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

Question 1

A dataset used for training a classification model contains 10% missing values in a feature that is known to be important. The data scientist decides to impute the missing values. Which imputation method is most robust if the data is not missing completely at random?

Accepted Answer

Use multiple imputation to model missing values. Option B is correct because multiple imputation accounts for uncertainty and is robust when data are missing at random. Option A is wrong because mean imputation distorts distributions and relationships. Option C is wrong because deleting rows with missing values reduces sample size and may bias results. Option D is wrong because filling with zero is arbitrary and introduces bias.

Answer

Delete all rows with missing values

Answer

Replace missing values with the mean of the feature

Answer

Fill missing values with 0

Question 2

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]. Option C is correct because SARIMA explicitly models seasonality and trend, making it ideal for this scenario. Option A is wrong because linear regression does not capture seasonality well without manual feature engineering. Option B is wrong because ARIMA can handle trend but not seasonality without differencing at seasonal lag; SARIMA is more appropriate. Option D is wrong because a simple moving average ignores trend and seasonality.

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 3

A deep learning model for image classification achieves 99% training accuracy but only 85% validation accuracy. The model has millions of parameters. Which technique is most likely to reduce overfitting while maintaining high accuracy?

Accepted Answer

Add dropout layers with a rate of 0.5 after each convolutional block. Option A is correct because dropout randomly deactivates neurons during training, acting as regularization and reducing overfitting. Option B is wrong because increasing epochs further will likely worsen overfitting. Option C is wrong because reducing batch size can increase training noise but is not a primary anti-overfitting technique. Option D is wrong because reducing learning rate may help convergence but does not directly combat overfitting caused by model capacity.

Answer

Reduce batch size from 32 to 8

Answer

Decrease the learning rate by a factor of 10

Answer

Increase the number of training epochs to 500

Question 4

A data engineer is splitting a dataset into training, validation, and test sets for a machine learning project. The dataset is large and representative of the population. Which split ratio is commonly recommended?

Accepted Answer

80% training, 10% validation, 10% test. Option B is correct because 80/10/10 is a typical split for large datasets, ensuring enough training data while having separate validation and test sets. Option A is wrong because 50/25/25 has too little training data. Option C is wrong because 90/5/5 gives too little validation data. Option D is wrong because 70/20/10 is also reasonable but 80/10/10 is more common for large datasets; however, the question asks for 'commonly recommended' and among the options, 80/10/10 is standard.

Answer

90% training, 5% validation, 5% test

Answer

70% training, 20% validation, 10% test

Answer

50% training, 25% validation, 25% test

Question 5

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. Option D is correct because feature scaling (normalization or standardization) is crucial for SVMs with RBF kernel, as the distance metric depends on feature scales. Option A is wrong because switching to linear kernel may not capture non-linearity. Option B is wrong because increasing C is regularization, not addressing scale. Option C is wrong because reducing gamma may help but without scaling, distances are dominated by large-scale features.

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 6

A natural language processing (NLP) team is building a sentiment analysis model. The raw text data contains punctuation, stop words, and URLs. Which TWO preprocessing steps are most appropriate to improve model performance? (Choose two.)

Accepted Answer

Remove all punctuation and URLs. Options A and B are correct. Removing stop words reduces noise, and removing punctuation and URLs helps standardize text. Option C (stemming) is useful but not always necessary; Option D (tokenization) is fundamental but the question asks for specific preprocessing to improve performance; Option E (converting to lowercase) is standard but not among the best two for this scenario; however, converting to lowercase is a basic step. Actually, typical preprocessing includes lowercasing, removing punctuation, removing stop words, and tokenization. The question asks for 'most appropriate'. Lowercasing is common but technically could be considered basic. I'll choose A and B as the two best. But to be safe, let's adjust: Many might argue lowercasing is essential. I'll make correct options A and D? Let's think: The stem says 'improve model performance'. Stop word removal and punctuation removal are often performed. Lowercasing is also standard. But I need exactly two. I'll go with A (remove stop words) and B (remove punctuation and URLs) as they directly reduce noise. Lowercasing is also important but often done anyway. I'll keep A and B.

Answer

Apply stemming to reduce words to root forms

Answer

Convert all text to lowercase

Answer

Tokenize the text into individual words

Question 7

A data scientist is evaluating a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). Which TWO metrics are most appropriate for assessing model performance? (Choose two.)

