AI0-001 Practice Test 34 — 15 Questions

Question 1

Refer to the exhibit. A batch inference job fails with the given logs. What is the most likely root cause of the failure?

Accepted Answer

The input data contains missing values that are not handled in preprocessing. The logs indicate a 'ValueError' or similar exception when the batch inference job attempts to process the input data. This error typically arises when the preprocessing pipeline encounters missing values (e.g., NaN or None) that it cannot handle, causing the job to fail. Option B is correct because missing values not handled in preprocessing are a common root cause for such failures, especially when the training data had no missing values but the inference data does.

Answer

The input data has values that exceed the model's expected range

Answer

The model was not trained to handle categorical features

Answer

The model version is outdated and incompatible with the current preprocessing pipeline

Question 2

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.

Answer

Principal component analysis (PCA)

Answer

Standardization (Z-score)

Answer

One-hot encoding

Question 3

Which THREE are common pitfalls when operationalizing AI models? (Select THREE.)

Accepted Answer

Training-serving skew due to differences in data preprocessing. Option A is correct because training-serving skew occurs when the data preprocessing logic used during model training differs from that used during inference in production. This is a common pitfall in operationalizing AI models, as even minor discrepancies in feature engineering, normalization, or encoding can cause significant performance degradation. For example, using different libraries or versions for tokenization between training and serving pipelines directly leads to skew.

Answer

Using simpler models that are easier to debug

Answer

Automating the model retraining process

Question 4

A team is building a regression model to predict house prices. The dataset includes numerical features (square footage, number of bedrooms) and categorical features (neighborhood, roof type). The categorical features have high cardinality (neighborhood has 200+ unique values). Which encoding strategy should the team use to avoid overfitting and maintain model interpretability?

Accepted Answer

Target encoding with regularization.. Target encoding with regularization is the best choice because it replaces each categorical value with the mean of the target variable for that category, which captures the relationship between the category and house prices. Regularization (e.g., adding a prior or using cross-validation) shrinks the encoded values toward the global mean, preventing overfitting on rare categories (e.g., neighborhoods with only a few houses). This maintains interpretability because each encoded value directly reflects the average price impact of that category, unlike black-box embeddings.

Answer

Label encoding.

Answer

Binary encoding.

Answer

One-hot encoding with feature selection.

Question 5

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.

Answer

Compute the root mean square error (RMSE)

Answer

Generate a confusion matrix

Answer

Perform a t-test on the means

Question 6

A data engineer discovers that a dataset contains duplicate rows. Which data cleaning step is MOST appropriate?

Accepted Answer

Remove duplicate rows.. Removing duplicate rows is the most appropriate data cleaning step because duplicate rows can bias statistical analyses and machine learning models by overrepresenting certain observations. In data engineering, deduplication is a standard preprocessing step to ensure data integrity and avoid skewed results. Option C directly addresses this by eliminating redundant entries without introducing artificial values or altering the data distribution.

Answer

Keep only the first occurrence.

Answer

Fill duplicates with the mean.

Answer

Convert duplicates to categorical.

Question 7

Refer to the exhibit. A data scientist reviews the MLflow run for a Random Forest model on customer churn data. What is the most likely issue with this model?

Accepted Answer

The model is overfitting because there is a large gap between train and validation accuracy.. Option B is correct because a large gap between training accuracy (e.g., 0.99) and validation accuracy (e.g., 0.82) indicates that the Random Forest model has memorized the training data but fails to generalize to unseen validation data. This is the classic symptom of overfitting, where the model captures noise rather than the underlying pattern. In MLflow, comparing train and validation metrics directly reveals this discrepancy.

Answer

The model is underfitting because training accuracy is too high.

Answer

The model is performing well because validation accuracy is above 0.8.

Answer

The model has a data leak because dataset version is v2.

Question 8

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.

Answer

RIGHT JOIN

Answer

LEFT JOIN

Answer

INNER JOIN

Question 9

A machine learning model for credit card fraud detection is deployed. The model's precision is 0.95 and recall is 0.60. The business cost of missing a fraud is very high. Which of the following should the team prioritize to reduce the number of false negatives?

Accepted Answer

Decrease the classification threshold.. Decreasing the classification threshold makes the model more sensitive, classifying more transactions as fraudulent. This increases recall (reducing false negatives) at the cost of precision. Given the high cost of missing fraud, lowering the threshold is the direct way to capture more true positives, even if it increases false positives.

Answer

Use a different model algorithm.

Answer

Add more features.

Answer

Increase the classification threshold.

Question 10

A data scientist is preparing a dataset for a classification model. The dataset contains a column "Age" with 10% missing values and a column "Income" with 30% missing values. Which imputation strategy is MOST appropriate to minimize bias?

Accepted Answer

Replace missing Age with the mean and missing Income with the median.. Option A is correct because using mean imputation for Age (10% missing) and median imputation for Income (30% missing) minimizes bias. Mean is suitable for roughly symmetric distributions with low missingness, while median is robust to outliers and skewness, which is common in income data. This combination reduces distortion of central tendency and preserves data integrity better than uniform methods.

Answer

Delete all rows with missing values.

Answer

Replace missing Age with the mode and missing Income with a constant value.

Answer

Replace missing values with zeros.

Question 11

A company streams sensor data from IoT devices. The data arrives as JSON messages at high velocity. Which data pipeline architecture is BEST suited to handle this streaming data for near-real-time analytics?

Accepted Answer

Stream processing using Apache Kafka and Spark Streaming.. Apache Kafka acts as a distributed, fault-tolerant ingestion layer that can handle high-velocity JSON messages, while Spark Streaming processes the data in micro-batches for near-real-time analytics. This combination provides the low-latency, scalable pipeline required for streaming IoT sensor data, unlike batch or single-node approaches.

Answer

Batch processing using Hadoop MapReduce every 24 hours.

Answer

Batch processing using nightly ETL jobs.

Answer

Single-node database with periodic inserts.

Question 12

An e-commerce company needs to update its recommendation model continuously as user preferences change. The model currently retrains from scratch every night, but the training time is too long. Which approach would reduce training time while keeping the model up-to-date?

Accepted Answer

Implement incremental learning using online gradient descent.. Incremental learning using online gradient descent updates the model parameters with each new data point or mini-batch, avoiding the need to retrain from scratch. This approach significantly reduces training time while continuously adapting to changing user preferences, making it ideal for real-time recommendation systems.

Answer

Use dimensionality reduction on features.

Answer

Switch to a simpler model.

Answer

Increase the batch size for retraining.

Question 13

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.

Answer

Feature scaling

Answer

Dimensionality reduction

Answer

Polynomial features

Question 14

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) is an appropriate feature selection technique because it iteratively removes the least important features based on a model's feature importance scores or coefficients, directly reducing the feature count from 100 to a smaller subset. This improves model performance by eliminating irrelevant or redundant features and reduces training time by decreasing dimensionality.

Answer

Normalization

Answer

One-Hot Encoding

Answer

Principal Component Analysis (PCA)

Question 15

Refer to the exhibit. What is the recall of the model?

Accepted Answer

0.8. Recall is calculated as True Positives divided by (True Positives + False Negatives). From the confusion matrix, True Positives = 72 and False Negatives = 18, so recall = 72 / (72 + 18) = 72 / 90 = 0.8. This measures the model's ability to correctly identify all actual positive cases.

Answer

0.72

Answer

0.7

Answer

0.73

AI0-001 Practice Test 34 — 15 Questions

Exhibit

AI0-001 Practice Test 34 — 15 Questions

Exhibit