AI Associate Practice Test 5 — 25 Questions

Question 1

Which TWO data sources can be used with Einstein Prediction Builder?

Accepted Answer

Data Cloud objects using the harmonized data model.. Einstein Prediction Builder requires structured data that can be mapped to a prediction objective. Data Cloud objects using the harmonized data model provide a unified, standardized schema that Prediction Builder can consume directly, enabling predictions across multiple Salesforce and external data sources. Standard Salesforce objects like Account and Opportunity are also supported because they contain the fields and relationships needed to train predictive models.

Answer

Files uploaded to Salesforce Files.

Answer

Chatter feed posts.

Answer

Dashboard and report snapshots.

Question 2

A user asks Einstein GPT to generate a product description. The AI returns a response with a confidence score of 0.65. What does this score indicate?

Accepted Answer

The model is 65% confident that the response is accurate. The confidence score in AI models like Einstein GPT quantifies the model's internal certainty that its generated output is factually correct or contextually appropriate. A score of 0.65 means the model estimates a 65% probability that the response is accurate based on its training and inference algorithms, not that it matches training data or has a fixed length.

Answer

There is a 65% probability that the response exactly matches the training data

Answer

The response is 65% shorter than the optimal length

Answer

The model has a 65% likelihood of generating the same response again

Question 3

A customer service team deploys an Einstein Bot to handle common queries. During testing, the bot frequently fails to understand user intent, leading to poor responses. What should the team do FIRST to improve the bot's understanding?

Accepted Answer

Add more utterances to each intent and retrain the bot's NLP model. The primary way to improve intent recognition in an Einstein Bot is to provide more training data in the form of utterances (example phrases) for each intent. By adding diverse and representative utterances and retraining the NLP model, the bot learns to better map user language to the correct intent, directly addressing the failure to understand user intent.

Answer

Increase the number of entities in the entity definition

Answer

Enable handoff to a human agent for all queries

Answer

Review the bot analytics to see which intents fail

Question 4

Which THREE factors can affect the accuracy of an Einstein GPT response?

Accepted Answer

Model temperature setting. The model temperature setting directly controls the randomness of the output. A higher temperature (e.g., 0.9) produces more creative but potentially less accurate responses, while a lower temperature (e.g., 0.1) makes the model more deterministic and factual. This parameter is a core hyperparameter in large language models like those powering Einstein GPT, and it significantly influences response accuracy.

Answer

Brightness of the user interface

Answer

Length of the response generated

Question 5

In Salesforce CRM Analytics (formerly Einstein Analytics), what is the primary purpose of a dataset?

Accepted Answer

To prepare data for AI and analytics. In Salesforce CRM Analytics, a dataset is the foundational data structure that transforms raw data into an optimized, columnar format for analytics and AI features like Einstein Discovery. It is created by extracting, cleaning, and aggregating data from sources such as Salesforce objects or external connectors, enabling efficient querying, dashboarding, and machine learning model training. This makes option A correct because the primary purpose is to prepare data specifically for AI and analytics workloads.

Answer

To run SQL queries directly

Answer

To store raw, unprocessed records

Answer

To create dashboards only

Question 6

A company uses Einstein Prediction Builder to predict which leads will convert. They have a binary outcome field 'Converted__c' which is true for 8% of leads. After training, the model shows high accuracy (95%) but very low precision for the positive class. What is the most likely cause?

Accepted Answer

The data is imbalanced favoring the negative class. The dataset is imbalanced: only 8% of leads are positive (Converted__c = true), while 92% are negative. In such a scenario, a model can achieve 95% accuracy by simply predicting the majority class (negative) for all leads, but this yields very low precision for the positive class because it rarely predicts positive correctly. Einstein Prediction Builder, like most ML models, is sensitive to class imbalance, and without techniques like oversampling or threshold tuning, the model will favor the majority class.

Answer

The prediction field is not a binary field

Answer

The prediction score field is not configured correctly

Answer

The dataset is too small for training

Question 7

A data analyst is using Einstein Discovery to analyze customer churn. They want to understand the key drivers of churn and get actionable recommendations. Which THREE outputs does Einstein Discovery provide to meet this need?

