AI Associate Data for AI Practice Test 6 — 15 Questions

Question 1

A company uses Einstein Discovery to identify factors that increase case resolution time. After training, the model shows that 'Case_Origin__c' has high importance. What action should the company take?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Investigate the categories within Case_Origin to understand their impact.. The correct option is D: investigate the categories within Case_Origin to understand their impact. When Einstein Discovery reports a field such as Case_Origin__c as highly important, the next step is to drill into that field's individual categories (e.g., Phone, Email, Web) to see which values drive longer case resolution times, enabling targeted process improvements. Option A is wrong because removing a high-importance field discards predictive signal rather than acting on the insight. Option B is premature, since interaction terms should be considered only after understanding the main effect of Case_Origin. Option C is irrelevant, as data quality thresholds do not explain why the field is important or how its categories affect resolution time.

Answer

Remove the field from the model to reduce complexity.

Answer

Create interaction terms between Case_Origin and other fields.

Answer

Increase the data quality threshold for Case_Origin records.

Question 2

A company has set up Einstein Next Best Action with a recommendation strategy. They want to ensure that recommendations are personalized based on the customer's recent behavior. What data should be used?

Accepted Answer

Streaming data from Data Cloud that includes recent website interactions.. Einstein Next Best Action requires real-time or near-real-time data to personalize recommendations based on recent customer behavior. Streaming data from Data Cloud captures website interactions as they happen, enabling the recommendation engine to use the most current signals (e.g., page views, clicks) to adjust offers dynamically.

Answer

Event data from the website tracked via Google Analytics.

Answer

Static profile fields like customer age and location.

Answer

Historical data from a data warehouse updated daily.

Question 3

Which TWO actions are required to prepare data for an Einstein Discovery model?

Accepted Answer

Ensure the data is stored in a Salesforce object or a connected data source.. Option C is correct because Einstein Discovery requires the source data to reside in a Salesforce object or be accessible through a connected data source (such as a CRM Analytics dataset or external connection) before a model can be created. Option E is correct because defining the outcome (dependent) field is mandatory — the model must know which variable it is predicting, whether numeric or categorical, to build the story and generate predictions. Options A, B, and D are not required: Einstein Discovery automatically handles missing values rather than requiring their removal, it can automatically select predictors (manual selection of exactly 10 is not a requirement), and it internally splits data for training and validation rather than requiring a separate user-created dataset.

Answer

Remove all records with missing values in any field.

Answer

Select exactly 10 predictor fields manually.

Answer

Create a separate dataset for training and validation.

Question 4

A company uses Salesforce Data Platform to store customer data. They want to use this data to train an AI model for lead scoring, but they are concerned about data quality. Which step should they take first to ensure the data is suitable for AI?

Accepted Answer

Profile the data to identify missing values, outliers, and inconsistencies. Profiling the data is the essential first step because it systematically identifies missing values, outliers, and inconsistencies that degrade model performance. Without this baseline assessment, any subsequent normalization or labeling would be applied to flawed data, leading to unreliable lead scoring predictions. Salesforce Data Platform supports profiling via tools like Einstein Analytics or Data Prep, which scan fields for nulls, range violations, and format errors.

Answer

Immediately normalize all numerical features

Answer

Create a labeled dataset using historical lead outcomes

Answer

Set up a data pipeline to stream data in real-time

Question 5

A data scientist is building a predictive model for customer churn using Salesforce data. The dataset has 20 features, and the target variable is highly imbalanced (5% churn, 95% non-churn). Which technique should be applied to handle the class imbalance before training?

Accepted Answer

Use Synthetic Minority Over-sampling Technique (SMOTE). SMOTE (Synthetic Minority Over-sampling Technique) is the correct choice because it generates synthetic samples for the minority class (churn) by interpolating between existing minority instances, effectively balancing the dataset without simply duplicating data. This prevents the model from being biased toward the majority class (non-churn) and improves recall for the churn class, which is critical in imbalanced classification problems.

