AI Associate Data for AI Practice Test 9 — 15 Questions

Question 1

A company is building a text classification model for customer support tickets. They have a dataset of 10,000 tickets. The team decides to use active learning for labeling. Which approach best aligns with active learning principles?

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

Accepted Answer

Train a preliminary model and prioritize labeling tickets with low prediction confidence.. Active learning iteratively selects the most informative unlabeled data points for labeling, typically those with low prediction confidence from a preliminary model. This minimizes labeling effort while maximizing model performance, which is the core principle of active learning.

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Randomly select 2,000 tickets and label them manually.

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Use a pre-trained model to label all tickets automatically.

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Have subject matter experts label all 10,000 tickets.

Question 2

A team is building a pipeline to train a model daily. The source data arrives in CSV files but needs to be converted to Parquet for efficiency. Which pipeline step should perform this conversion?

Accepted Answer

Data ingestion step. The data ingestion step is responsible for bringing raw data into the pipeline, including format conversions like CSV to Parquet. Converting to Parquet at ingestion improves storage efficiency and query performance for downstream processing, as Parquet uses columnar storage and compression.

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Feature engineering step

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Model deployment step

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Data validation step

Question 3

During data transformation, a data scientist applies one-hot encoding to a categorical feature with 50 unique values. The resulting dataset has 50 new columns. What is a potential drawback of this transformation?

Accepted Answer

High cardinality leading to sparse data and overfitting. One-hot encoding a categorical feature with 50 unique values creates 50 binary columns, each representing one category. This high cardinality leads to a very sparse matrix (most entries are 0), which can cause the model to overfit by learning noise from rare categories, especially when the dataset is not large enough to support such dimensionality.

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Reduction in training time

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Increased interpretability of the model

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Loss of ordinal information in categories

Question 4

An organization uses Salesforce Data Cloud to unify customer data from multiple sources. They want to ensure that data lineage is tracked for AI models. Which practice supports data lineage?

Accepted Answer

Maintain metadata that records source, transformations, and dependencies.. Maintaining metadata that records source, transformations, and dependencies is the correct practice because data lineage for AI models requires a complete audit trail of where data originated, how it was transformed, and its dependencies. In Salesforce Data Cloud, this metadata is captured through the Data Catalog and Data Lineage feature, which tracks the flow of data from source objects through calculated insights and segments to AI model inputs, ensuring transparency and reproducibility.

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Use data partitioning to improve query performance.

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Implement role-based access control on datasets.

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Regularly run data profiling to check completeness.

Question 5

Which TWO of the following are common dimensions of data quality that must be addressed for AI training?

Accepted Answer

Accuracy of values. Accuracy of values (B) is a core data-quality dimension because AI models learn from the data they are trained on, so incorrect, outdated, or mislabeled values directly degrade model predictions and reliability. Completeness of records (E) is equally fundamental, since missing fields, null values, or absent records create gaps that bias training and prevent the model from learning the full feature space. Together, accuracy and completeness are standard dimensions in data-quality frameworks (alongside timeliness, uniqueness, validity, and consistency) that must be assessed before AI training. Storage efficiency (A) is an infrastructure/cost concern, not a data-quality dimension. Encryption strength (C) is a security control, not a measure of data quality. Consistency with external benchmarks (D) is not a standard data-quality dimension; consistency in data quality refers to agreement across internal sources or formats, not conformity to outside benchmarks.

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Storage efficiency

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Encryption strength

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Consistency with external benchmarks

Question 6

Which TWO considerations are critical when planning data labeling for a computer vision project in a regulated industry?

Accepted Answer

Mitigating labeler bias to ensure fairness. Option B is correct because in a regulated industry, labeler bias can produce systematically skewed annotations that lead to unfair or discriminatory model behavior, so bias mitigation and fairness auditing are critical planning considerations. Option C is correct because regulated industries must ensure data labeling workflows comply with data privacy regulations such as GDPR, including lawful processing, data minimization, consent, and cross-border transfer restrictions. Option A is not the key consideration here, since where label files are stored is an implementation detail that matters mainly as part of broader privacy and security compliance. Option D is not critical in this context, as timeline and budget are general project-management constraints rather than regulated-industry-specific labeling concerns. Option E is not critical here either, because choosing between bounding boxes and segmentation masks is an annotation-format decision driven by model requirements, not by regulatory compliance or fairness.

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Data storage location for label files

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Labeling timeline and budget constraints

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Choosing between bounding boxes and segmentation masks

Question 7

Which THREE types of data sources are commonly integrated into Salesforce Data Cloud for AI use cases?

Accepted Answer

Third-party demographic data. Option A (Third-party demographic data) is correct because Data Cloud commonly ingests external datasets such as demographic or firmographic attributes from providers to enrich unified customer profiles for AI segmentation and scoring. Option B (Web and mobile app engagement data) is correct because behavioral event streams from websites and mobile apps are ingested via sources like Web/Mobile SDKs or Interaction/Ingestion APIs to power real-time personalization and predictive models. Option C (CRM transaction records) is correct because CRM data such as Sales/Service Cloud objects and transactional history are core Data Cloud sources, harmonized into the Customer 360 data model via Data Streams and Data Model Objects. Option D (Model training logs) does not belong because those are internal artifacts of model development/training pipelines, not a standard customer data source integrated into Data Cloud. Option E (Data transformation scripts) does not belong because scripts are processing logic (e.g., SQL transformations in Data Cloud), not a data source type ingested for AI use cases.

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Model training logs

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Data transformation scripts

Question 8

Refer to the exhibit. A data pipeline fails during the DataTransformation stage. What is the most likely root cause?

Accepted Answer

A transformation step references the 'age' column, but it is not present in the input data.. The error occurs during the DataTransformation stage, which processes data after it has been successfully ingested. If a transformation step references the 'age' column but that column is missing from the input data, the pipeline will fail with a column-not-found error. This is a common schema mismatch issue in data pipelines, distinct from connectivity or data quality problems.

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The pipeline has a network connectivity issue.

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The data type for 'income' is incorrect.

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The 'age' column contains null values.

Question 9

Refer to the exhibit. A data transformation configuration is shown. Which of the following describes the outcome of applying this transformation?

Accepted Answer

'color' is one-hot encoded into multiple binary columns; 'price' and 'weight' are standardized to have mean 0 and variance 1.. The transformation configuration applies a one-hot encoder to the 'color' categorical column, creating multiple binary columns, and applies a standard scaler to the 'price' and 'weight' numerical columns, centering them to mean 0 and scaling to unit variance. This is a common preprocessing pipeline that handles mixed data types appropriately.

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The transformation is invalid because one-hot encoding cannot be combined with scaling.

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Only 'color' is transformed; 'price' and 'weight' are unchanged.

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'color' is scaled to [0,1] and 'price', 'weight' are one-hot encoded.

Question 10

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.. 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 11

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 12

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 13

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.. 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 14

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.

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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 15

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'.. 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.

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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.