AI Associate Practice Test 32 — 15 Questions

Question 1

A user asks an AI assistant to generate content that may be offensive. What should the AI do?

Accepted Answer

Refuse and explain why. The AI should refuse the request and explain why, upholding ethical standards.

Answer

Ignore the request

Answer

Generate and report the user

Answer

Generate with a warning

Question 2

Which statement best describes 'inference' in the context of machine learning?

Accepted Answer

The process of using a trained model to make predictions on new data. In machine learning, inference (also called prediction or scoring) is the phase where a previously trained model is applied to new, unseen data to generate outputs such as classifications, probabilities, or regression values. It is distinct from training, which learns parameters from labeled data, and from data preparation or evaluation, which occur at different stages of the ML lifecycle. Inference is what delivers business value — the model's learned patterns are used in production to make decisions.

Answer

The process of training a model on labeled data

Answer

The process of collecting and preparing data

Answer

The process of evaluating a model's accuracy

Question 3

A company implements Einstein Opportunity Scoring and wants to understand where the opportunity scores appear in the Salesforce Lightning interface. Which two locations display the score?

Accepted Answer

List view column. Einstein Opportunity Scoring can be added as a column in list views, allowing users to see the score for each opportunity directly in the list. Option D is correct because the score is also displayed on the opportunity record page via a dedicated component, providing detailed scoring insights at the record level.

Answer

Global search results

Answer

Home page sidebar

Answer

Report chart

Question 4

A company wants to use AI to automatically extract key information (e.g., invoice number, date, total amount) from scanned PDF invoices. Which AI capability should they use?

Accepted Answer

Computer vision with optical character recognition (OCR). Computer vision with OCR is the correct capability because it combines image processing to detect and extract text from scanned documents (invoices) with optical character recognition to convert that text into machine-readable form, enabling extraction of fields like invoice number, date, and total amount. This is a classic document AI use case.

Answer

Generative AI for text generation

Answer

Predictive analytics

Answer

Sentiment analysis

Question 5

What is the primary purpose of the Einstein Copilot feature in Salesforce?

Accepted Answer

To provide a conversational AI assistant within the CRM. Einstein Copilot is a conversational AI assistant embedded directly in the Salesforce CRM interface. It allows users to ask natural-language questions, get summaries of records, generate field values, and execute actions without navigating menus. This aligns with option D, as its primary purpose is to provide an AI-powered chat experience within the CRM, not to automate logging, analyze calls, or generate marketing campaigns.

Answer

To automatically log emails and events

Answer

To analyze call recordings for talk-time metrics

Answer

To generate marketing email campaigns

Question 6

An AI Associate deploys an Einstein Bot that uses sentiment analysis to escalate frustrated customers. After launch, the bot escalates disproportionately for non-native English speakers. What is the most likely cause?

Accepted Answer

The sentiment model was trained on a non-representative dataset.. The sentiment analysis model likely exhibits bias due to training data that does not adequately represent the linguistic patterns, idioms, or expressions of non-native English speakers. This causes the model to misinterpret neutral or positive statements from these users as negative or frustrated, leading to disproportionate escalations. A non-representative dataset is a common source of algorithmic bias in AI systems.

Answer

The bot is routing to the wrong department.

Answer

The escalation threshold is set too low.

Answer

The bot is not properly connected to the escalation queue.

Question 7

A company wants to use AI to automatically extract invoice numbers, dates, and totals from scanned invoices. Which AI capability is MOST relevant?

Accepted Answer

Computer vision with entity extraction. Computer vision can process images of documents, and entity extraction (often part of NLP) identifies specific data points. Sentiment analysis is for emotions, intent detection for purpose, and generative AI for content creation.

Answer

Intent detection

Answer

Generative AI to create invoices

Answer

Sentiment analysis

Question 8

A company is developing an AI system to assist with hiring. Which TWO practices are essential for ethical AI deployment?

Accepted Answer

Conduct regular bias audits on model predictions. Option C is correct because regular bias audits on model predictions are essential to detect and mitigate disparate impact across protected groups, using metrics such as demographic parity or equalized odds to verify the model does not systematically disadvantage any group. Option E is correct because obtaining informed consent from applicants when their data is used respects data-subject autonomy and complies with privacy regulations like GDPR, which require lawful bases and transparency for processing personal data in automated decision-making. Options A, B, and D do not belong: optimizing for speed (A) addresses latency, not ethics; removing all demographic features (B) does not guarantee fairness because proxy variables can still encode bias; and maximizing accuracy on historical hiring data (D) can perpetuate and amplify past discriminatory patterns rather than promote ethical outcomes.

Answer

Optimize the system for speed to reduce waiting times

Answer

Remove all demographic features to ensure fairness

Answer

Maximize accuracy on historical hiring data

Question 9

A sales operations manager wants to compare the AI-predicted forecast against the sales rep's manual commit for the current quarter. Which Einstein feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the specific feature designed to compare AI-predicted forecasts against sales reps' manual commitments for the current quarter. It provides a side-by-side view of the AI-generated forecast and the human-entered commit, enabling managers to identify discrepancies and adjust strategies accordingly.

