AI0-001 AI Models and Data Engineering Practice Test 4 — 15 Questions

Question 1

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 = 80 and False Negatives = 20, so recall = 80 / (80 + 20) = 80 / 100 = 0.8. This measures the model's ability to correctly identify all actual positive cases.

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0.72

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0.7

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0.73

Question 2

A data scientist is preparing a dataset for training a classification model. The dataset has a column with missing values in 5% of rows. Which action should the data engineer take to minimize bias?

Accepted Answer

Impute missing values with the median of the column. Imputing with the median preserves the distribution without significantly reducing sample size, minimizing bias. Removing rows reduces sample size, constant 999 introduces artificial outlier, and native handling may not be available.

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Remove all rows with missing values

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Replace missing values with a constant such as 999

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Use a model that can handle missing values natively

Question 3

A financial institution is training a risk assessment model. The dataset includes customer credit scores, income, age, and past loan defaults. During feature engineering, a data engineer creates a new feature 'income_to_debt_ratio'. Which type of feature engineering technique is this?

Accepted Answer

Feature combination. 'income_to_debt_ratio' is created by combining two existing features (income and debt) into a single derived feature. This is a classic example of feature combination (also known as feature crossing or feature construction), where arithmetic operations or logical rules are applied to existing variables to generate new predictive signals. The goal is to capture interactions or relationships that the original features alone may not express linearly.

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Feature encoding

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Feature scaling

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Feature selection

Question 4

A machine learning team is developing a model to predict server failure from telemetry data. They use a deep neural network with 3 hidden layers. After training, the model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the team apply to reduce the generalization error?

Accepted Answer

Apply L2 regularization. The model exhibits high variance (overfitting) because it achieves 99% accuracy on training data but only 85% on validation data. L2 regularization (also known as weight decay) adds a penalty proportional to the squared magnitude of the weights to the loss function, which discourages the network from fitting noise in the training data and improves generalization. This directly reduces the gap between training and validation performance.

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Increase the number of hidden layers

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Increase the learning rate

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Add more training data

Question 5

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 6

A team is training a language model using a large text corpus. They want to ensure the model does not learn biased associations between gender and professions. Which data engineering technique should they apply?

Accepted Answer

Balance the representation of professions across genders. Balancing the representation of professions across genders in the training data directly addresses the root cause of biased associations. By ensuring that each profession appears with roughly equal frequency for all gender references, the model learns statistical correlations that are fair rather than skewed by imbalanced data. This is a fundamental data engineering technique for bias mitigation, as it prevents the model from encoding spurious correlations between gender and occupation.

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Remove all gender-related words from the text

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Use a pre-trained model that is already debiased

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Apply adversarial debiasing during training

Question 7

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 8

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 9

A deep learning model for image classification is overfitting due to a small dataset. The team decides to apply data augmentation. Which augmentation technique is least likely to preserve the label?

Accepted Answer

Random cropping and rescaling. Random cropping and rescaling is least likely to preserve the label because it can cut out the primary object or distort its proportions, potentially removing the discriminative features needed for correct classification. For example, cropping a dog image to show only the background or a leg could change the semantic meaning, making the label 'dog' incorrect. In contrast, other techniques like noise, rotation, or flipping typically retain the core subject and its label.

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Adding random noise

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Random rotation

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Horizontal flip

Question 10

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.

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Normalization

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One-Hot Encoding

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Principal Component Analysis (PCA)

Question 11

A data science team is building a model to predict customer churn. The dataset includes categorical variables like 'region' and 'subscription_type'. Which three preprocessing steps should be applied to these categorical features? (Select THREE).

Accepted Answer

Label encoding. Label encoding (B) is correct because it converts each unique category in a categorical variable into a unique integer, which is a simple and memory-efficient way to prepare categorical data for machine learning models. Ordinal encoding (D) is correct for categorical variables with a natural order, such as 'subscription_type' if tiers exist (e.g., basic, premium, enterprise), preserving ordinal relationships. One-hot encoding (E) is correct for nominal categorical variables like 'region' where no order exists, creating binary columns for each category to avoid implying false ordinality.

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Normalization

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Standard scaling

Question 12

A data engineer is designing a data pipeline for a real-time recommendation system. The pipeline must handle high velocity streams and ensure data quality. Which three components should be included in the pipeline? (Select THREE).

Accepted Answer

A stream processing engine like Apache Kafka Streams. Apache Kafka Streams is a correct choice because it is a stream processing library specifically designed for building real-time applications and microservices that process data in motion. For a high-velocity recommendation pipeline, it provides exactly-once semantics, stateful processing (e.g., windowed joins, aggregations), and seamless integration with Kafka topics, enabling low-latency transformations without requiring an external cluster.

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A data warehouse for historical analysis

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A batch processing framework like Apache Spark

Question 13

Refer to the exhibit. A data engineer notices that the batch processing step is taking too long and causing delays. Which change would most likely reduce the latency?

Accepted Answer

Move feature engineering to the stream processing step in Flink. Moving feature engineering from the batch Spark job to the stream processing Flink job reduces the workload on the batch step, making it faster. Replacing Flink, increasing parallelism, or changing output format do not address the bottleneck as effectively.

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Increase the parallelism of the Spark job

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Replace Apache Flink with Apache Storm for stream processing

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Change the output format from Parquet to CSV

Question 14

A data scientist is preparing a dataset for a supervised learning model. The dataset contains missing values in 15% of the rows for a numeric feature. Which preprocessing technique should be applied to minimize bias?

Accepted Answer

Impute missing values with the mean of the feature.. Imputing missing values with the mean of the feature is the correct approach because it preserves the overall distribution of the numeric feature and avoids introducing systematic bias when only 15% of rows are affected. Removing rows would reduce sample size and potentially introduce selection bias, while encoding missing values as a separate category is inappropriate for numeric features in regression or classification tasks. Mean imputation is a standard, low-bias technique for MCAR (Missing Completely At Random) data in supervised learning pipelines.

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Remove all rows with missing values.

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Encode the missing values as a separate category.

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Use a model that handles missing values natively.

Question 15

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