AI0-001 AI Models and Data Engineering Practice Test 1 — 15 Questions

Question 1

A team is training a deep learning model for image classification. The training loss decreases rapidly but validation loss starts increasing after a few epochs. Which regularization technique should be applied to mitigate this issue?

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

Accepted Answer

Early stopping. Early stopping halts training when validation loss starts increasing, preventing overfitting. Option A (data augmentation) is wrong because it increases data diversity but does not stop training when validation loss increases. Option B (L2 regularization) is wrong because it penalizes large weights but does not directly address the issue of validation loss increasing. Option D (dropout) is wrong because while it helps generalize by randomly dropping neurons, it does not stop training when overfitting occurs.

Answer

Data augmentation

Answer

L2 regularization

Answer

Dropout

Question 2

A data scientist is working with a dataset that has 10,000 features but only 500 samples. The goal is to train a model for binary classification. Which feature selection technique is MOST appropriate to reduce overfitting?

Accepted Answer

Recursive Feature Elimination (RFE) with cross-validation.. Recursive Feature Elimination (RFE) with cross-validation iteratively removes features and evaluates model performance, making it robust for datasets with many features but few samples, thus reducing overfitting. Option A (Univariate selection using chi-squared test) is univariate and can miss feature interactions. Option C (Using all features with L2 regularization) does not reduce the number of features, and L2 regularization alone may be insufficient to prevent overfitting when p >> n. Option D (Principal Component Analysis) creates components that are linear combinations, which may still lead to overfitting and reduces interpretability.

Answer

Univariate selection using chi-squared test.

Answer

Using all features with L2 regularization.

Answer

Principal Component Analysis (PCA) without feature selection.

Question 3

A streaming data pipeline ingests sensor data from IoT devices. The data arrives at irregular intervals and contains occasional spikes. Which data transformation is most appropriate for preparing this data for a time-series model?

Accepted Answer

Downsampling to a fixed frequency using mean aggregation. Downsampling to a fixed frequency using mean aggregation is the most appropriate transformation because time-series models require uniformly spaced timestamps to learn temporal dependencies. By aggregating irregularly arriving sensor data into fixed intervals (e.g., every 5 minutes) using the mean, you smooth out occasional spikes and create a consistent input structure that models like ARIMA or LSTM can process without bias from irregular gaps.

Answer

Removing all rows with values outside 3 standard deviations

Answer

Using a sliding window to compute moving averages

Answer

Padding missing timestamps with zeros

Question 4

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.

Answer

Change the activation to tanh

Answer

Add dropout after each layer

Answer

Increase the number of units in the first dense layer

Question 5

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.

Answer

Remove rows with null income

Answer

Replace nulls with 0

Answer

Replace nulls with the mean income of the entire dataset

Question 6

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().

Answer

Data encryption

Answer

Data transformation

Answer

Data validation

Question 7

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 data ingestion pipeline is essential because it handles the extraction, transformation, and loading (ETL) of raw data into the feature store. This pipeline ensures that features are computed, validated, and stored in a consistent format for both training and serving, which is critical for maintaining data freshness and reliability in ML workflows.

Answer

Experiment tracking

Answer

Model registry

Question 8

A data engineer is preprocessing text data for sentiment analysis. Which technique preserves word order while converting text to numeric features?

Accepted Answer

N-grams with frequency counts. (N-grams with frequency counts) is correct because n-grams capture sequences of words, preserving local order. TF-IDF and Bag-of-words ignore word order entirely, treating text as an unordered set of tokens. Word2Vec produces dense embeddings that encode semantic similarity based on context, but it does not explicitly preserve exact word order; it learns from surrounding words but focuses on meaning rather than sequence.

Answer

TF-IDF

Answer

Word2Vec

Answer

Bag-of-words

Question 9

A data pipeline ingests streaming data from IoT sensors. The current batch processing pipeline causes stale predictions. Which architecture change is most appropriate?

Accepted Answer

Implement Apache Kafka and stream processing. Implementing Apache Kafka and stream processing enables real-time data ingestion and processing, reducing latency and stale predictions. Option A (larger batch interval) increases staleness. Option B (micro-batch with Spark) still uses batch processing with small intervals, not true streaming. Option C (HDFS) is for storage, not real-time processing.

Answer

Use a larger batch interval

Answer

Revert to micro-batch processing with Apache Spark

Answer

Store raw data in Hadoop HDFS

Question 10

Which TWO data preprocessing techniques reduce the dimensionality of a dataset?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original features into a new set of uncorrelated variables (principal components) ordered by the variance they capture. By selecting only the top components, PCA reduces the number of features while retaining most of the dataset's information, effectively lowering the dimensionality.

