AI Associate Data for AI — 100 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.. When Einstein Discovery identifies 'Case_Origin__c' as highly important, the company should investigate the categories within that field to understand how different case origins impact resolution time. Option A is incorrect because removing the field would discard valuable predictive information. Option B is incorrect because Einstein Discovery already handles interactions automatically. Option C is incorrect because data quality thresholds are not related to the importance of a field; the model has already determined the field is important.

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.. Options C and E are correct. For Einstein Discovery, the data must be stored in a Salesforce object or a connected data source (C), and you must define the outcome field that the model will predict (E). Option A is not required because Einstein handles missing values automatically; removing all records with missing values can result in data loss. Option B is not required because Einstein can automatically select predictor fields. Option D is not required because the training and validation split is handled automatically by Einstein.

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 Salesforce admin is troubleshooting an Einstein Prediction Builder model that is not generating predictions. The model was created with a custom object 'Feedback__c'. The admin notices that the model's data source includes records with status 'In Progress' and 'Closed'. What is the most likely cause of the model not generating predictions?

Accepted Answer

The outcome field has more than two unique values. Einstein Prediction Builder requires the outcome (prediction) field to contain exactly two unique values for binary classification. If the outcome field has more than two unique values, the model cannot generate predictions. The admin's mention of records with statuses 'In Progress' and 'Closed' suggests that the outcome field (often a status field) may include additional values, violating this requirement. Note that the specific statuses listed are not the root cause—the key issue is the number of unique outcome values.

Answer

The data source is not refreshed daily

Answer

The object is a custom object

Answer

The object has fewer than 1000 records

Question 5

An admin is configuring Einstein Vision and wants to train a model to identify product defects from images. The admin has uploaded 500 images of defective products and 500 images of non-defective products. However, the model training fails with an error about data quality. What is the most likely cause?

Accepted Answer

The images are larger than 10 MB each. The most likely cause of a data quality error in Einstein Vision when uploading 500 images per category is that the images exceed the maximum file size limit of 10 MB. Einstein Vision has a strict limit on image file size; if any image is larger than 10 MB, training will fail with a data quality error. Option B is incorrect because each category has 500 images, meaning many labels per category, not just one. Option D is incorrect because 500 images per category is sufficient. Option A is incorrect because JPEG is a supported format.

Answer

The images are in JPEG format

Answer

The dataset has only one label per category

Answer

The dataset does not have enough images

Question 6

You are a Salesforce admin at a nonprofit organization. The organization uses Einstein Engagement Scoring to prioritize donors for outreach. The model is based on donation history and event attendance. Recently, the model stopped generating new scores for recently added donors. You check the data source and see that the model's data includes the 'Contact' and 'Opportunity' objects. The data refresh is scheduled daily. The model status is 'Active'. What should you investigate first to resolve the issue?

Accepted Answer

Check if the model has reached its scoring capacity and needs retraining. Einstein Engagement Scoring models have a maximum scoring capacity (e.g., 2 million scored records per model). When new donors are added but the model stops generating scores, the most likely cause is that the model has reached this capacity and requires retraining to incorporate new records. Retraining resets the scoring queue and allows the model to score newly added donors.

Answer

Add the 'Lead' object to the data source

Answer

Increase the data refresh frequency to hourly

Answer

Check if the model was deactivated automatically

Question 7

A global company needs to ensure that customer data used for AI models complies with multiple regional regulations (GDPR, CCPA, LGPD). Which data governance practice is most effective?

Accepted Answer

Use a unified data catalog with tagging and classification.. A unified data catalog with tagging and classification (Option B) is the most effective practice because it enables a global company to apply different regulatory requirements (e.g., GDPR, CCPA, LGPD) to specific data sets based on their classification. This approach provides granular control over data usage, ensuring compliance without unnecessarily restricting data access.

Answer

Apply the strictest regulation globally.

Answer

Store all data in a single data warehouse.

Answer

Allow each region to manage its own data separately.

