AI Associate Data for AI Practice Test 8 — 15 Questions

Question 1

After applying a log transformation to a numeric feature, an Einstein model’s performance dropped significantly. What is the most likely cause?

Accepted Answer

The feature contained zero or negative values. Log transformation is undefined for zero or negative values because log(0) is negative infinity and log of a negative number is not a real number. In Salesforce Einstein, numeric features with such invalid transformed values can cause the model to fail or produce erratic results, leading to a significant drop in performance. This is the most likely cause given the symptom described.

Answer

The data volume was reduced by the transformation

Answer

The feature was normally distributed after transformation

Answer

The transformation introduced multicollinearity with other features

Question 2

A bank uses Einstein Discovery to generate insights about loan approval decisions. After deployment, they notice the model denies loans to a higher percentage of applicants from a certain postal code. Which action should be taken to ensure responsible AI?

Accepted Answer

Audit model outcomes for fairness across demographic groups and retrain if needed. Responsible AI requires auditing model outcomes for fairness across demographic groups, even when the disparity correlates with a non-protected attribute like postal code. In Einstein Discovery, postal code can act as a proxy for protected attributes such as race or socioeconomic status, and ignoring this could lead to discriminatory lending practices. Auditing allows the team to detect and mitigate bias, and retraining with fairness constraints ensures the model aligns with ethical AI principles.

Answer

Ignore the discrepancy because postal code is not a protected attribute

Answer

Retrain the model using only recent loan data

Answer

Remove the postal code field from the model

Question 3

A company plans to use Einstein Discovery to analyze sales data. Which data preparation step is essential for time-series forecasting?

Accepted Answer

Ensure date fields are properly formatted and contain sufficient historical range. For time-series forecasting in Einstein Discovery, the date field must be properly formatted (e.g., as a date or datetime data type) and contain a sufficient historical range to identify patterns like seasonality and trends. Without adequate historical data, the model cannot learn temporal dependencies, making this step essential.

Answer

Remove all outliers in sales amounts

Answer

Remove duplicate records

Answer

Scale all numeric fields to a 0-1 range

Question 4

A company is training a customer service chatbot using historical conversation logs. Which TWO data preparation practices should be followed to ensure data quality?

Accepted Answer

Include answers with varied phrasing to enhance language variety. Training a chatbot on varied phrasing (e.g., synonyms, different sentence structures) improves its ability to understand and generate natural language responses. This practice enhances the model's robustness and generalization, preventing overfitting to a narrow set of expressions and ensuring it can handle the diverse ways customers phrase their queries.

Answer

Exclude all user identifiers to protect privacy

Answer

Include only successful interactions that were resolved

Answer

Filter only English conversations for consistency

Question 5

Before training an Einstein Prediction model, a data analyst must perform data quality checks. Which THREE checks are most critical?

Accepted Answer

Remove duplicate records that could cause data leakage. Duplicate records can cause data leakage by allowing the model to see the same or highly similar data in both training and validation splits, leading to overfitting and inflated performance metrics. Removing duplicates ensures that the model generalizes to unseen data rather than memorizing repeated instances.

Answer

Confirm that label distribution matches the target baseline

Answer

Ensure all features follow a normal distribution

Question 6

A data scientist is preparing numeric features for a regression model. Which TWO transformations are commonly applied to improve model performance?

Accepted Answer

Normalize to a 0-1 range. Normalizing features to a 0-1 range (min-max scaling) ensures that all numeric features contribute equally to the model, preventing features with larger magnitudes from dominating the gradient descent optimization. This is especially important for distance-based algorithms like k-nearest neighbors or neural networks, where feature scale directly impacts convergence speed and model accuracy.

Answer

Remove outliers beyond 3 standard deviations

Answer

Convert numbers to string labels

Answer

Apply one-hot encoding

Question 7

Refer to the exhibit. A data analyst has defined this field mapping for Einstein Prediction Builder. Which data issue would most likely arise from this mapping?

Accepted Answer

The 'Id' field should be excluded as it can cause data leakage and overfitting. Including the 'Id' field in Einstein Prediction Builder can cause data leakage and overfitting. The 'Id' field is a unique identifier that has no predictive value for the target outcome, but the model could learn to memorize specific records based on it, leading to poor generalization on unseen data.

Answer

The 'LeadSource' field should be mapped to 'Text' instead of 'Category' to preserve verbatim values

Answer

The 'Amount' field should be mapped to 'Category' to discretize the values

Answer

The 'CloseDate' field should be mapped to 'Text' to avoid date parsing issues

Question 8

Refer to the exhibit. A data file for click-through model training has the above content. Which data quality issue is most critical to address before training?