Answer

Einstein Opportunity Scoring

Answer

Einstein Lead Scoring

Answer

Einstein Prediction Builder

Question 10

A company is developing an AI system that makes loan approval decisions. Under GDPR, customers have the right to request an explanation of how the decision was made. Which Salesforce feature provides this explanation?

Accepted Answer

Einstein Discovery prediction explanations. Einstein Discovery prediction explanations is correct because it provides human-readable, factor-by-factor explanations of how a model arrived at a specific prediction, including the relative influence of each contributing variable. This directly satisfies GDPR Article 22's requirement for meaningful information about the logic involved in automated decisions. Salesforce exposes these explanations through the Einstein Discovery prediction explanation feature, which can be surfaced in CRM records and APIs.

Answer

Einstein Bots

Answer

Einstein Trust Layer audit trail

Answer

Einstein Next Best Action

Question 11

Refer to the exhibit. A company uses an AI model for loan approvals. The error log shows a drift warning for a specific zip code, followed by a retraining failure due to insufficient data. What is the MOST ethical concern?

Accepted Answer

The model may produce biased outcomes for underserved groups. The drift warning indicates that the model's performance has degraded for a specific zip code, likely due to changes in the underlying data distribution. When retraining fails due to insufficient data, the model cannot adapt to these changes, which can lead to biased outcomes for underserved groups in that zip code. This is the most ethical concern because it directly impacts fairness and equity in automated decision-making.

Answer

The system failed to log the error

Answer

The system ignored the drift warning

Answer

The retraining process is too slow

Question 12

A financial services company is deploying Einstein Prediction Builder to predict loan default risk. They are concerned about using sensitive attributes like race or gender in the model. Which data governance practice should they apply?

Accepted Answer

Exclude sensitive attributes from the model features unless they are essential and legally permitted, and ensure no proxies exist.. The correct governance practice is to exclude sensitive attributes such as race or gender from model features unless they are essential and legally permitted, and to verify that no proxy variables (e.g., ZIP code, name) indirectly encode them. This reduces disparate impact and aligns with fair lending regulations like ECOA and GDPR. Simply masking or including them still allows the model to learn discriminatory patterns.

Answer

Use synthetic data to replace sensitive attributes with random values.

Answer

Include all available attributes to maximize model accuracy, then apply fairness constraints.

Answer

Mask the sensitive attributes but still include them in the model training.

Question 13

Refer to the exhibit. A data transformation configuration is shown. Which of the following describes the outcome of applying this transformation?

Accepted Answer

'color' is one-hot encoded into multiple binary columns; 'price' and 'weight' are standardized to have mean 0 and variance 1.. The transformation configuration applies a one-hot encoder to the 'color' categorical column, creating multiple binary columns, and applies a standard scaler to the 'price' and 'weight' numerical columns, centering them to mean 0 and scaling to unit variance. This is a common preprocessing pipeline that handles mixed data types appropriately.

Answer

The transformation is invalid because one-hot encoding cannot be combined with scaling.

Answer

Only 'color' is transformed; 'price' and 'weight' are unchanged.

Answer

'color' is scaled to [0,1] and 'price', 'weight' are one-hot encoded.

Question 14

A predictive model for opportunity scoring shows high precision but low recall. Which business impact is most likely?

Accepted Answer

The sales team misses some convertible opportunities. High precision means that when the model predicts an opportunity will convert, it is usually right — so the sales team is not wasting time on false positives. Low recall means the model misses many opportunities that would actually convert, so the sales team fails to pursue some winnable deals. The business impact is therefore missed convertible opportunities.

Answer

The model is overfitting to the training data

Answer

The sales team wastes time on many low-quality opportunities

Answer

The model overestimates the likelihood of conversion for most opportunities

Question 15

A company wants to use Einstein Forecasting to improve sales predictions. Which TWO statements about Einstein Forecasting are correct?

Accepted Answer

It provides AI-enhanced forecast predictions beyond manager rollups. Option D is correct because Einstein Forecasting applies AI/ML to generate predictive forecasts that go beyond the standard collaborative forecasting rollups managers see, using historical opportunity and pipeline data to project outcomes. Option E is correct because Einstein Forecasting surfaces both the AI-predicted forecast and the rep's commit/submitted forecast side by side, letting users compare the machine prediction against the human judgment. Option A is incorrect because Einstein Forecasting is not limited to Service Cloud; it is used with Sales Cloud (and available in related clouds), not exclusively Service Cloud. Option B is incorrect because automatic quota adjustment for reps is not a function of Einstein Forecasting; quotas are managed separately and are not auto-tuned by the forecasting AI. Option C is incorrect because Einstein Forecasting does not replace CRM Analytics (Tableau CRM) reporting; it is a forecasting feature and coexists with, rather than substitutes for, CRM Analytics.

Answer

It is only available for Service Cloud

Answer

It automatically adjusts quotas for reps

Answer

It replaces CRM Analytics for all reporting