AI Associate Data for AI Practice Test 7 — 15 Questions

Question 1

A company uses Einstein Prediction Builder to predict customer churn. The data includes account creation date, number of support cases, and average payment delay. After training, the model shows low confidence scores. What is the most likely cause?

Accepted Answer

The data contains many missing values or outliers for the selected fields.. Option B is correct because low confidence scores in Einstein Prediction Builder often stem from data quality issues such as missing values or outliers. These anomalies distort the model's ability to learn meaningful patterns, leading to uncertain predictions. Clean, complete data is essential for the model to produce high-confidence scores.

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The training dataset includes fewer than 500 records.

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The prediction field is set to a numeric type instead of a picklist.

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The model was trained on data refreshed daily instead of weekly.

Question 2

A Salesforce admin wants to use Einstein Recommendations to suggest products. What is a key requirement for the data used to train the recommendation model?

Accepted Answer

A minimum of 1,000 user-product interactions must exist.. Einstein Recommendations requires a minimum of 1,000 user-product interactions (such as views, clicks, or purchases) to train a statistically significant collaborative filtering model. This threshold ensures the algorithm can identify meaningful patterns in user behavior and generate accurate product suggestions.

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Product prices must be stored in a custom currency field.

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User profiles must include demographic data.

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Product descriptions must be at least 100 characters long.

Question 3

An organization is preparing data for Einstein Next Best Action. They have multiple action types (discounts, product suggestions, content). Which data model approach best ensures accurate recommendations?

Accepted Answer

Use a single model that includes all action types in the training data.. Option D is correct because Einstein Next Best Action is designed to learn from all action types simultaneously within a single model. By including all action types (discounts, product suggestions, content) in the training data, the model can capture cross-action patterns and relative effectiveness, leading to more accurate and contextually relevant recommendations. A unified model avoids fragmentation and ensures consistent scoring across actions.

Answer

Train a separate model per customer segment and then merge.

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Create a separate model for each action type and combine results manually.

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Build an ensemble of models and average their outputs.

Question 4

A data scientist is preparing data for Einstein Discovery. The dataset has 10,000 records with 5 predictors and one outcome. The outcome is binary (1/0). What is the minimum number of positive outcomes typically required for a reliable model?

Accepted Answer

250. Option A is correct because for binary classification with 10,000 records and 5 predictors, a common rule of thumb in predictive modeling (including Einstein Discovery) is to have at least 10 events per predictor variable (EPV). With 5 predictors, you need at least 50 positive outcomes, but to ensure model stability and reliable training, a minimum of 250 positive outcomes (5% of 10,000) is typically required. This aligns with best practices for avoiding overfitting and achieving adequate statistical power.

Answer

500

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100

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50

Question 5

An admin is setting up Einstein Article Recommendations. Which type of data is essential for the model to learn which articles are relevant?

Accepted Answer

Article view events from users. Einstein Article Recommendations uses a collaborative filtering model that learns article relevance from user interaction signals, specifically article view events. The model analyzes patterns of which articles users view together to identify related content, making view events the essential training data for generating recommendations.

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Article publication dates

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User job titles

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Article author names

Question 6

A company uses Einstein Forecasting for revenue prediction. The historical data shows seasonal spikes every quarter. The model consistently underestimates peak periods. What is the best data preparation step to improve accuracy?

Accepted Answer

Add a 'quarter' index field (1-4) to the dataset.. Einstein Forecasting can detect seasonality if the data contains enough history and a seasonality marker. Adding a 'quarter' feature explicitly helps the model capture recurring patterns.

Answer

Increase the forecast horizon to 12 months.

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Remove the spike data points as outliers.

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Use only the last 6 months of data to reduce noise.

Question 7

An admin is troubleshooting Einstein Sentiment. The model returns high confidence but wrong sentiment (e.g., positive reviews labeled negative). What is the most likely issue?