Answer

Apply Principal Component Analysis (PCA) for dimensionality reduction

Answer

Create interaction features between existing variables

Answer

Use accuracy as the evaluation metric

Question 6

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 7

A company is deploying an AI model that recommends next best actions for sales reps. They notice that the model's recommendations are biased towards high-revenue opportunities. Which data-related action can help reduce this bias?

Accepted Answer

Oversample the underrepresented segments in the training data. Oversampling underrepresented segments in the training data directly addresses the class imbalance that causes the model to favor high-revenue opportunities. By increasing the frequency of low-revenue examples, the model learns to treat all segments more equally, reducing bias in its recommendations. This is a standard data-level technique for mitigating bias in AI models.

Answer

Use a larger neural network model

Answer

Encrypt the data before training

Answer

Remove all low-revenue opportunities from the training data

Question 8

A Salesforce admin wants to use Einstein Prediction Builder to predict case resolution time. What type of data is most critical for training this model?

Accepted Answer

Historical case records including resolution time. Einstein Prediction Builder requires historical data with known outcomes to train a supervised machine learning model. Historical case records containing actual resolution times provide the labeled examples needed for the model to learn patterns and predict future case resolution times. Without this ground truth data, the model cannot be trained to make accurate predictions.

Answer

Customer satisfaction survey responses

Answer

Product inventory levels

Answer

Employee work schedules

Question 9

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

Accepted Answer

Deletion of rows with missing values. Option B (Deletion of rows with missing values) is correct because listwise deletion removes records containing nulls, a standard and simple preprocessing technique when missingness is random and the affected rows are few. Option E (Imputation with mean or median) is correct because it fills missing numeric entries with the column's central tendency, preserving sample size and avoiding data loss during AI training. Option A (L1 regularization) is incorrect because it is a model penalty that drives weights toward zero to reduce overfitting, not a missing-value treatment. Option C (One-hot encoding) is incorrect because it converts categorical variables into binary vectors and does not address nulls. Option D (Min-max normalization) is incorrect because it rescales numeric features to a fixed range and cannot handle missing entries.

Answer

L1 regularization

Answer

One-hot encoding

Answer

Min-max normalization

Question 10

Which THREE factors should be considered when selecting features for a predictive model in Salesforce?

Accepted Answer

Correlation between features to avoid multicollinearity. Option B is correct because highly correlated features introduce multicollinearity, which destabilizes model coefficients and reduces the reliability of feature-importance scores in Salesforce Einstein Discovery, so correlation between features must be evaluated during selection. Option C is correct because a feature must have a meaningful relationship with the target variable; irrelevant features add noise, inflate dimensionality, and degrade predictive accuracy. Option E is correct because Salesforce predictive models are consumed by business users, and features that are interpretable and actionable make the resulting insights and recommendations easier to trust and operationalize. Option A is not among the marked answers because data volume alone does not determine whether a feature should be selected; a small but highly relevant feature can outperform a large but noisy one. Option D is not among the marked answers because privacy compliance is a governance and data-handling requirement that applies to the overall project, not a feature-selection criterion for model quality.

Answer

Volume of data available for each feature

Answer

Compliance with data privacy regulations

Question 11

Which TWO are common data quality issues that can negatively impact AI model performance?

Accepted Answer

Missing values in critical fields. Option A (Missing values in critical fields) is correct because nulls or absent values in key features cause models to learn from incomplete patterns, often forcing imputation or dropping rows, which biases training and degrades prediction quality. Option E (Duplicate records in the dataset) is correct because duplicated rows over-represent certain examples, skewing class distributions and causing the model to overfit to repeated instances rather than generalizable patterns. Option B (Low model accuracy during validation) is not a data quality issue but a symptom or outcome measured after training, so it does not describe a root cause in the data itself. Option C (Insufficient storage space for data) is an infrastructure/capacity concern, not a data quality defect affecting feature integrity. Option D (Inconsistent data governance policies) is an organizational/process issue that can indirectly affect data, but it is not itself a common data quality issue such as missingness or duplication.