Accepted Answer

Missing value in the Conversions column for the third row. Missing values in the Conversions column directly impact the supervised learning target variable. If the label (conversion) is missing for a training instance, the model cannot learn the correct mapping from features to outcome, leading to biased or incomplete training. This is a critical data quality issue that must be addressed before training, typically via imputation or row removal.

Answer

The header row is missing a column name for the last field

Answer

Inconsistent date formats across rows

Answer

Clicks column is an integer but may need scaling

Question 9

Refer to the exhibit. A data analyst runs a profile on a dataset and sees these statistics. Based on best practices, which action should be taken first?

Accepted Answer

Remove the 200 duplicate records. Duplicate records introduce bias and redundancy, leading to overfitting or skewed model performance. Removing duplicates is a standard first step in data preprocessing to ensure data integrity before handling missing values or outliers. In the context of the AI Associate exam, best practices prioritize deduplication early in the data cleaning pipeline.

Answer

Impute the 500 missing values with the mean

Answer

Remove the 50 outliers in the Amount field

Answer

Skip all preprocessing and train the model directly

Question 10

A data scientist notices that the model accuracy drops significantly after retraining with new data. Upon inspection, they find that many records have missing values for a key feature. Which data quality improvement should be prioritized first?

Accepted Answer

Implement imputation for missing feature values.. The core issue is that missing values in a key feature introduce noise and bias, directly degrading model performance. Imputation (option A) is the most direct and impactful first step because it preserves the dataset size and feature set, allowing the model to learn from complete patterns. Without addressing missing data first, other quality improvements like normalization or feature reduction would be applied to corrupted data, failing to resolve the root cause.

Answer

Normalize the feature range.

Answer

Reduce the number of features.

Answer

Remove duplicate records.

Question 11

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?

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.

Answer

Randomly select 2,000 tickets and label them manually.

Answer

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

Answer

Have subject matter experts label all 10,000 tickets.

Question 12

For an AI project, data must be stored in a way that supports both training and real-time inference. Which storage solution meets this requirement?

Accepted Answer

Data lake (e.g., Amazon S3 or Azure Data Lake). A data lake (e.g., Amazon S3 or Azure Data Lake) is the correct choice because it can store vast amounts of raw, unstructured, and structured data in its native format, making it ideal for training AI models on diverse datasets. At the same time, data lakes support real-time inference by enabling direct access to data via APIs or streaming services (e.g., AWS Lambda or Azure Functions) without the latency of transforming data into a schema-on-write structure. This dual capability—handling both batch processing for training and low-latency reads for inference—is a key requirement that other storage solutions cannot fulfill as effectively.

Answer

Data warehouse (e.g., Snowflake)

Answer

Relational database (e.g., PostgreSQL)

Answer

In-memory cache (e.g., Redis)

Question 13

A data engineer needs to create a feature that represents the average purchase amount per customer over the last 30 days. The transactional data is timestamped. Which feature engineering technique is most appropriate?

Accepted Answer

Rolling average of purchase amounts over a 30-day window. A rolling average over a 30-day window directly computes the average purchase amount per customer for only the most recent 30 days of transactions, which matches the requirement of a time-sensitive feature. This technique uses a sliding window function (e.g., AVG() with a ROWS or RANGE frame in SQL, or rolling().mean() in pandas) that respects the timestamp order, ensuring only relevant data contributes to the feature.

Answer

Sum of all purchase amounts per customer

Answer

Count of purchases per customer

Answer

Minimum purchase amount per customer

Question 14

A healthcare AI model uses patient data. The legal team requires that all data used for training be de-identified according to HIPAA Safe Harbor method. Which data handling process satisfies this?

Accepted Answer

Remove all 18 HIPAA identifiers from each record.. The HIPAA Safe Harbor method specifically requires the removal of all 18 identifiers listed in the HIPAA Privacy Rule from each patient record. This includes direct identifiers like names, addresses, and Social Security numbers, as well as indirect identifiers such as dates and geographic subdivisions. By removing these 18 identifiers, the data is considered de-identified and no longer subject to HIPAA restrictions, allowing it to be used for AI training.

Answer

Generate synthetic data that mimics patient records.

Answer

Remove patient names and replace with IDs.

Answer

Anonymize data by aggregating into groups of 10 or more.

Question 15

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.

Answer

Feature engineering step

Answer

Model deployment step

Answer

Data validation step