AI Associate Practice Test 6 — 10 Questions

Question 1

Which TWO practices are recommended when using AI for automated decision-making in hiring?

Accepted Answer

Regularly audit the model for adverse impact.. Regular auditing for adverse impact is a core ethical practice to detect and mitigate bias in AI-driven hiring systems. Audits involve statistical analysis (e.g., the four-fifths rule from the Uniform Guidelines on Employee Selection Procedures) to compare selection rates across protected groups, ensuring the model does not disproportionately disadvantage certain demographics.

Answer

Use the AI model as the sole decision-maker.

Answer

Use all available data including protected attributes.

Answer

Ignore adverse impact if the model is accurate.

Question 2

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 3

Which THREE factors should an AI Associate consider when evaluating a model for potential bias?

Accepted Answer

Whether features are correlated with protected attributes.. If a feature is correlated with a protected attribute (e.g., race, gender, age), the model may inadvertently learn and perpetuate discriminatory patterns, even if the protected attribute itself is not used as an input. This is a key source of indirect or proxy bias in machine learning systems.

Answer

The complexity of the model architecture.

Answer

The date the model was last deployed.

Question 4

An administrator is configuring a Salesforce AI model that uses historical sales data. The data includes fields like 'Amount', 'Close_Date', and 'Lead_Source'. What is the primary purpose of data preprocessing in this context?

Accepted Answer

To clean and transform data into a format suitable for model training. Data preprocessing is essential for AI models because raw historical sales data often contains missing values, inconsistent formats, and noise. Cleaning (e.g., handling nulls in 'Amount') and transforming (e.g., encoding 'Lead_Source' into numerical features) ensure the model can learn patterns effectively, directly impacting training accuracy and convergence.

Answer

To generate visualizations for business stakeholders

Answer

To increase the storage capacity of the database

Answer

To enforce data access permissions for different user roles

Question 5

A company wants an AI chatbot that can handle customer inquiries about order status. Which tool should be configured?

Accepted Answer

Einstein Bot. Einstein Bot is the correct tool because it is Salesforce's native AI-powered chatbot designed to handle customer inquiries, including order status, through natural language processing and automated conversations. It can be configured to answer common questions, escalate complex issues, and integrate with backend systems to retrieve real-time order data without human intervention.

Answer

Queues

Answer

Omni-Channel

Answer

Case Assignment Rules

Question 6

Which data transformation is most appropriate for converting categorical variables into numerical format for a machine learning model?

Accepted Answer

One-hot encoding.. One-hot encoding is the correct transformation because it converts categorical variables into a binary vector representation, where each category becomes a separate column with a 1 or 0. This allows machine learning models to interpret categorical data without implying any ordinal relationship, which is essential for algorithms that rely on numerical distances or linear algebra.

Answer

Normalization.

Answer

Principal component analysis.

Answer

Standardization.

Question 7

Refer to the exhibit. A developer wrote a trigger to call an Einstein prediction API on lead insert. When new leads are created, the trigger fails with a 'Too many SOQL queries' error. What is the most likely cause?

Accepted Answer

The trigger is making API calls in a loop, causing governor limit issues. The trigger is making synchronous API calls to the Einstein prediction service inside a loop that iterates over each new lead. Each API call counts against the 'Number of callouts' governor limit (default 100 per transaction), and if more leads are inserted than the limit allows, the transaction fails with a 'Too many SOQL queries' error—though the error message is misleading, the root cause is exceeding the callout limit, not SOQL queries. The trigger should batch the API calls or use asynchronous processing to stay within limits.

Answer

The trigger is executed before the lead is saved

Answer

The trigger is querying a related object without an index

Answer

The Score__c field is not writeable

Question 8

Which THREE are key dimensions of data quality that directly impact AI model performance?

Accepted Answer

Consistency.. Consistency is a key dimension of data quality because AI models rely on stable patterns in the data. If the same entity is represented differently across records (e.g., 'NY' vs 'New York'), the model may learn incorrect correlations, leading to degraded prediction accuracy and unreliable outputs.

Answer

Timeliness.

Answer

Data volume.

Question 9

A company wants to use Einstein Prediction Builder to predict customer churn. Which data preparation step is essential before building the model?

Accepted Answer

Define the prediction objective and the target date field.. Einstein Prediction Builder requires you to define the prediction objective (e.g., 'Will this customer churn?') and specify the target date field that marks the event. This step is essential as it tells the model what to predict and over what time window, enabling the automated feature engineering and model training process.

Answer

Ensure the data is in a Salesforce connected data source like Data Cloud.

Answer

Create a formula field to calculate the churn probability.

Answer

Create a new custom object to store the prediction results.

Question 10

A bank uses Einstein Discovery to generate insights about loan approval decisions. After deployment, they notice the model denies loans to a higher percentage of applicants from a certain postal code. Which action should be taken to ensure responsible AI?

Accepted Answer

Audit model outcomes for fairness across demographic groups and retrain if needed. Responsible AI requires auditing model outcomes for fairness across demographic groups, even when the disparity correlates with a non-protected attribute like postal code. In Einstein Discovery, postal code can act as a proxy for protected attributes such as race or socioeconomic status, and ignoring this could lead to discriminatory lending practices. Auditing allows the team to detect and mitigate bias, and retraining with fairness constraints ensures the model aligns with ethical AI principles.

Answer

Ignore the discrepancy because postal code is not a protected attribute

Answer

Retrain the model using only recent loan data

Answer

Remove the postal code field from the model