CCNA AI Fundamentals Questions — Page 3 of 3

151

Multi-Selectmedium

A company uses a generative AI model to create marketing copy. They want to ensure the output is accurate and not misleading. Which TWO practices should they implement?

Select 2 answers

A.Increase the model's temperature setting to encourage creativity

B.Disable all content filtering to avoid blocking useful output

C.Always have a human review the generated content before publishing

D.Ground the model with a knowledge base of verified facts

E.Use the model's output directly without checking

AnswersC, D

Human oversight catches hallucinations and inaccuracies.

Why this answer

Human review catches errors (hallucinations). Grounding the model in verified data reduces false information. Avoid over-reliance on the model without verification.

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152

MCQeasy

A company wants to use Einstein Bots to handle customer support queries. Which preparation is most important before deploying the bot?

A.Train all agents on how to monitor the bot.

B.Set up a new email channel for the bot.

C.Ensure the knowledge base articles are well-organized and cover common issues.

D.Create a custom object to store bot conversations.

AnswerC

Einstein Bots use Knowledge to answer queries.

Why this answer

Einstein Bots rely on a well-organized knowledge base to retrieve accurate answers for customer queries. Without properly structured articles covering common issues, the bot cannot effectively resolve tickets, leading to poor deflection rates and user frustration. This preparation directly impacts the bot's ability to understand and respond to natural language inputs using Salesforce's NLP and article matching.

Exam trap

Salesforce often tests the misconception that operational tasks (like agent training or data storage) are more critical than content preparation, leading candidates to overlook the foundational role of knowledge base organization in bot success.

How to eliminate wrong answers

Option A is wrong because training agents to monitor the bot is a post-deployment operational task, not a prerequisite for deployment; the bot's core functionality depends on knowledge base readiness, not agent oversight. Option B is wrong because Einstein Bots operate on existing channels like web chat or messaging services (e.g., Facebook Messenger), not a dedicated email channel; setting up a new email channel is irrelevant and not required for bot deployment. Option D is wrong because storing bot conversations in a custom object is an optional reporting or compliance feature, not a mandatory preparation; the bot uses standard Salesforce objects like Case and Chat Transcript by default.

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153

MCQmedium

A sales director wants to use AI to prioritize leads that are most likely to convert. The company has historical data on leads that includes whether they converted (yes/no) and various attributes. Which machine learning type should be used?

A.Generative AI

B.Reinforcement learning

C.Unsupervised learning (clustering)

D.Supervised learning (classification)

AnswerD

Correct: supervised classification uses labeled conversion outcomes to predict new leads.

Why this answer

Lead scoring is a binary classification problem (convert or not) using historical labeled data, which is supervised learning.

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154

MCQeasy

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should the admin enable?

A.Einstein Lead Scoring

B.Einstein Activity Capture

C.Einstein Prediction Builder

D.Einstein Bot

AnswerA

Einstein Lead Scoring directly scores leads based on conversion likelihood.

Why this answer

Einstein Lead Scoring uses historical data to predict lead conversion probability and assign scores, enabling prioritization.

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155

MCQeasy

A company uses an AI model to classify customer support cases into categories. The model often misclassifies cases from a specific region, leading to longer resolution times. What is the most likely cause?

A.The model is underfitted

B.The model uses too many features

C.The model is overfitted

D.The training data lacks diversity for that region

AnswerD

Machine learning models learn from data; if a region is underrepresented, the model may not learn its patterns.

Why this answer

If the training data is not representative of all regions, the model will perform poorly on underrepresented groups.

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156

MCQhard

A financial services firm uses an AI model to approve small business loans. The model denies loans at a much higher rate for businesses owned by minorities, even when financial indicators are similar. What is the MOST likely cause?

A.Low recall for minority groups

B.Bias in training data

C.Hallucination in model predictions

D.Overfitting to historical data

AnswerB

If historical loan decisions were biased against minority-owned businesses, the model would learn and perpetuate that bias.

Why this answer

Bias in training data can cause the model to learn historical discrimination patterns. Overfitting would not cause systematic demographic differences. Hallucination is for generative AI.

Low recall may indicate the model misses some positive cases but doesn't explain the demographic disparity.

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157

MCQeasy

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?

A.The model lacks confidence scoring

B.The algorithm choice (XGBoost) is inappropriate

C.Accuracy is not a reliable metric because the data is imbalanced

D.The training data size is insufficient

AnswerC

With 80% won leads, a constant 'won' prediction yields 80% accuracy, so accuracy does not measure model's discriminative power.

Why this answer

Correct: Accuracy is misleading due to class imbalance; a model that always predicts 'won' would get 80% accuracy. Option A: Data size is fine. Option B: XGBoost is good for tabular data.

Option D: Confidence score not provided.

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158

MCQeasy

A marketing manager wants to use AI to recommend next-best actions for customers based on their previous purchases. Which Einstein feature is most appropriate?

A.Einstein Discovery

B.Einstein Bot

C.Einstein Prediction Builder

D.Einstein Recommendations

AnswerD

Recommendations uses AI to suggest relevant items or actions.

