AI0-001 AI Models and Data Engineering Practice Test 6 — 15 Questions

Question 1

A data engineer needs to combine two datasets, each with unique customer_id, to include all records from both datasets. Which join type should be used?

Accepted Answer

FULL OUTER JOIN. A FULL OUTER JOIN returns all records from both datasets, matching rows where the customer_id is present in both and filling in NULLs for missing matches. This is the only join type that guarantees every unique customer_id from either dataset appears in the result, which is exactly what the requirement specifies.

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RIGHT JOIN

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LEFT JOIN

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INNER JOIN

Question 2

A data engineer needs to store training data in a format that supports columnar pruning during model training. Which storage format should they use?

Accepted Answer

Parquet. Parquet is the correct choice because it is a columnar storage format that enables column pruning, allowing the training process to read only the columns needed for model training rather than entire rows. This reduces I/O and speeds up data loading, which is critical for large-scale AI/ML workloads. Unlike row-oriented formats, Parquet stores data by columns, making it efficient for analytical queries and feature selection.

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XML

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JSON

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CSV

Question 3

During model deployment, a data engineer notices that the model's predictions are consistently lower than expected due to a shift in the distribution of one feature between training and production. Which technique should be used to detect and quantify this shift?

Accepted Answer

Calculate the population stability index (PSI). The Population Stability Index (PSI) is specifically designed to detect and quantify shifts in the distribution of a feature or score between two populations, such as training and production datasets. It measures the stability of the feature by comparing the proportion of observations in each bin across the two time periods, making it the correct choice for diagnosing distribution drift in model deployment.

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Compute the root mean square error (RMSE)

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Generate a confusion matrix

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Perform a t-test on the means

Question 4

A data engineer is preparing a dataset for a binary classification model. The dataset has 10,000 samples with 100 features. To improve model performance and reduce training time, the engineer decides to perform feature selection. Which two techniques are appropriate for this task? (Select TWO).

Accepted Answer

Recursive Feature Elimination (RFE). Recursive Feature Elimination (RFE) (B) is a wrapper-based feature selection method that repeatedly trains a model, ranks features by importance (e.g., coefficients or feature importances), and prunes the least important ones until the desired number of features remains, directly reducing the 100-feature space to improve performance and cut training time. L1 Regularization (C) — Lasso — adds a penalty equal to the absolute value of coefficients to the loss function, which drives many feature coefficients exactly to zero, effectively performing embedded feature selection and yielding a sparse model. Normalization (A) is a scaling preprocessing step (e.g., min-max or z-score) that changes feature magnitudes but does not remove features, so it is not feature selection. One-Hot Encoding (D) is a categorical-encoding transformation that expands categorical variables into binary columns, increasing dimensionality rather than reducing it. Principal Component Analysis (E) is a dimensionality-reduction technique that creates new uncorrelated components from linear combinations of the original features, but it is not feature selection because it does not retain or select the original features.

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Normalization

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One-Hot Encoding

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Principal Component Analysis (PCA)

Question 5

Refer to the exhibit. A data engineer is training a binary classification neural network. The loss fluctuates and does not converge. Which hyperparameter adjustment is most likely to stabilize training?

Accepted Answer

Decrease the learning rate. Fluctuating loss that fails to converge during neural network training is a classic sign of an excessively high learning rate, causing the optimizer to overshoot the minimum. Decreasing the learning rate allows the gradient descent updates to take smaller, more stable steps, which smooths the loss curve and promotes convergence.

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Change the activation to tanh

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Add dropout after each layer

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Increase the number of units in the first dense layer

Question 6

Refer to the exhibit. A data engineer runs a validation report on the customers table. The "income" column has 12 null values. Which imputation strategy is most appropriate for this column?

Accepted Answer

Replace nulls with the median income per region. Imputing missing income values with the median per region preserves the central tendency of each regional subgroup, which is robust to outliers and maintains the distributional characteristics of the data. This strategy is particularly appropriate for income data, which often exhibits skewness and regional variation, ensuring that the imputed values are contextually relevant and do not distort downstream analytics or machine learning models.

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Remove rows with null income

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Replace nulls with 0

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Replace nulls with the mean income of the entire dataset

Question 7

A company is deploying an AI model to recommend products. The model's training data included historical purchases from the past two years, but the business environment has changed significantly due to a market shift. What is the most likely issue affecting model performance?

Accepted Answer

Concept drift. Concept drift occurs when the statistical properties of the target variable change over time, degrading model performance. In this scenario, the market shift alters customer purchasing patterns, making the historical training data (from the past two years) no longer representative of current behavior. This is the most likely issue because the model's recommendations will be based on outdated correlations.

