AI0-001 AI Models and Data Engineering Practice Test 8 — 15 Questions

Question 1

A retail analytics team is preparing a dataset of product reviews for a sentiment classification model. The dataset contains 50,000 reviews, but only 2,000 are labeled as positive or negative. The team wants to use the unlabeled reviews to improve model performance. Which approach best leverages the unlabeled data?

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

Accepted Answer

Apply semi-supervised learning using a self-training algorithm that iteratively labels high-confidence unlabeled examples and retrains the model.. Semi-supervised learning, specifically self-training, is designed to exploit a small labeled set alongside a large unlabeled set. By iteratively adding high-confidence pseudo-labels from the unlabeled reviews, the model can learn from the broader data distribution, improving generalization. The other options either ignore the unlabeled data or use techniques that are not appropriate for text sentiment classification with limited labels.

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Apply principal component analysis (PCA) to reduce the dimensionality of the unlabeled reviews and then train a classifier on the reduced features.

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Perform data augmentation by generating synthetic reviews using a generative adversarial network (GAN) trained on the labeled data.

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Use transfer learning by fine-tuning a pre-trained language model on the 2,000 labeled reviews only, ignoring the unlabeled data.

Question 2

A data engineer is designing a pipeline to ingest high-velocity clickstream events from a web application into a data lake. The events must be queryable within minutes of arrival, and the schema evolves frequently as new fields are added. Which storage approach best meets these requirements?

Accepted Answer

Store raw JSON files in an object storage bucket partitioned by date, and query them directly using a serverless SQL engine.. Storing raw JSON in object storage and querying with a serverless SQL engine provides schema-on-read flexibility and near-real-time access. It accommodates evolving schemas without costly migrations and scales to high volumes. The other options impose rigid schemas, high latency, or lack analytical query capabilities, making them unsuitable for this scenario.

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Batch load events every 24 hours into a data warehouse using a rigid ETL process with predefined transformations.

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Load events into a relational database with a fixed schema, using ALTER TABLE to add columns as new fields appear.

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Write events to a distributed message queue and retain them for 7 days, querying the queue directly for analytics.

Question 3

A retail analytics team is preparing a dataset of product reviews for a sentiment classification model. The dataset contains 50,000 reviews, but only 2,000 are labeled as positive or negative. The team wants to use the unlabeled reviews to improve model performance. Which approach best leverages the unlabeled data?

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

Accepted Answer

Use target encoding for 'store_id', replacing each store with a statistic such as the mean of the target computed from training folds.. High-cardinality identifiers like store_id need a compact numeric representation, and out-of-fold target encoding provides one column that captures store-level signal without exploding dimensionality. Low-cardinality nominal features like product_category and day_of_week are best handled with one-hot encoding, which avoids implying an order and keeps the feature space small. Together these choices match encoding strategy to cardinality.

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Use one-hot encoding for 'store_id' so every store gets its own binary column.

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Use binary encoding for 'day_of_week' so each day is represented by a binary code across multiple columns.

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Use label encoding for 'product_category' so categories become arbitrary integers based on alphabetical order.

Question 4

A machine learning engineer is preparing a dataset for a natural language processing task. The dataset contains text reviews with varying lengths, and the engineer plans to use a transformer model. Which preprocessing step is most critical to ensure the model can handle the input effectively?

Accepted Answer

Tokenize the text into subword units and pad or truncate sequences to a fixed maximum length.. Transformer models require fixed-length input sequences, so tokenizing into subword units and padding/truncating to a uniform length is essential. This enables batch processing and handles out-of-vocabulary words. Other steps like lowercasing, one-hot encoding, or stemming are either not critical or incompatible with transformer input expectations.

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Convert all text to lowercase and remove punctuation to reduce vocabulary size.

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Perform stemming or lemmatization to reduce words to their base forms.

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Apply one-hot encoding to each word in the vocabulary to create binary vectors.

Question 5

A data engineer is preparing a dataset for a machine learning model that predicts customer lifetime value. The dataset contains a 'monthly_spend' column with a highly right-skewed distribution. The team wants to apply a transformation to reduce skewness while preserving the relative order of values. Which transformation is most appropriate?

Accepted Answer

Apply a logarithmic transformation (log(1 + x)) to the 'monthly_spend' column.. A logarithmic transformation is the most appropriate because it compresses large values and reduces right skewness while preserving the relative order of observations. Unlike standardization, it changes the distribution shape; unlike binning or rank transformation, it retains the continuous nature and magnitude relationships, making it well-suited for monetary data that is positively skewed.

