AI Associate Practice Test 10 — 10 Questions

Question 1

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?

Accepted Answer

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

Answer

The model is overfitted

Answer

The model uses too few features

Answer

The model has low precision

Question 2

A company uses an AI model to classify customer support cases into categories (billing, technical, general). The model performs well on training data but poorly on new cases. Which issue is MOST likely occurring?

Accepted Answer

Overfitting. Overfitting means the model memorized the training data and fails to generalize to new, unseen data. Underfitting would show poor performance on both training and test data. Data leakage occurs when future information leaks into training. Bias in data is a different issue.

Answer

Underfitting

Answer

Data leakage

Answer

Bias in training data

Question 3

A data scientist needs to prepare data for Einstein Discovery. The dataset includes a field 'Customer_Status__c' with values 'Active', 'Inactive', and 'Churned'. How should this field be treated?

Accepted Answer

Keep as a text field and let Einstein Discovery handle it as a categorical predictor.. Einstein Discovery natively supports text fields as categorical predictors, automatically encoding them for model training. The platform handles string values like 'Active', 'Inactive', and 'Churned' without requiring manual transformation, preserving the semantic meaning and cardinality of the data.

Answer

Create separate boolean fields for each value to improve model accuracy.

Answer

Remove the field because text fields cannot be used in Einstein Discovery.

Answer

Convert to numeric values 1, 2, 3 to preserve order.

Question 4

A team is building a pipeline to train a model daily. The source data arrives in CSV files but needs to be converted to Parquet for efficiency. Which pipeline step should perform this conversion?

Accepted Answer

Data ingestion step. The data ingestion step is responsible for bringing raw data into the pipeline, including format conversions like CSV to Parquet. Converting to Parquet at ingestion improves storage efficiency and query performance for downstream processing, as Parquet uses columnar storage and compression.

Answer

Feature engineering step

Answer

Model deployment step

Answer

Data validation step

Question 5

A company uses Einstein Forecasting. Their sales reps' committed forecasts are consistently lower than the AI-predicted forecast. The manager wants to understand why. What is the BEST first step to investigate the discrepancy?

Accepted Answer

Review the AI forecast explanation to see which factors (e.g., deal stage, historical win rates) are driving the higher prediction.. Einstein Forecasting provides an AI-predicted forecast based on historical data and deals. Comparing the rep commit to the AI forecast, and analyzing the key drivers behind the AI prediction (such as deal stage, age, amount) helps identify why the AI expects more.

Answer

Disable Einstein Forecasting and revert to manager rollups only.

Answer

Adjust the historical date range in forecast settings to exclude past low-performing quarters.

Answer

Run a report on closed won opportunities to see if the AI overestimates.

Question 6

A company is preparing customer data for a predictive model. They notice that many records have missing values for the 'annual income' field. Which approach is best to handle this issue while minimizing bias?

Accepted Answer

Use model-based imputation considering other features.. Model-based imputation (Option B) is best because it uses relationships between features (e.g., education, job role) to predict missing 'annual income' values, preserving data distribution and minimizing bias. This approach avoids the distortion caused by simple mean/zero imputation and retains sample size better than deletion.

Answer

Remove all records with missing values.

Answer

Replace missing values with the mean.

Answer

Set missing values to zero.

Question 7

A company wants to deploy an Einstein AI model that uses sensitive customer data. Which practice should they follow to comply with data privacy regulations?

Accepted Answer

Use Einstein Trust Layer features to mask personally identifiable information (PII) in the model.. The Einstein Trust Layer provides built-in capabilities to automatically mask or redact personally identifiable information (PII) before data is sent to the underlying AI model, ensuring compliance with data privacy regulations like GDPR and CCPA without requiring manual data handling. This feature operates at the platform level, intercepting data in transit and applying masking rules based on predefined patterns, so sensitive customer data is never exposed to the model or stored in its training logs.

Answer

Store all sensitive data in an external data lake and connect via APIs.

Answer

Obtain explicit consent from data subjects before using their data in AI models.

Answer

Limit the data used for training to only essential fields.

Question 8

Which type of AI is designed to perform only a specific task, such as playing chess or recommending products?

Accepted Answer

Narrow AI. Narrow AI (weak AI) is specialized for one task and cannot generalize beyond its training.

Answer

Super AI

Answer

General AI

Answer

Strong AI

Question 9

A sales rep wants to use Einstein Activity Capture to automatically log emails and meetings. Which prerequisite must be met?

Accepted Answer

Users must grant access to their email and calendar via OAuth. Einstein Activity Capture requires users to explicitly grant access to their email and calendar via OAuth (Option D). This OAuth-based authentication allows Salesforce to securely sync emails and meetings from the user's email provider (e.g., Gmail, Outlook) into Salesforce records. Without this explicit consent, the feature cannot access or log the user's activity data.

Answer

Chatter must be disabled for the organization

Answer

Sales Cloud Einstein licenses for all users

Answer

The feature is automatically enabled once email integration is configured

Question 10

A company wants to build a chatbot that can understand customer intents like 'open a case' or 'check order status' and route conversations accordingly. They need the bot to be trained with natural language examples. Which Einstein Bot component is used to map user phrases to actions?

Accepted Answer

Intents. Intents are the correct component because they map user phrases (natural language examples) to specific actions or goals, such as 'open a case' or 'check order status'. In Einstein Bot, intents are trained with sample utterances to recognize what the user wants, enabling the bot to route conversations to the appropriate dialog flow.

Answer

Entities

Answer

Dialog flows

Answer

NLP training