Why this answer

Einstein Recommendations is the correct choice because it is specifically designed to analyze customer purchase history and behavioral data to suggest next-best actions, such as products or content, in real time. It uses collaborative filtering and deep learning models to generate personalized recommendations, directly matching the use case of suggesting actions based on previous purchases.

Exam trap

The trap here is that candidates confuse Einstein Prediction Builder (which outputs a prediction score) with Einstein Recommendations (which outputs a specific action or item), leading them to select Prediction Builder for a 'next-best action' scenario when it only predicts likelihoods, not suggestions.

How to eliminate wrong answers

Option A is wrong because Einstein Discovery is a predictive analytics tool that identifies patterns and explains why outcomes occur, but it does not generate next-best action recommendations for individual customers. Option B is wrong because Einstein Bot is a conversational AI for automating customer service interactions via chatbots, not for analyzing purchase history to recommend actions. Option C is wrong because Einstein Prediction Builder allows users to create custom predictive models (e.g., predicting churn or conversion), but it outputs a prediction score, not a recommended next action.

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159

MCQmedium

A sales team wants to prioritize leads that are most likely to convert. They have historical data on lead attributes and conversion outcomes. Which AI technique should be used?

A.Unsupervised clustering to group leads by similarity

B.Supervised learning to build a lead scoring model

C.Natural language processing to analyze lead emails

D.Computer vision to analyze lead profile pictures

AnswerB

Supervised learning uses labeled historical data to predict a target outcome, perfect for lead scoring.

Why this answer

Lead scoring uses supervised learning on historical lead data to predict conversion probability.

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160

Multi-Selecthard

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Select 3 answers

A.Generating report charts and dashboards.

B.Summarizing case conversations from service interactions.

C.Automatically generating personalized customer emails.

D.Creating call scripts for sales reps based on opportunity data.

E.Creating workflow rules based on user behavior.

AnswersB, C, D

GPT can summarize lengthy case threads.

Why this answer

Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

Exam trap

Salesforce often tests the distinction between generative AI capabilities (like content creation and summarization) and traditional CRM features (like reporting or workflow automation), so candidates mistakenly select options that describe standard Salesforce functions rather than Einstein GPT's specific generative AI outputs.

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161

MCQeasy

A company is implementing Einstein Activity Capture. Users have enabled the feature, but emails are not being automatically logged. Which configuration should the administrator verify first?

A.Check that the users' email clients are supported.

B.Verify that Email-to-Salesforce is enabled.

C.Confirm that the Einstein Activity Capture permission set is assigned to users.

D.Ensure users have the 'Log a Task' permission.

AnswerC

The permission set is necessary for the feature to function.

Why this answer

Einstein Activity Capture requires the dedicated permission set to be assigned to users before it can automatically log emails and events. Without this permission set, the feature is enabled at the org level but users lack the necessary access rights to capture activities, so verifying this assignment is the first logical step.

Exam trap

The trap here is that candidates confuse Einstein Activity Capture with Email-to-Salesforce or client-side logging tools, leading them to check email client support or Email-to-Salesforce settings instead of the permission set assignment.

How to eliminate wrong answers

Option A is wrong because Einstein Activity Capture works with any email client that supports IMAP or Exchange Web Services; it does not require a specific supported client list. Option B is wrong because Email-to-Salesforce is a separate feature for logging emails via a generated address, not a prerequisite for Einstein Activity Capture, which uses server-side sync. Option D is wrong because the 'Log a Task' permission is not required for Einstein Activity Capture; the feature creates activity records automatically without needing manual task logging permissions.

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162

MCQhard

A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?

A.Einstein Trust Layer with model explainability features.

B.Data Mask to protect sensitive information.

C.Salesforce Shield with encryption and monitoring.

D.Field Audit Trail to track changes to data.

AnswerA

The Trust Layer includes capabilities to explain predictions.

Why this answer

The Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.

Exam trap

Salesforce often tests the distinction between data security features (like Data Mask or Shield) and AI explainability features, so candidates mistakenly choose a security-focused option when the question explicitly asks about understanding model predictions.

How to eliminate wrong answers

Option B is wrong because Data Mask is a feature for obfuscating sensitive data in non-production environments, not for explaining AI predictions. Option C is wrong because Salesforce Shield provides encryption, field audit trails, and event monitoring for data security and compliance, but it does not offer model explainability or interpretability for AI predictions. Option D is wrong because Field Audit Trail tracks changes to field values over time for data governance, not for understanding why an AI model made a specific prediction.

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163

MCQhard

A financial services firm uses an AI model to approve loan applications. They discover the model denies loans at a higher rate for a protected demographic. What is the most likely root cause?

A.The model is overfitted

B.The training data contains historical bias

C.The model uses too few features

D.The model has low precision

AnswerB

If historical loan decisions were biased, the model will learn that bias.

Why this answer

Historical bias in training data can cause models to learn and perpetuate discrimination.

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