Accepted Answer

The training data has incorrect labels for sentiment.. Option C is correct because if the training data contains incorrect labels for sentiment, the model learns from erroneous ground truth, leading to high confidence in wrong predictions. In Einstein Sentiment, the model's accuracy depends directly on the quality and correctness of the labeled training data; mislabeled examples cause the classifier to associate features with the wrong sentiment class, resulting in confident but incorrect outputs.

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The model was not retrained after the last data load.

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The training data contains predominantly neutral examples.

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The field mapping for the sentiment field is incorrect.

Question 8

When using Einstein Lead Scoring, which data source is most critical for generating accurate lead scores?

Accepted Answer

Converted lead records with attached opportunities. Converted lead records with attached opportunities are the most critical data source because Einstein Lead Scoring uses supervised machine learning to analyze historical patterns in leads that successfully converted into opportunities. By training on these converted records, the model learns which lead attributes and behaviors are predictive of conversion, enabling it to assign accurate scores to new leads. Without this historical conversion data, the model lacks the ground truth needed to distinguish high-quality leads from low-quality ones.

Answer

Lead source (e.g., Webinar, Trade Show)

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Lead field update timestamps

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Email open rates from marketing campaigns

Question 9

A company has international customers and wants Einstein Prediction Builder to forecast deal closure probability. The data includes fields like 'region', 'product line', and 'deal amount'. What is a best practice to ensure the model works for all regions?

Accepted Answer

Group regions into broader categories like 'Americas', 'EMEA', 'APAC'.. Option D is correct because grouping regions into broader categories like 'Americas', 'EMEA', and 'APAC' reduces high cardinality and sparsity in categorical features, which improves model stability and prevents overfitting in Einstein Prediction Builder. This approach ensures each region group has sufficient training data to learn meaningful patterns, enabling the model to generalize better across all regions without introducing bias from rare categories.

Answer

One-hot encode the region field using 50+ dummy variables.

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Remove the region field to avoid bias.

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Use region as a numeric rank based on past conversion rates.

Question 10

Which TWO data preparation steps are required before using Einstein Discovery for sales forecasting? (Choose 2)

Accepted Answer

Include a date or timestamp field for time series analysis. Einstein Discovery requires a date or timestamp field to perform time series analysis, which is essential for identifying trends, seasonality, and patterns in historical sales data. Without this field, the model cannot properly order observations or forecast future values based on temporal dependencies.

Answer

Convert all text fields to numeric using one-hot encoding

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Remove duplicate records

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Normalize numeric fields to a 0-1 scale

Question 11

Which THREE actions are recommended when preparing data for Einstein Next Best Action? (Choose 3)

Accepted Answer

Provide data on which actions were offered and whether they were accepted. Option A is correct because Einstein Next Best Action (NBA) requires historical interaction data showing which actions were offered and whether they were accepted to train the predictive model. This feedback loop enables the AI to learn which actions are most effective for specific customer contexts, directly improving recommendation accuracy.

Answer

Include at least 10 different action types per strategy

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Use only historical data from the last 30 days

Question 12

A data analyst is troubleshooting Einstein Article Recommendations that are not showing up on the site. Which TWO checks should be performed first? (Choose 2)

Accepted Answer

Check that article view events are being captured in the data. Option C is correct because Einstein Article Recommendations rely on user interaction data, specifically article view events, to generate personalized recommendations. If these events are not being captured, the model has no input to learn from, and recommendations will not appear. Checking event capture is a fundamental first step in troubleshooting data pipeline issues.

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Ensure at least 100 articles are in the knowledge base

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Confirm that article authors have the correct profile permissions

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Increase the recommendation frequency from daily to hourly

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.

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

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

Question 14

Refer to the exhibit. A developer runs this SOQL query to prepare data for Einstein Lead Scoring. The query returns an error. What is the most likely issue?

Accepted Answer

The alias 'TotalAmount' is not allowed in the HAVING clause.. The HAVING clause references alias TotalAmount, but SOQL does not allow aliases in HAVING; the aggregated expression must be repeated.

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The query misses a GROUP BY clause.

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The SUM(Amount) cannot be used in the HAVING clause.

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The WHERE clause condition is invalid.

Question 15

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.

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

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The source should be Lead, not Opportunity.

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