AI Associate Practice Test 25 — 15 Questions

Question 1

A service manager wants to reduce manual case classification effort by automatically setting the Type, Priority, and Reason fields on incoming cases. Which Einstein feature meets this requirement?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it uses machine learning to automatically predict and set the Type, Priority, and Reason fields on incoming cases based on historical case data and patterns. This directly reduces manual classification effort by assigning these three standard fields without requiring custom models or additional configuration.

Answer

Einstein Prediction Builder

Answer

Einstein Reply Recommendations

Answer

Einstein Article Recommendations

Question 2

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?

Accepted Answer

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

Answer

The model lacks confidence scoring

Answer

The algorithm choice (XGBoost) is inappropriate

Answer

The training data size is insufficient

Question 3

Refer to the exhibit. A sales manager sees that an account has an Einstein Score of 78 with a confidence of 0.65. What is the most appropriate interpretation?

Accepted Answer

The account is predicted to convert, but the model's confidence is relatively low, suggesting the prediction should be verified.. The Einstein Score is a predictive lead scoring model that outputs a conversion probability (0 to 100), and the confidence score (0 to 1) indicates the model's certainty in that prediction. A confidence of 0.65 is below the typical threshold (e.g., 0.75 or higher), meaning the prediction is less reliable and should be manually verified before acting on it.

Answer

The score indicates the account has been contacted 78 times, with a 65% satisfaction rate.

Answer

The account is predicted to have a 78% chance of converting, and the model is 65% confident in that prediction.

Answer

The account is in the top 78% of scoring accounts, with a 65% chance of being accurate.

Question 4

Refer to the exhibit. An admin runs a preprocess script before training an Einstein model. Why is normalization applied to the 'AnnualRevenue' and 'NumberOfEmployees' columns?

Accepted Answer

To ensure both features contribute equally to the model. Normalization scales features like 'AnnualRevenue' and 'NumberOfEmployees' to a comparable range (e.g., 0–1 or with zero mean and unit variance). Without normalization, a feature with larger numeric values (e.g., revenue in millions) would dominate distance-based calculations in models like k-nearest neighbors or gradient descent, causing the model to undervalue the smaller-scale feature. By normalizing, both features contribute equally to the model's learning process, which is essential for many machine learning algorithms used in Einstein.

Answer

To detect outliers in the data

Answer

To remove rows with missing values

Answer

To reduce the number of features from 30 to 2

Question 5

Refer to the exhibit. A Salesforce admin runs an audit command for an Einstein model. What conclusion can be drawn from the output?

Accepted Answer

The model's average response time of 350ms is within typical performance expectations. The output shows an average response time of 350ms for the Einstein model, which is within the typical performance expectation of under 500ms for real-time AI inference in Salesforce. This indicates the model is responding efficiently without significant latency issues, making option C correct.

Answer

Token usage indicates the average response length is 300 tokens

Answer

The model experienced significant slowdowns

Answer

The model has a high error rate and needs retraining

Question 6

A company deploys Einstein Recommendation Builder on its e-commerce site. The recommendations are not personalized. What is the most likely cause?

Accepted Answer

The model has not been trained with enough user behavior data.. Einstein Recommendation Builder relies on user interaction data to personalize. If insufficient data exists, recommendations become generic. Option A is correct. Option B is wrong because real-time sync is not required. Option C is wrong because the builder can work without a data scientist. Option D is wrong because the model can recommend products beyond categories.

Answer

The company did not hire a data scientist to tune the model.

Answer

The recommendation engine is not syncing in real-time with the website.

Answer

The product catalog is too large for the model to process.

Question 7

Based on the exhibit, what is the primary issue with this Einstein Bot conversation?

Accepted Answer

The bot lacks alternative ways to identify the customer.. The exhibit shows the bot repeatedly asking for the customer's account number without offering alternative identification methods (e.g., email, phone number, or name). This is the primary issue because Einstein Bot's conversational design should include fallback paths to handle cases where the user cannot provide the requested information, ensuring a smooth user experience and reducing drop-offs.

Answer

The bot is confused about the user's intent.

Answer

The bot is repeating itself excessively.

Answer

The bot does not understand the initial intent.

Question 8

Data quality is critical for AI model performance. Which three data quality dimensions should be monitored? (Choose three.)

Accepted Answer

Completeness. Completeness, timeliness, and consistency are fundamental data quality dimensions. Volume is not a quality dimension; uniqueness is related to consistency but not always required.

Answer

Uniqueness

Answer

Volume

Question 9

Refer to the exhibit. A dataflow is set up to prepare data for a prediction model. The model is expected to predict close probability for all open opportunities. What is wrong with this dataflow?

Accepted Answer

The filter on StageName is too restrictive; it excludes non-won opportunities needed for training.. The filter excludes all opportunities that are not 'Closed Won'. The model should be trained on both won and lost opportunities to predict close probability. The filter should be removed or include all stages.

Answer

The output target should be a dataset, not a model.

Answer

The source should be Lead, not Opportunity.

Answer

The dataflow is missing a transform node to remove null values.

Question 10

When training an Einstein Discovery model, which data type is not supported as a predictor field?

Accepted Answer

Multi-select picklist. Multi-select picklists are not supported as predictors in Einstein Discovery. Numeric, picklist, and date fields are supported.

Answer

Numeric

Answer

Picklist

Answer

Date

Question 11

A company plans to train an AI model using data from Salesforce CRM and an external marketing automation platform. What is the first step to unify these data sources in Data Cloud?

Accepted Answer

Define a Data Model that maps fields from both sources to a unified customer object. Creating a data model that maps fields from both sources to a common object ensures consistency. Option B is wrong because data streams come after the model. Option C is wrong because Calculated Insights are for aggregations. Option D is wrong because data transformations are applied later.

Answer

Create two separate Data Streams to bring data in

Answer

Build a Calculated Insight to merge the data

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Set up a Data Transformation to blend the sources

Question 12

Refer to the exhibit. An admin sees this error in the Einstein activity log. What is the most likely cause?

Accepted Answer

Data masking is configured but not applied to the sentiment analysis model.. The error indicates that sentiment analysis is not enabled, but PII is leaking, likely because data masking is not applied to the model generating the output.

Answer

Sentiment analysis is generating PII that data masking cannot hide.

Answer

The model output should not contain any text.

Answer

The Einstein Trust Layer is completely disabled.

Question 13

A user asks an AI assistant to generate content that may be offensive. What should the AI do?

Accepted Answer

Refuse and explain why. The AI should refuse the request and explain why, upholding ethical standards.

Answer

Ignore the request

Answer

Generate and report the user

Answer

Generate with a warning

Question 14

A developer is creating a custom AI model on Salesforce. To ensure the model is fair across demographic groups, which activity should be included in the development process?

Accepted Answer

Bias testing using a diverse test dataset.. Bias testing using diverse datasets directly evaluates fairness across groups.

Answer

Feature selection using correlation matrix.

Answer

Cross-validation to avoid overfitting.

Answer

Hyperparameter tuning with grid search.

Question 15

An admin wants to use Einstein Reply Recommendations in Service Cloud. Which ethical consideration is most important to implement before enabling the feature?

Accepted Answer

Ensure customer data is anonymized.. Customer data privacy is paramount; anonymizing data ensures recommendations do not expose sensitive information.

Answer

Reduce the cost of agent training.

Answer

Maximize the number of recommendations.

Answer

Increase the speed of case resolution.