Question 8

Refer to the exhibit. A data access policy is defined for a customer data set. Which statement best describes this policy?

Accepted Answer

Only users in the EU region can read customer data, with emails and phones partially masked.. The policy shown in the exhibit applies a row-level security filter that restricts access to customer data based on the user's region, and a column-level masking rule that partially masks email and phone fields. Only users whose region attribute is set to 'EU' can view the rows, and even then, the email and phone columns are obfuscated (e.g., j***@example.com, ***-***-1234). This matches the behavior of a data masking policy combined with a regional access filter, which is a common pattern in Salesforce Data Cloud.

Answer

All users can read customer data.

Answer

All users can read customer data but only those in the EU can see unmasked emails.

Answer

Only users in the EU region can read customer data, with emails and phones fully hidden.

Question 9

Refer to the exhibit. The data pipeline is failing. What is the most likely cause?

Accepted Answer

Schema mismatch between source and target.. A schema mismatch between source and target is the most common cause of pipeline failures in data integration workflows within Salesforce Data Cloud. When the source data structure (e.g., field names, data types, or object relationships) does not match the target data model (e.g., a Data Cloud data lake object or Einstein Studio dataset), the pipeline cannot map or transform the data correctly, leading to errors during ingestion or transformation stages. This is especially relevant in Salesforce Data Cloud's data pipelines where schema validation is enforced at runtime.

Answer

Network timeout.

Answer

Missing required field in source data.

Answer

Insufficient memory.

Question 10

Refer to the exhibit. What is the most likely cause of this error?

Accepted Answer

A division formula in the data transform is dividing by a field that contains zero values.. The error shown in the exhibit is a division-by-zero runtime error, which occurs when a formula in a data transform attempts to divide a value by a field that contains zero. In Salesforce Data Cloud, data transforms execute field-level calculations, and if a divisor field holds a zero, the system throws a 'Divide by zero' exception. Option D correctly identifies this as the most likely cause because the error message explicitly indicates a division operation failed due to a zero divisor.

Answer

The source data is missing required fields.

Answer

The data transform has a recursive formula.

Answer

The target field expects an integer but source provides null.

Question 11

A company wants to build a sentiment analysis model using customer feedback. What is the best practice for labeling the training data?

Accepted Answer

Employ a diverse set of human labelers with clear guidelines. Best practice for labeling training data for sentiment analysis is to use multiple human labelers with clear guidelines to ensure consistency and reduce individual bias. Option A (unsupervised learning) avoids labeling but is not a labeling practice. Option B (single expert) introduces personal bias. Option D (automated keyword matching) is error-prone and misses nuance. Therefore, option C is correct.

Answer

Ignore labeling and use unsupervised learning

Answer

Have a single domain expert label all data

Answer

Use automated keyword matching to assign sentiment

Question 12

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.

Answer

Product prices must be stored in a custom currency field.

Answer

User profiles must include demographic data.

Answer

Product descriptions must be at least 100 characters long.

Question 13

A data scientist needs to feed customer interaction data into Einstein Discovery for predictive analysis. Which data format is required?

Accepted Answer

CSV. SV (Option A) because Einstein Discovery requires data in CSV format for predictive analysis. Option B (XML) is incorrect as XML is not a standard input format for Einstein Discovery. Option C (Parquet) is incorrect because Parquet is a columnar storage format that is not directly supported by Einstein Discovery. Option D (JSON) is incorrect as JSON is not the typical format used; Einstein Discovery expects tabular data in CSV.

Answer

XML

Answer

Parquet

Answer

JSON

Question 14

A company uses Salesforce Data Cloud to unify customer data from multiple sources for AI model training. After adding a new data source, model performance degrades significantly. What is the most likely cause?

Accepted Answer

Data duplication from overlapping sources. Data duplication from overlapping sources. When multiple data sources have overlapping records (e.g., the same customer from different systems), duplicating data can introduce bias and skew the model's learning, leading to performance degradation. Option A is incorrect because insufficient compute resources would likely affect all models equally, not just after adding a new source. Option B is incorrect because data labeling errors impact training quality, not data unification. Option C is incorrect because a schema mismatch typically causes data load failures or errors, not gradual performance degradation.

