and Account fields to ground the call summary prompt in the relevant sales context. Option D is correct because the call recording transcript generated by Einstein Conversation Insights is a primary input for producing call summaries in Einstein GPT for Sales. Options A, B, and E are not the intended data sources for this feature: Case details belong to Service Cloud case management, Einstein Email Insights focuses on email engagement signals rather than call transcripts, and Einstein Discovery stories are predictive analytics outputs, not call-summary prompt inputs.

Answer

Case details from Service Cloud

Answer

Email threads from Einstein Email Insights

Answer

Einstein Discovery stories

Question 10

A company wants to use AI to reduce customer churn. Which TWO approaches are most appropriate? (Select 2)

Accepted Answer

Use predictive AI to score churn risk based on historical data. Option A is correct because predictive AI models (e.g., classification algorithms like logistic regression or gradient boosting) trained on historical customer data such as usage patterns, tenure, and billing history can output a churn probability score, directly enabling proactive retention targeting. Option E is correct because sentiment analysis (a natural language processing technique) applied to support tickets, chat logs, or call transcripts can surface dissatisfaction signals that often precede churn, giving an additional leading indicator beyond structured data. Option B is not the best fit because generative AI creating retention offers addresses the response after churn risk is identified, not the core churn-reduction analytics the scenario asks for, and it depends on first knowing who is at risk. Option C is irrelevant since computer vision analyzing customer photos has no meaningful connection to churn prediction. Option D is also off-target because reinforcement learning to train a chatbot focuses on conversational performance, not on identifying or reducing customer churn.

Answer

Use generative AI to create personalized retention offers

Answer

Use computer vision to analyze customer photos

Answer

Use reinforcement learning to train a chatbot

Question 11

A Service Cloud admin wants to deploy a chatbot that can handle common customer requests and escalate to a human agent when necessary. The chatbot must understand natural language variations. Which combination of tools should they use?

Accepted Answer

Einstein Bots with intents and entities, and handoff to agent. Einstein Bots is Salesforce's purpose-built chatbot platform that uses natural language processing to match user utterances to configured intents and extract entities (e.g., order numbers, dates). It natively supports handoff to a human agent via Omni-Channel routing when the bot cannot resolve a request or when the user asks for a person. This combination directly satisfies the requirement for NLU-driven handling plus escalation.

Answer

Einstein Case Classification with auto-response

Answer

Einstein Copilot with custom actions

Answer

Einstein Next Best Action with flows

Question 12

A call center manager wants to analyze recorded sales calls to identify keywords, measure talk-time, and capture follow-up tasks. Which Einstein feature provides these capabilities?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct answer because it is specifically designed to analyze recorded sales calls by transcribing conversations, identifying keywords and topics, measuring talk-time, and capturing follow-up tasks. It uses natural language processing (NLP) to extract actionable insights from voice and text interactions, directly matching the requirements in the question.

Answer

Einstein Discovery

Answer

Einstein Activity Capture

Answer

Einstein Email Insights

Question 13

When implementing AI in Salesforce, which practice best supports the ethical principle of transparency?

Accepted Answer

Provide human-readable explanations for each AI prediction. Transparency in AI ethics requires that users understand how and why an AI system reached a decision. Providing human-readable explanations for each AI prediction directly enables stakeholders to see the reasoning behind outputs, which is the operational definition of transparency. Options that hide logic or only report aggregate accuracy fail to give individuals the insight needed to trust or contest a decision.

Answer

Use proprietary algorithms without disclosing their logic

Answer

Deploy a complex neural network model without interpretability features

Answer

Only report overall model accuracy metrics to end users

Question 14

A company uses an AI model to screen job applicants. They discover the model is rejecting candidates from a certain demographic at a higher rate. Which ethical principle is most clearly violated?

Accepted Answer

Fairness. Fairness in AI requires that models do not produce discriminatory outcomes across demographic groups. A hiring model that rejects one demographic at a higher rate exhibits disparate impact, which is the textbook definition of an unfair or biased model. The other principles — transparency, privacy, and accountability — address disclosure, data handling, and responsibility, not unequal treatment.

Answer

Transparency

Answer

Privacy

Answer

Accountability

Question 15

A sales team uses an AI model to prioritize leads. The model's predictions are not improving despite adding more data. Which THREE factors could explain this? (Choose three.)

Accepted Answer

The new data is noisy or of low quality. Option A is correct because adding noisy or low-quality data introduces label errors and irrelevant variance that degrade generalization, so more data cannot improve predictions and may even hurt them. Option B is correct because an underfitting model lacks the capacity (e.g., too few parameters or too simple a hypothesis class) to capture the underlying relationship, so additional data alone will not raise accuracy. Option D is correct because if the input features have little or no predictive signal for lead conversion, no amount of extra data can teach the model a useful mapping. Option C is not correct here because overfitting would typically show strong training performance with poor validation, and the scenario describes stagnant predictions rather than a train/validation gap. Option E is not correct because merely adding more features is not a guaranteed fix; irrelevant or redundant features can worsen performance, and the question asks about factors explaining the lack of improvement.

Answer

The model is already overfitting to the training data

Answer

The team has not added enough features to the model