AI Associate Data for AI — Timed 100 Questions

Question 1

A company uses Salesforce Data Cloud to unify customer data from multiple sources. After connecting a data stream, they notice that records are missing from the unified profile. What is the most likely cause?

100 minute time limit — choose an answer to begin.

Accepted Answer

The reconciliation rule is not configured for the data source.. Reconciliation rules in Salesforce Data Cloud define how records from different data sources are matched and merged into a unified profile. If a reconciliation rule is not configured for a data source, records from that source may not be properly linked to existing profiles, leading to missing records in the unified view. This is a common configuration step that must be completed after connecting a data stream.

Answer

The data stream object is not a standard Salesforce object.

Answer

The data stream is not activated for identity resolution.

Answer

The data source is not from Salesforce, so it cannot be unified.

Question 2

A company wants to use Einstein Article Recommendations to suggest knowledge articles to support agents. What is a prerequisite for this feature?

Accepted Answer

Knowledge articles must be created and published.. Einstein Article Recommendations requires that knowledge articles are created and published in the Salesforce Knowledge base. The feature uses natural language processing (NLP) to match the context of a case or conversation with published articles, so unpublished or draft articles cannot be recommended. Without published articles, the AI model has no content to analyze or suggest.

Answer

Articles must be of a specific type, such as FAQ.

Answer

The org must be enabled for Einstein features.

Answer

A case must be open for the recommendation to appear.

Question 3

Which THREE factors should be considered when evaluating the quality of a dataset for an AI model?

Accepted Answer

Presence of outliers that may skew the model.. Option B is correct because outliers can disproportionately influence model parameters (especially in distance-based or linear models), skewing predictions and degrading generalization, so their presence must be assessed during dataset quality evaluation. Option D is correct because a high percentage of missing values in key fields reduces the effective training signal, can introduce bias if missingness is non-random, and may require imputation or exclusion decisions that directly affect model reliability. Option E is correct because duplicate records inflate the apparent size of the dataset, cause overrepresentation of certain patterns, and can lead to data leakage between training and validation splits, undermining honest performance estimates. Option A is not a quality factor per se—record count reflects dataset size/quantity rather than quality, and a large dataset can still be noisy, biased, or incomplete. Option C is also not a quality metric—the number of distinct labels describes the target's cardinality or class structure (relevant to task type and class balance), not the intrinsic quality of the data itself.

Answer

Total number of records available for training.

Answer

Number of distinct labels in the outcome field.

Question 4

Which TWO data sources can be used with Einstein Prediction Builder?

Accepted Answer

Data Cloud objects using the harmonized data model.. Einstein Prediction Builder can build predictions on standard Salesforce objects such as Account and Opportunity (option C), because it reads records directly from the object's fields via the standard Salesforce data model to train and score the model. It can also use Data Cloud objects that follow the harmonized data model (option B), since Prediction Builder supports Data Cloud (formerly Customer 360 Audiences/Genie) as a data source for building predictions on unified profile data. Options A, D, and E are not supported data sources: files in Salesforce Files, Chatter feed posts, and dashboard/report snapshots are not structured record data that Prediction Builder can query as an object for model training and scoring.

Answer

Files uploaded to Salesforce Files.

Answer

Chatter feed posts.

Answer

Dashboard and report snapshots.

Question 5

During the data preparation phase for an AI model, a data engineer discovers that the 'AnnualRevenue' field contains some negative values. What is the best course of action?

Accepted Answer

Investigate the data source to correct the negative values. Negative revenue values typically indicate data entry errors, system bugs, or incorrect data transformations. The best practice in data preparation is to investigate the source system to understand why negative values were generated and correct them at the origin, ensuring data integrity before any imputation or deletion. Simply deleting or imputing without root-cause analysis can introduce bias or mask underlying data quality issues.

Answer

Delete all records with negative revenue

Answer

Replace negative values with the mean of positive values

Answer

Keep negative values as they might represent returns or refunds

Question 6

A company wants to use its data from Salesforce to train an Einstein AI model. However, they need to exclude records where the customer has opted out of data use. Which field should they configure in the Data Manager?

Accepted Answer

A checkbox field named Data Use Opt-Out. The Data Manager in Einstein AI uses a standard checkbox field named 'Data Use Opt-Out' to identify records that should be excluded from model training. When this checkbox is true, the system automatically filters out those records during data preparation, ensuring compliance with data privacy requirements.

Answer

The Record Type of the object

Answer

A picklist field on the object

Answer

A formula field that evaluates to true

Question 7

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 8

A large enterprise is using Einstein Lead Scoring and notices that the model score is not updating for leads created via a web-to-lead form. The leads have all required fields populated. The admin has verified that the model is active and the data source includes the Lead object. What could be causing the score to remain static?

Accepted Answer

The model has not scored enough leads to start scoring new ones. Einstein Lead Scoring requires a minimum number of scored leads (typically 500) before it begins scoring new leads. Until that threshold is met, the model remains in a 'training' or 'pending' state and will not update scores for any leads, including those from web-to-lead forms. The admin has confirmed the model is active and the Lead object is included, so the most likely cause is that the model has not yet processed enough leads to start scoring.