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Standardize the 'monthly_spend' column by subtracting the mean and dividing by the standard deviation.

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Replace each value with its rank among all values in the column (rank transformation).

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Apply one-hot encoding to the 'monthly_spend' column by binning into deciles.

Question 6

A data scientist is building a model to predict equipment failure using sensor data. The dataset contains time-series readings from multiple sensors, and the goal is to detect anomalies that precede failures. Which TWO feature engineering techniques are most appropriate for this time-series data? (Choose two.)

Accepted Answer

Compute rolling window statistics such as mean, standard deviation, and min/max over recent time intervals.. Rolling window statistics and lag features are essential for time-series data because they encode temporal dependencies and trends. Rolling statistics summarize recent behavior, while lag features provide historical context. Together, they enable the model to detect anomalies that precede equipment failure. The other options either ignore time order or are not suitable for temporal feature extraction.

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Replace missing sensor values with the overall mean of the entire dataset.

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Apply one-hot encoding to the timestamp column to represent each time point as a binary vector.

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Perform principal component analysis (PCA) on the raw sensor readings to reduce dimensionality.

Question 7

A machine learning engineer is building a model to predict whether a customer will make a purchase within the next week. The dataset contains 10,000 samples with 20 features, and the target variable is binary. The engineer wants to use a model that provides interpretable results to explain predictions to business stakeholders. Which model is most appropriate?

Accepted Answer

Logistic regression. Logistic regression is a linear model that provides coefficients for each feature, making it highly interpretable. Business stakeholders can understand how each feature influences the predicted probability of purchase. While ensemble methods like random forest and gradient boosting may offer higher accuracy, they are less transparent. Support vector machines with non-linear kernels are also difficult to interpret. Therefore, logistic regression is the most appropriate choice when interpretability is a primary requirement.

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Gradient boosting machine

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Random forest

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Support vector machine with RBF kernel

Question 8

A data analyst is exploring a dataset and notices that one numerical feature has a highly skewed distribution with a long right tail. The analyst wants to apply a transformation to make the distribution more symmetric for a linear model. Which transformation is most appropriate?

Accepted Answer

Logarithmic transformation. A logarithmic transformation is the most appropriate for a highly right-skewed distribution because it compresses large values and can make the distribution more symmetric. Square root is milder and less effective for high skewness, while standardization and min-max normalization only rescale without altering distribution shape.

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Standardization (z-score normalization)

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Square root transformation

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Min-max normalization

Question 9

A machine learning team is deploying a model that predicts loan default probabilities. The model outputs a probability score, and the team wants to convert it into a binary decision (default/no default). The costs of false positives and false negatives are not equal; a false negative (predicting no default when the customer defaults) is five times more costly than a false positive. Which approach best optimizes the decision threshold?

Accepted Answer

Choose a threshold that minimizes the expected cost, weighting false negatives five times more than false positives.. The optimal decision threshold minimizes expected cost, which requires weighting false negatives according to their higher cost. Lowering the threshold increases the number of predicted defaults, reducing expensive false negatives at the expense of more false positives. This cost-sensitive approach aligns model decisions with business objectives, unlike accuracy maximization or arbitrary thresholds.

Answer

Set the threshold to 0.5 to balance false positives and false negatives equally.

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Use the threshold that maximizes overall accuracy on the validation set.

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Set the threshold to the prevalence of defaults in the training data.

Question 10

A data scientist is preparing a dataset for a natural language processing task. The dataset contains a 'review_text' column with free-form customer reviews. Before feeding the text into a machine learning model, the team wants to convert the text into numerical features. Which technique is most appropriate for this purpose?

Accepted Answer

Apply TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to the 'review_text' column.. TF-IDF is the most appropriate because it transforms text into numerical vectors that reflect term importance, capturing semantic content while reducing the influence of common words. It is widely used for text classification and clustering, and it works well with many machine learning algorithms without requiring deep learning architectures.

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Use one-hot encoding on each unique word in the 'review_text' column.

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Convert each review to its average word length and use that as a single numerical feature.

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Hash each review to a fixed-length integer using a cryptographic hash function.