Accepted Answer

Waterfall charts showing contribution of each variable. Waterfall charts in Einstein Discovery visually decompose the contribution of each predictor variable to the overall prediction, showing how much each driver increases or decreases the likelihood of churn. This directly helps the analyst identify the key drivers of churn, meeting the requirement to understand what factors are most influential.

Answer

Prediction scores for each record

Answer

A trained model for deployment

Question 8

Which TWO techniques are commonly used to handle missing values in a dataset for AI training?

Accepted Answer

Deletion of rows with missing values. Deleting rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This approach avoids introducing bias from imputation methods but can lead to loss of valuable information if too many rows are removed.

Answer

L1 regularization

Answer

One-hot encoding

Answer

Min-max normalization

Question 9

A healthcare AI model uses patient data. The legal team requires that all data used for training be de-identified according to HIPAA Safe Harbor method. Which data handling process satisfies this?

Accepted Answer

Remove all 18 HIPAA identifiers from each record.. The HIPAA Safe Harbor method specifically requires the removal of all 18 identifiers listed in the HIPAA Privacy Rule from each patient record. This includes direct identifiers like names, addresses, and Social Security numbers, as well as indirect identifiers such as dates and geographic subdivisions. By removing these 18 identifiers, the data is considered de-identified and no longer subject to HIPAA restrictions, allowing it to be used for AI training.

Answer

Generate synthetic data that mimics patient records.

Answer

Remove patient names and replace with IDs.

Answer

Anonymize data by aggregating into groups of 10 or more.

Question 10

An organization uses an AI-powered resume screening tool to shortlist candidates for a software engineering role. The tool was trained on historical hiring data from the past five years, during which the company predominantly hired male candidates. After deployment, the tool consistently ranks female candidates lower, even when they have equivalent qualifications. The AI team reports that the overall model accuracy is 92%, and they argue that performance is strong. However, the diversity and inclusion team raises ethical concerns about gender bias. The Salesforce AI Associate is asked to evaluate the situation. What should the associate recommend?

Accepted Answer

Retrain the model using a balanced dataset that includes equal representation from all genders and implement ongoing fairness monitoring.. Retraining with a balanced dataset addresses the root cause of bias, and ongoing monitoring ensures fairness over time. Option A is incorrect because ignoring ethical concerns for accuracy is unacceptable. Option C is incorrect because switching vendors without understanding the bias may not solve the issue. Option D is incorrect because manually adjusting scores introduces reverse discrimination and is unethical.

Answer

Continue using the model because 92% accuracy is acceptable and the bias is not significant.

Answer

Replace the current AI tool with a different vendor's tool without further analysis.

Answer

Manually adjust the scoring algorithm to give preference to female candidates to balance the outcome.

Question 11

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?

Accepted Answer

Always have a human review the generated content before publishing. Human review catches errors (hallucinations). Grounding the model in verified data reduces false information. Avoid over-reliance on the model without verification.

Answer

Increase the model's temperature setting to encourage creativity

Answer

Disable all content filtering to avoid blocking useful output

Answer

Use the model's output directly without checking

Question 12

Which TWO practices help ensure accountability in AI systems?

Accepted Answer

Assign a human owner for each AI system.. Assigning a human owner for each AI system establishes clear accountability, ensuring that a specific individual is responsible for the system's behavior, decisions, and compliance with ethical guidelines. This practice aligns with the principle of human oversight, which is critical for maintaining trust and addressing failures in AI systems.

Answer

Blame the AI system for mistakes to protect employees.

Answer

Automate all decisions to eliminate human error.

Answer

Open-source the model code to share responsibility.

Question 13

Refer to the exhibit. A data scientist sees this error when training an Einstein Discovery model for customer churn prediction. What is the most likely reason for the error?

Accepted Answer

The positive examples (180) are insufficient for the number of fields (8).. Einstein Discovery requires at least 50 positive examples per predictor field. With 8 fields, at least 400 positive examples are needed. Only 180 were provided, causing the error.