Answer

Insufficient compute resources

Answer

Data labeling errors

Answer

Data schema mismatch

Question 15

Which data type is most commonly used for image recognition AI models?

Accepted Answer

Unstructured data. Image recognition AI models typically process unstructured data, such as raw pixel values from images. Structured data (Option B) is organized in tables and not suitable for image inputs. Semi-structured data (like JSON) is not the primary format for images. Time-series data (Option C) is used for sequential measurements over time, not for images.

Answer

Structured data

Answer

Time-series data

Answer

Semi-structured data

Question 16

A team has limited labeled data for a Salesforce predictive model but wants to leverage a pre-trained model from a related task. Which machine learning approach should they use?

Accepted Answer

Transfer learning. Transfer learning (option A) is correct because it leverages a pre-trained model from a related task and fine-tunes it with limited labeled data. Unsupervised learning (option B) does not use labels and is not suitable for leveraging pre-trained models. Supervised learning (option C) typically requires large labeled datasets and does not inherently reuse pre-trained models. Reinforcement learning (option D) is for sequential decision-making, not classification or regression tasks with limited labeled data.

Answer

Unsupervised learning

Answer

Supervised learning

Answer

Reinforcement learning

Question 17

After deploying an AI model in Salesforce, the data scientist notices high accuracy on the training set but poor accuracy on new incoming data. What is this phenomenon called?

Accepted Answer

Overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data rather than the underlying general pattern, resulting in high accuracy on training but poor performance on new, unseen data. Option A is incorrect because data leakage leads to inflated performance on training and test sets due to improper information flow, but does not typically cause poor generalization on new data. Option C is incorrect because underfitting would show poor performance on both training and new data. Option D is incorrect because concept drift refers to a change in the data distribution over time, not an immediate discrepancy between training and new data performance.

Answer

Data leakage

Answer

Underfitting

Answer

Concept drift

Question 18

To ensure AI model fairness and avoid biased outcomes, which practice is most critical when preparing training data?

Accepted Answer

Use balanced training data. Using balanced training data ensures representation across different groups, which helps reduce bias. Option A is incorrect because using only recent data may not address bias; the issue is lack of balance, not recency. Option B is incorrect because increasing model complexity can cause overfitting but does not directly relate to fairness in training data. Option D is incorrect because adding more features may introduce noise or irrelevant information, but the critical factor for fairness is balanced data, not feature quantity.

Answer

Use only recent data

Answer

Increase model complexity

Answer

Use more features

Question 19

A company wants to integrate external customer behavior data into Salesforce to enhance AI predictions. Which Salesforce Data Cloud feature is specifically designed to ingest and map external data?

Accepted Answer

Data Streams. Data Streams in Data Cloud are specifically designed to ingest and map external data from various sources. Option A is incorrect because Apex triggers are custom code for automation, not data ingestion. Option C is incorrect because Einstein Studio is used for building and managing AI models, not for data ingestion. Option D is incorrect because Flow Builder is for process automation, not for data mapping or ingestion.

Answer

Apex triggers

Answer

Einstein Studio

Answer

Flow Builder

Question 20

Which TWO are best practices for data labeling in AI projects? (Choose two.)

Accepted Answer

Have multiple labelers cross-check annotations. Options A and E are correct. Having multiple labelers cross-check annotations (A) reduces errors and improves consistency, while using clear labeling guidelines (E) ensures uniformity and quality across the dataset. Option B is incorrect because labeling all available data immediately may lead to poor quality and isn't always necessary. Option C is incorrect because labeling only training data neglects validation and testing datasets, which are crucial for model evaluation. Option D is incorrect because relying solely on automated labeling tools can introduce inaccuracies without human oversight.

Answer

Label all available data immediately

Answer

Label only training data

Answer

Use automated labeling tools exclusively