Question 11

A machine learning engineer is preparing a dataset for a model that predicts whether a customer will click on an ad. The dataset contains a feature 'time_since_last_purchase' measured in hours, which has a highly skewed distribution with a long tail. The engineer decides to apply a logarithmic transformation to this feature. Which statement BEST describes the effect of this transformation?

Accepted Answer

It reduces the impact of extreme values and makes the distribution more symmetric, which can help linear models.. Applying a logarithmic transformation to a skewed feature like time since last purchase compresses the long tail and reduces skewness. This makes the feature more symmetric, which can improve the performance of models that assume normality or are sensitive to outliers, such as linear models. It does not normalize to a fixed range, bin the feature, or standardize it; those are separate preprocessing steps.

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It converts the feature into a categorical variable by binning values into logarithmic intervals.

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It normalizes the feature to a 0-1 range, ensuring all features contribute equally to distance calculations.

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It eliminates the need for feature scaling because the transformed values are already standardized.

Question 12

A data scientist is building a model to predict the likelihood of a customer defaulting on a loan. The dataset contains a feature 'debt_to_income_ratio' that is highly skewed with a long right tail. The scientist decides to apply a logarithmic transformation to this feature. Which statement best describes the effect of this transformation?

Accepted Answer

It reduces the impact of outliers and makes the distribution more symmetric, which can help linear models perform better.. Log transformation is commonly used to handle right-skewed data. It reduces skewness, stabilizes variance, and makes the distribution more symmetric, which can improve the performance of linear models and neural networks that are sensitive to feature scaling. It does not increase variance, convert to categorical, or normalize to a fixed range.

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It normalizes the feature to a range between 0 and 1, which is required for all machine learning algorithms.

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It converts the feature into a categorical variable, which is useful for tree-based models.

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It increases the variance of the feature, allowing the model to capture more complex patterns.

Question 13

A data engineer is designing a data pipeline for a machine learning model that predicts equipment failure in a manufacturing plant. The pipeline ingests sensor data every second, and the model must be retrained daily with the latest data. The team needs to ensure that the training data is representative of the current operating conditions and that the model does not become stale. Which strategy is most appropriate for maintaining the training dataset?

Accepted Answer

Implement a sliding window approach where the training dataset includes only the most recent 30 days of sensor data.. A sliding window of recent data is most appropriate because it keeps the training set aligned with current operating conditions, mitigating concept drift. This ensures the model learns from the most relevant examples, which is essential for predictive maintenance where equipment behavior evolves over time.

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Randomly sample 10% of all historical data for each daily retraining to reduce computational load.

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Use the entire historical dataset from the plant's inception to train the model, ensuring maximum data volume.

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Manually curate a fixed dataset of known failure events and use it for all retrainings.

Question 14

A data scientist is building a model to detect anomalies in server logs. The dataset contains millions of log entries, each with a timestamp and a message. The scientist wants to create features that capture the frequency of certain keywords (e.g., 'error', 'timeout') over time. Which approach is MOST appropriate for creating these features while avoiding data leakage?

Accepted Answer

Compute keyword frequencies using a sliding window that only includes past log entries relative to each timestamp.. To avoid data leakage when creating time-based features, the computation must only use data available up to each point in time. A sliding window that aggregates past log entries ensures causality, mimicking real-time conditions. This captures temporal dynamics like increasing error rates, which are critical for anomaly detection, without using future information that would not be available in production.

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Compute keyword frequencies over the entire dataset and use them as static features.

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Aggregate keyword frequencies per day and assign the same daily frequency to all entries within that day.

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Use a bag-of-words representation for each log entry individually, ignoring timestamps.

Question 15

A data scientist is preparing a dataset for training a machine learning model. The dataset contains a mix of numerical and categorical features, and some features have high cardinality. The data scientist needs to apply appropriate encoding techniques to transform categorical variables into a format suitable for the model. Which TWO encoding methods are most appropriate for high-cardinality categorical features? (Choose two.)

Accepted Answer

Target encoding. Target encoding and frequency encoding are both effective for high-cardinality categorical features. Target encoding replaces categories with the mean target value, capturing predictive relationships, while frequency encoding replaces categories with their counts, reducing dimensionality without imposing order. One-hot encoding creates too many columns, label encoding imposes false order, and binary encoding may not capture target relationships as well. Therefore, target and frequency encoding are the most appropriate methods.

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Label encoding

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Binary encoding

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One-hot encoding