Accepted Answer

Precision. Precision is appropriate because it measures the proportion of predicted fraud cases that are actually fraudulent, which is critical when false positives (flagging legitimate transactions as fraud) are costly. In a highly imbalanced dataset like this (99% non-fraud), precision directly evaluates the model's ability to avoid overwhelming fraud analysts with false alarms.

Answer

F1 score

Answer

Area under the ROC curve (AUC-ROC)

Answer

Accuracy

Question 8

A computer vision team is building an image classifier for rare wildlife species. The dataset has only 500 images per class, and the model overfits. Which THREE data augmentation techniques are most likely to reduce overfitting? (Choose three.)

Accepted Answer

Horizontal flip. Horizontal flip is a simple and effective data augmentation technique that doubles the training data by mirroring images, which helps the model generalize better to variations in orientation. This is particularly useful for wildlife images where the animal may appear facing left or right, reducing overfitting by exposing the model to more diverse examples without collecting new data.

Answer

Adding Gaussian noise

Answer

Random cropping

Question 9

A healthcare startup is deploying a machine learning model to predict patient readmission within 30 days using electronic health records (EHR). The data pipeline uses Apache Spark for preprocessing and training on an Amazon EMR cluster. The training dataset is 50 GB and composed of structured numeric and categorical features, along with unstructured clinical notes. The data scientist observes that training takes over 12 hours and frequently fails due to out-of-memory (OOM) errors, especially when processing the clinical notes via TF-IDF vectorization. The cluster has 10 nodes with 64 GB RAM each. The data engineer has already tried increasing spark.sql.shuffle.partitions to 400 and using Kryo serialization, but OOM persists. Which action should the data engineer take next to resolve the OOM errors?

Accepted Answer

Repartition the clinical notes data into 2000 partitions before TF-IDF. Option B is correct because repartitioning the clinical notes data into 2000 partitions before TF-IDF vectorization increases parallelism and reduces the memory pressure per partition. The default partition count (often based on spark.default.parallelism) is too low for 50 GB of data, causing individual partitions to exceed executor memory limits. By increasing partitions, each executor processes smaller chunks, preventing OOM errors during the memory-intensive TF-IDF stage.

Answer

Broadcast the TF-IDF model to all executors to avoid shuffling

Answer

Add 10 more nodes to the cluster to increase total memory

Answer

Use a single executor with 64 GB and increase driver memory to 128 GB

Question 10

A financial services company has a real-time fraud detection system that uses Apache Kafka to stream transaction events, a TensorFlow Serving model for scoring, and a Redis cache for lookup of historical fraud patterns. The system processes 10,000 transactions per second with an SLA of 100ms latency per transaction. Recently, after a model update, the latency for some transactions spiked to over 500ms, causing timeouts. The model uses a deep neural network with 10 million parameters. The engineering team suspects the issue is due to increased model inference time. Which action should be taken to reduce latency without significant loss in accuracy?

Accepted Answer

Quantize the model weights from FP32 to FP16. The latency spike is caused by increased model inference time after a model update. Quantizing model weights from FP32 to FP16 reduces memory bandwidth and computation requirements, directly speeding up inference on compatible hardware (e.g., GPUs with Tensor Cores) with minimal accuracy loss. This addresses the root cause—model inference latency—without changing the system architecture.

Answer

Add more Redis nodes to the cache cluster

Answer

Increase the number of Kafka partitions and consumer threads

Answer

Decrease the inference batch size from 32 to 1

Question 11

A medical imaging team is developing an AI model to detect tumors from CT scans. They have 10,000 labeled scans, but the labels were created by a semi-automated process with an estimated 20% error rate (mislabeled tumor vs. no tumor). The team trains a convolutional neural network (CNN) and achieves 90% accuracy on a held-out test set that was carefully validated by an expert radiologist. However, when deployed to a new hospital's patient population, the accuracy drops to 70%. The team suspects domain shift and label noise. Which strategy is most likely to improve model robustness for the new hospital?

Accepted Answer

Use active learning to select the most uncertain predictions from the new hospital's data, then have an expert radiologist correct those labels. Option C is correct. Active learning helps select the most informative samples (e.g., uncertain predictions) for expert review, efficiently improving the model with limited expert effort. Option A is wrong because simply adding more noisy labels will amplify errors. Option B is wrong because random sampling may not capture the most valuable corrections. Option D is wrong because reducing model complexity may underfit, and dropouts fine-tuning might not address label noise or domain shift.