Answer

Low model accuracy during validation

Answer

Insufficient storage space for data

Answer

Inconsistent data governance policies

Question 12

A company wants to use Einstein Article Recommendations to surface relevant knowledge articles to its support agents. What two data components are required to set up this feature?

Accepted Answer

Knowledge Article View event data and Case records. Einstein Article Recommendations uses historical Knowledge Article View event data to understand which articles agents have found useful in the past, and Case records to provide context about the current issue. By analyzing patterns between case attributes and article views, the AI can predict and surface the most relevant articles for a given case. Without both data components, the recommendation engine cannot learn the association between case details and article usefulness.

Answer

Email-to-case logs and Knowledge Article feedback

Answer

Knowledge Article categories and Case priority

Answer

Community user activity and Knowledge Article ratings

Question 13

A company is using Einstein Discovery to predict customer churn. The model was created six months ago and has been making predictions. Recently, the model's accuracy has dropped significantly. The data scientist confirms that the data schema has not changed. What is the most likely reason for the drop in accuracy?

Accepted Answer

The model is suffering from concept drift. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less accurate even though the data schema remains unchanged. In Einstein Discovery, models are trained on historical data, and if the underlying patterns of customer churn evolve (e.g., due to market shifts or new competitor behavior), the model's learned relationships become stale. Since the data schema is confirmed unchanged, concept drift is the most likely cause of the accuracy drop.

Answer

The data source is not being refreshed daily

Answer

The model's features have become irrelevant

Answer

The model needs to be retrained weekly instead of monthly

Question 14

A company is implementing Einstein Prediction Builder to predict whether a support case will escalate. Which TWO data preparation steps should the admin take to improve model accuracy?

Accepted Answer

Ensure missing values are handled appropriately (e.g., imputed or excluded). Option B is correct because Einstein Prediction Builder requires clean, complete data: missing values in predictor fields can bias or break model training, so they must be imputed (e.g., with a default or mean value) or the affected records/fields excluded to keep the model accurate. Option E is correct because fields with a one-to-one relationship to the outcome (such as a field that directly encodes whether the case escalated) cause target leakage, letting the model 'cheat' and producing artificially high accuracy that will not generalize to new cases. Option A is not correct because adding every possible field introduces noise, irrelevant predictors, and overfitting rather than improving accuracy. Option C is not correct because encryption of PII is a security/compliance measure and does not affect model accuracy. Option D is not correct because excluding non-escalated closed cases would remove the negative class, leaving an unbalanced or single-class dataset that prevents the model from learning to distinguish escalations.

Answer

Include as many fields as possible to provide more context

Answer

Encrypt all fields containing personally identifiable information

Answer

Exclude cases that were closed without escalation

Question 15

You are a Salesforce AI Specialist at a mid-sized manufacturing company. The company uses Einstein Lead Scoring to prioritize leads. The model was trained on historical lead data and has been in production for three months. Recently, the sales team reports that high-scoring leads are not converting as expected. You investigate and find that the model's data source includes leads from the past 18 months. However, six months ago, the company changed its lead qualification process: they started requiring a demo before scoring leads as 'qualified.' As a result, the definition of a converted lead changed. What is the best course of action to improve model performance?

Accepted Answer

Retrain the model using only leads from the last six months after the process change. The change in lead qualification process six months ago introduced a data distribution shift (concept drift), making older leads no longer representative of the current conversion behavior. Retraining the model on only the last six months of data aligns the training set with the new definition of a 'converted lead,' allowing Einstein Lead Scoring to learn the updated patterns and improve prediction accuracy.

Answer

Manually adjust the model's prediction threshold to account for the new process

Answer

Remove the 'Demo Scheduled' field from the model to avoid bias

Answer

Add more historical leads from before the process change to increase data volume