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Overfitting

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Underfitting

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Data leakage

Question 8

A data engineer is building a pipeline to ingest streaming data from IoT sensors. Which data storage solution is best suited for real-time analytics on timestamped sensor readings?

Accepted Answer

Time-series database. Time-series databases (TSDBs) are optimized for high-ingest rates of timestamped data and provide efficient downsampling, retention policies, and time-based aggregation functions. For IoT sensor streaming, a TSDB like InfluxDB or TimescaleDB delivers sub-second query performance on time-range scans, which is essential for real-time analytics.

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Data warehouse

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Relational database

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Data lake

Question 9

During feature engineering, a data scientist creates a new feature that is a linear combination of two existing features. What risk does this pose to the model?

Accepted Answer

Multicollinearity. Creating a new feature as a linear combination of two existing features introduces perfect multicollinearity, where the new feature is an exact linear function of the original ones. This violates the assumption of no perfect multicollinearity in linear models, causing the design matrix to become singular and making coefficient estimates unstable or impossible to compute. Even in non-linear models, high multicollinearity can inflate variance and reduce interpretability.

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Data leakage

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Overfitting

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Underfitting

Question 10

A team is using a pre-trained language model for sentiment analysis. They want to adapt it to a specific domain with limited labeled data. Which approach is most efficient?

Accepted Answer

Fine-tune the pre-trained model on domain data. Fine-tuning a pre-trained language model on domain-specific labeled data is the most efficient approach because it leverages the general language understanding learned from large corpora while adapting to the target domain with minimal additional data. This process uses transfer learning, where only the final layers or a subset of parameters are updated, significantly reducing the amount of labeled data and compute required compared to training from scratch.

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Use the pre-trained model as is

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Train a new model from scratch

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Ensemble multiple pre-trained models

Question 11

A data pipeline processes customer data from multiple sources. The data quality check reveals duplicate records. Which step should the pipeline include to handle this?

Accepted Answer

Data deduplication. Duplicate records in a data pipeline compromise data integrity and downstream analytics. Data deduplication (Option A) is the correct step because it identifies and removes redundant entries based on key fields or fuzzy matching, ensuring each customer record is unique. This is a core data quality operation in ETL pipelines, often implemented via hash-based comparison or SQL window functions like ROW_NUMBER().

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Data encryption

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Data transformation

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Data validation

Question 12

An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?

Accepted Answer

Quantization. Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which decreases memory footprint and speeds up computation on resource-constrained devices like mobile phones. This directly lowers inference latency without requiring additional hardware or architectural changes.

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Increase batch size

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Add more layers

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Use a larger model

Question 13

A data engineer is designing a feature store for machine learning. Which THREE components are essential for a feature store? (Choose THREE.)

Accepted Answer

Data ingestion pipeline. A feature store fundamentally requires a data ingestion pipeline (A) to pull raw data from sources such as batch files, streaming events, or databases and transform it into features, since without ingestion there would be no features to store or serve. The online serving layer (B) is essential because it provides low-latency access to the latest feature values for real-time inference, typically backed by a low-latency store like Redis or DynamoDB. The feature repository (C) is also essential as the central catalog that stores feature definitions, metadata, and versioned feature values, enabling reuse and consistency between training and serving. Experiment tracking (D) and model registry (E) are important MLOps components but belong to the model development and deployment lifecycle, not to the core architecture of a feature store, so they are not essential components of a feature store itself.

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Experiment tracking

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Model registry

Question 14

A data scientist notices that a binary classification model consistently predicts the majority class. Which data engineering technique should be applied?

Accepted Answer

Oversampling. Oversampling (Option D) is correct because the model's bias toward the majority class indicates a class imbalance problem. By synthetically increasing the number of minority class samples (e.g., using SMOTE or random oversampling), the training data becomes more balanced, allowing the classifier to learn decision boundaries that are not skewed toward the majority class.

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Feature scaling

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Dimensionality reduction

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Polynomial features

Question 15

A team is building a regression model to predict house prices. Which data transformation is most appropriate if the target variable exhibits right skewness?

Accepted Answer

Log transformation. Log transformation is the most appropriate technique for right-skewed target variables because it compresses the long tail, making the distribution more symmetric and closer to Gaussian. This stabilizes variance and often improves the performance of regression models that assume normally distributed errors, such as linear regression.

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Principal component analysis (PCA)

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Standardization (Z-score)

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One-hot encoding