Answer

Randomly select 1,000 scans from the new hospital and have them re-labeled by the radiologist

Answer

Collect 20,000 more scans with the same semi-automated labeling process

Answer

Reduce the CNN's number of layers and apply dropout to combat overfitting

Question 12

An e-commerce company deploys a model to recommend products to users. The recommendation system uses collaborative filtering based on user-item interaction history. After deployment, the model shows decreasing click-through rates (CTR) over time. The data engineer notices that the model was trained on data from the past six months and is retrained daily. However, the trend suggests that user preferences are shifting more rapidly than expected. The engineer suspects that the model is suffering from distribution drift. Which approach should the engineer implement to adapt the model more quickly to changing user behavior?

Accepted Answer

Switch to an online learning algorithm that updates the model after each user click. Option A is correct. Online learning allows the model to update incrementally with each new interaction, adapting quickly to changes. Option B is wrong because batch retraining weekly is slower than daily. Option C is wrong because using only last week's data may not provide enough data and could be noisy. Option D is wrong because increasing model complexity may cause overfitting and is not a direct solution to drift.

Answer

Increase the retraining period to once per week to reduce computational cost

Answer

Increase the model complexity by adding more features and layers

Answer

Use only the last week of data for training to focus on recent trends

Question 13

A logistics company uses a machine learning model to predict delivery times based on historical data including distance, traffic, weather, and driver performance. The model is deployed as a REST API using Flask and run on a single server. Recently, the model has been returning predictions with high latency (over 2 seconds) during peak hours when the API receives 500 requests per second. The server has 8 CPU cores and 32 GB RAM. The model is a gradient boosting model (XGBoost) with 500 trees. The engineer wants to reduce inference latency to under 500ms without retraining the model. Which action is most effective?

Accepted Answer

Prune the model by reducing the number of trees to 100 and limit tree depth. Option B is correct. Model pruning reduces the number of trees in the ensemble, directly lowering inference time. Option A is wrong because adding more servers (horizontal scaling) addresses throughput but not per-request latency; it may help if the bottleneck is CPU, but pruning is more efficient. Option C is wrong because using a simpler model (linear regression) would require retraining and likely lose accuracy. Option D is wrong because increasing server memory does not speed up CPU-bound tree inference.

Answer

Replace XGBoost with a linear regression model

Answer

Scale horizontally by deploying additional servers behind a load balancer

Answer

Increase server RAM to 128 GB

Question 14

A data scientist is preparing a dataset for a classification model. The dataset contains several categorical variables with high cardinality. Which TWO encoding methods are appropriate for converting these categorical variables into numerical features?

Accepted Answer

One-hot encoding. One-hot encoding and label encoding are both appropriate techniques for encoding categorical variables. One-hot encoding creates binary columns for each category, while label encoding assigns a unique integer to each category. PCA is a dimensionality reduction technique, K-means is a clustering algorithm, and min-max scaling is a normalization method, none of which are encoding methods.

Answer

Min-max scaling

Answer

K-means clustering

Answer

Principal component analysis (PCA)

Question 15

A healthcare company is developing a predictive model to identify patients at risk of readmission within 30 days. The data engineering team has built a pipeline that collects data from multiple sources, including electronic health records (EHR), lab results, and wearable device data. During initial testing, the model's performance is poor, with high false positives. Upon investigation, the team discovers that the data contains significant temporal misalignment: lab results are timestamped when ordered, not when collected; wearable data is aggregated hourly; and EHR data has inconsistent update frequencies. The data pipeline currently joins all features on the patient ID without aligning timestamps. The data volume is large, and processing time is a concern. Which action should the data engineering team take to most effectively address the issue and improve model performance?

Accepted Answer

Implement a window-based feature aggregation (e.g., 6-hour windows) and align all features to the same time windows before joining.. Implementing a window-based feature aggregation with consistent time windows (e.g., 6-hour or 12-hour) and aligning all data to those windows before joining ensures temporal consistency and reduces noise. This approach addresses the root cause of misalignment while managing data volume through aggregation. Simply discarding data or padding with zeros loses valuable information. Using an interpolation algorithm may introduce unrealistic values for irregularly sampled data. Leaving the pipeline as-is and tuning the model does not fix the data quality issue.

Answer

Discard all records where timestamps do not match exactly across sources, and only use records with perfect alignment.

Answer

Leave the pipeline unchanged and instead adjust the model's classification threshold to reduce false positives.

Answer

Use a data imputation algorithm to fill in missing timestamps and then join on the nearest timestamp.