Answer

The data source excludes leads created by web-to-lead

Answer

Web-to-lead leads are not supported by Einstein AI

Answer

The model is not yet activated

Question 9

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 10

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 11

A team is labeling text data for a sentiment analysis model. To ensure consistency and quality, which practice should they prioritize?

Accepted Answer

Use majority voting among multiple labelers.. Majority voting among multiple labelers reduces individual bias and errors, improving label consistency and quality for training data. This approach is standard in supervised learning for sentiment analysis because it aggregates diverse judgments, leading to more reliable ground truth labels.

Answer

Use a single expert labeler for all data.

Answer

Label all data by a single expert labeler.

Answer

Allow each labeler to interpret guidelines freely.

Question 12

A data pipeline fails intermittently when processing large CSV files. The error log shows 'OutOfMemoryError'. Which configuration change is most likely to resolve this?

Accepted Answer

Increase the heap memory for the processing application.. The OutOfMemoryError indicates that the Java Virtual Machine (JVM) heap space is exhausted while processing large CSV files. Increasing the heap memory (e.g., using -Xmx flag) allocates more memory to the application, allowing it to handle larger datasets without crashing. This directly addresses the root cause of insufficient memory for the data pipeline's processing workload.

Answer

Use a smaller file size limit.

Answer

Increase the number of worker threads.

Answer

Switch to XML format.

Question 13

A dataset contains a 'date' column. Which feature engineering technique would best capture both long-term trends and seasonal patterns?

Accepted Answer

Create cyclic features (sin/cos of month, day).. Cyclic features using sine and cosine transformations preserve the circular nature of temporal data (e.g., month 12 and month 1 are adjacent, not far apart). This allows a model to learn both long-term trends (via the year component) and seasonal patterns (via the cyclic encoding of month and day) without imposing a false linear ordering. In contrast, simple numeric extraction treats time as linear, which can misrepresent seasonal cycles.

Answer

Extract year, month, day as separate features.

Answer

Use only the day of week.

Answer

Drop the date column.

Question 14

Which THREE are key dimensions of data quality that directly impact AI model performance?

Accepted Answer

Consistency.. Consistency (A) is a key data-quality dimension because contradictory or conflicting values across sources/systems cause the model to learn unstable or conflicting patterns, degrading prediction reliability. Accuracy (C) matters because incorrect labels or feature values directly inject errors into training and evaluation, so the model learns wrong mappings and produces unreliable outputs. Completeness (E) is essential because missing fields, nulls, or absent records bias training, reduce coverage of the feature space, and can cause the model to fail on underrepresented cases. Timeliness (B) is not marked correct here because it concerns freshness/latency of data rather than an intrinsic quality dimension that directly determines whether the values are correct and usable for learning. Data volume (D) is not marked correct because quantity alone is not a quality dimension; large but inaccurate, inconsistent, or incomplete data can still harm AI model performance.

Answer

Timeliness.

Answer

Data volume.

Question 15

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 16

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 17

Refer to the exhibit. A developer runs a SOQL query. What does the output indicate?

Accepted Answer

The query returned 10 records in total.. The SOQL query output shows '10 records returned' with no error or partial result indicator, confirming that the query completed successfully and returned exactly 10 records. In Salesforce SOQL, the query result includes a 'totalSize' field that reflects the total number of records matching the query criteria, and here it matches the number of records displayed, indicating a complete and successful retrieval.

Answer

The query is still processing.

Answer

The output is incomplete.

Answer

The query failed.

Question 18

A retail company uses Einstein Next Best Action with customer data from Data Cloud. The recommendations are not personalized. The admin checks the data quality dashboard and finds that the 'Customer_Profile' object has 40% records with missing 'PreferredChannel' field. What is the best course of action?

Accepted Answer

Impute the missing values using the mode of the field.. Imputing missing values using the mode (most frequent value) of the 'PreferredChannel' field is a standard data preprocessing technique that preserves the dataset size and statistical distribution. In Einstein Next Best Action, missing categorical data can degrade model personalization, and mode imputation is a simple, effective way to handle this without losing records or altering the model structure.

Answer

Remove the field from the model.

Answer

Increase the data refresh frequency.

Answer

Train the model with only records that have non-null PreferredChannel.

Question 19

A company wants to use Einstein Reply Recommendations in Service Cloud. What data is required to train the model?

Accepted Answer

Case comments and chat transcripts.. Einstein Reply Recommendations in Service Cloud uses historical service interactions to suggest relevant replies to agents. The model is trained on case comments and chat transcripts because these contain the natural language patterns and resolutions that agents actually use in real-time service conversations, enabling the AI to learn effective response strategies.

Answer

Knowledge articles only.

Answer

Email templates.

Answer

Historical email replies and customer satisfaction ratings.

Question 20

A company uses Einstein Prediction Builder to predict customer churn. The model's accuracy is low. The admin reviews the training data and notices that only 2% of records are churned. What should the admin do to improve the model?

Accepted Answer

Use oversampling techniques.. When a dataset has severe class imbalance (only 2% churned records), the model becomes biased toward predicting the majority class (non-churned), leading to low accuracy despite high apparent performance. Oversampling techniques, such as SMOTE or random oversampling, artificially increase the number of churned records in the training set to balance the classes, allowing Einstein Prediction Builder to learn patterns for the minority class more effectively.

Answer

Remove the churned records.

Answer

Increase the amount of training data.

Answer

Change the prediction field.