Answer

One-hot encoding

Answer

Imputation

Answer

Feature scaling

Question 11

A company wants to forecast monthly sales for the next year using historical sales data over three years. The data shows strong seasonality and a slight upward trend. Which model type is best suited for this task?

Accepted Answer

SARIMA model with seasonal order (1,1,1)[12]. The SARIMA (Seasonal ARIMA) model with seasonal order (1,1,1)[12] is best suited because it explicitly captures both the strong seasonality (period 12 for monthly data) and the slight upward trend through its non-seasonal differencing and seasonal components. This model extends ARIMA by adding seasonal terms, making it ideal for time series with clear seasonal patterns and trends.

Answer

Simple moving average of the last 12 months

Answer

ARIMA model without seasonal terms

Answer

Linear regression with time as the independent variable

Question 12

A machine learning engineer is training a Support Vector Machine (SVM) with an RBF kernel on a dataset with features on different scales (e.g., age 0-100, income 0-1,000,000). The model converges slowly and yields poor accuracy. What should the engineer do first?

Accepted Answer

Standardize the features to have zero mean and unit variance. Standardizing features to zero mean and unit variance is the correct first step because SVMs with RBF kernels are distance-based models. Features on vastly different scales (e.g., age 0-100 vs. income 0-1,000,000) cause the kernel to disproportionately weight larger-scale features, leading to slow convergence and poor accuracy. Standardization ensures each feature contributes equally to the distance calculations, improving both training speed and model performance.

Answer

Increase the regularization parameter C to penalize misclassifications more

Answer

Decrease the gamma parameter to reduce the influence of each data point

Answer

Switch to a linear kernel to avoid distance calculations

Question 13

A retail company is building a recommendation system to suggest products to customers based on their purchase history. The data engineering team has collected data from point-of-sale systems, online browsing logs, and customer reviews. After cleaning the data, they notice that the feature set has over 500 dimensions, leading to high computational costs and potential overfitting. They need to reduce dimensionality while preserving as much variance as possible for the model. The team is considering various techniques. Which approach should they take to achieve this goal most effectively?

Accepted Answer

Use Principal Component Analysis (PCA) to reduce the feature space to the top 50 principal components that explain 95% of the variance.. Principal Component Analysis (PCA) is a linear dimensionality reduction technique that transforms the original high-dimensional feature space into a set of orthogonal principal components, ordered by the amount of variance they capture. By selecting the top 50 components that explain 95% of the variance, the team effectively reduces the feature set from over 500 dimensions while preserving the most informative structure in the data, directly addressing the goals of lowering computational cost and mitigating overfitting.

Answer

Keep all features but apply L1 regularization (Lasso) in the model to automatically reduce coefficients to zero.

Answer

Apply t-Distributed Stochastic Neighbor Embedding (t-SNE) to reduce the feature space to 50 dimensions.

Answer

Select only features that have a high correlation with the target variable, discarding all others.

Question 14

A data engineer is preparing a dataset for training a classification model. The dataset contains missing values in multiple features, inconsistent categorical labels, and outliers in numerical features. Which TWO preprocessing steps should the engineer prioritize to improve model performance?

Accepted Answer

Normalize numerical features using min-max scaling.. Min-max scaling normalizes numerical features to a fixed range (typically 0 to 1), which is essential for many classification algorithms (e.g., neural networks, SVM, k-NN) that are sensitive to feature magnitudes. Without scaling, features with larger numeric ranges can dominate the loss function and bias the model. Option D is correct because imputing missing values with the median is robust to outliers and preserves the central tendency of the data, which is critical when outliers are present in numerical features.

Answer

Remove all rows with any missing data.

Answer

Encode categorical variables using label encoding.

Answer

Apply one-hot encoding to all categorical variables.

Question 15

A data scientist is preparing a dataset for training a classification model. The dataset contains 10,000 records with a binary target variable where 9,500 belong to class A and 500 belong to class B. Which technique should the scientist use to address the class imbalance?

Accepted Answer

SMOTE (Synthetic Minority Oversampling Technique). SMOTE is the correct technique because it generates synthetic samples for the minority class (class B) by interpolating between existing minority instances, effectively balancing the dataset without losing information. This approach avoids the overfitting risk of simple oversampling and the information loss of undersampling, making it ideal for a 19:1 imbalance ratio.

Answer

Random undersampling of class A

Answer

Adding Gaussian noise to class B

Answer

Principal Component Analysis (PCA)