Answer

The field count (8) exceeds the maximum of 5 allowed fields.

Answer

The model name contains a version number, which is not allowed.

Answer

The dataset has too few records (3200) for 8 fields.

Question 14

A user wants to use Einstein GPT to automatically generate a case summary after a service call is logged. Which feature should they use?

Accepted Answer

Service GPT. Service GPT is the correct feature because it is specifically designed to automate service-related tasks within Salesforce, such as generating case summaries after a service call. It leverages generative AI to analyze call logs and produce concise summaries, directly addressing the user's need for post-call documentation.

Answer

Einstein Discovery

Answer

Sales GPT

Answer

Einstein Copilot

Question 15

A company is deploying Einstein Prediction Builder to predict equipment failure. Which three considerations are essential for building an accurate prediction model? (Choose 3)

Accepted Answer

Missing values in features should be handled appropriately.. Missing values in features can introduce bias or cause errors in the predictive model. Einstein Prediction Builder automatically handles missing data through imputation, but understanding how missing values are treated is essential for model accuracy, as inappropriate handling can distort relationships between features and the target outcome.

Answer

All input features must be numerical.

Answer

The model should be retrained only once after initial deployment.

Question 16

An organization uses Einstein Lead Scoring and notices that leads with a score above 80 are being sent to the sales team too quickly, overwhelming them. The admin wants to adjust when leads are automatically assigned. What should the admin do?

Accepted Answer

Modify the lead assignment rule to only assign leads with scores above a higher threshold. Einstein Lead Scoring assigns a score (0–100) to each lead based on predictive models. The default assignment rule triggers when a lead's score exceeds a threshold (e.g., 80). To reduce the volume of leads sent to sales, the admin should raise that threshold in the lead assignment rule so only higher-scored leads are automatically assigned. This directly controls the flow without altering the scoring model itself.

Answer

Reduce the number of features used in scoring

Answer

Disable Einstein Lead Scoring and use a custom scoring model

Answer

Create a new lead queue and manually review all leads

Question 17

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should be used?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it uses predictive models to automatically rank leads based on their likelihood to convert, enabling the sales manager to prioritize follow-up efforts. It analyzes historical lead data and engagement patterns to assign a score, directly addressing the requirement for automated prioritization without manual rules.

Answer

Einstein Prediction Builder

Answer

Einstein Relationship Health

Answer

Einstein Activity Capture

Question 18

A sales manager wants to understand why certain opportunities are predicted to close won while others are not. They need a visual breakdown of the key factors influencing the prediction. Which Einstein feature provides this automatically?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct feature because it automatically analyzes historical data to identify and visualize the key factors (drivers) that influence prediction outcomes, such as why certain opportunities close won. Unlike scoring features that provide a single score, Einstein Discovery offers a visual breakdown of influential factors, making it ideal for understanding the 'why' behind predictions.

Answer

Einstein Lead Scoring

Answer

Einstein Forecasting

Answer

Einstein Opportunity Scoring

Question 19

A company uses Einstein Conversation Insights to analyze sales call recordings. They want to automatically capture the next steps mentioned in calls. Which feature of Conversation Insights should they configure?

Accepted Answer

Next Step Capture. Next Step Capture, is correct because it is the specific Einstein Conversation Insights feature designed to automatically identify and extract action items or follow-up tasks mentioned during sales calls. This allows the system to surface commitments and next steps without manual note-taking, directly addressing the requirement to capture next steps from call recordings.

Answer

Talk-time Metrics

Answer

Keyword Tracking

Answer

Call Summary

Question 20

A sales team is implementing Einstein Lead Scoring. Which two actions should they take to ensure the model is effective? (Choose 2)

Accepted Answer

Ensure the training data includes both converted and unconverted leads.. Einstein Lead Scoring requires training data that includes both converted and unconverted leads to build a predictive model that can distinguish between leads likely to convert and those that are not. Without unconverted leads, the model cannot learn the negative patterns, leading to biased predictions and poor accuracy.

Answer

Include only demographic data for scoring.

Answer

Set a fixed score threshold for all users.

Answer

Disable the model for low-volume leads.