AI0-001 Practice Test 15 — 15 Questions

Question 1

A natural language processing team wants to build a sentiment analysis model for customer reviews. They have 10,000 labeled reviews and 1 million unlabeled reviews. Which approach would MOST effectively leverage the unlabeled data?

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

Accepted Answer

Implement a semi-supervised learning algorithm that propagates labels from the labeled to the unlabeled data. Semi-supervised learning uses the small labeled set to guide learning from the large unlabeled set. Self-supervised learning would require a pretext task; fine-tuning a pre-trained model is also valid but semi-supervised directly addresses the labeled-unlabeled mix.

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Use self-supervised learning to pretrain on the unlabeled data, then fine-tune on the labeled data

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Train a supervised classifier on only the 10,000 labeled reviews

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Use reinforcement learning with the unlabeled data as rewards

Question 2

A data engineering team needs to orchestrate a complex ML pipeline that involves data extraction, transformation, model training, and deployment. They require scheduling, monitoring, and retry logic. Which MLOps tool is BEST suited for this task?

Accepted Answer

Apache Airflow. Apache Airflow is a workflow orchestration tool that supports complex DAGs, scheduling, monitoring, and retries, making it ideal for ML pipelines.

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Weights & Biases

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Kubeflow

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MLflow

Question 3

A team is fine-tuning a BERT model for a document classification task. They notice the model achieves high F1 scores on the training set but low F1 on the validation set. Which regularization technique would be MOST effective?

Accepted Answer

Dropout. Dropout randomly drops neurons during training, preventing co-adaptation and overfitting. L1 and L2 add penalties to weights but are less common for transformers; L1 induces sparsity, L2 reduces weight magnitude. However, dropout is the standard regularization in BERT-like models.

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L1 regularization

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L2 regularization

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Reduce batch size

Question 4

A data scientist is using PyTorch to train a custom NLP model. The training is slow on a single GPU. They want to speed up training by using multiple GPUs on a single machine. Which PyTorch feature should they use?

Accepted Answer

torch.nn.DataParallel. DataParallel (or DistributedDataParallel) is PyTorch's built-in feature to split batches across multiple GPUs. It is straightforward for single-machine multi-GPU training.

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TorchScript tracing

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torch.optim.SGD

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PyTorch Lightning's zero_grad function

Question 5

A data scientist is evaluating a binary classification model. The model achieves 95% accuracy on the test set, but the precision is 0.60 and recall is 0.55. The dataset has 90% negative class samples. Which metric should the team focus on to improve the model?

Accepted Answer

F1 score. With high class imbalance (90% negatives), accuracy is misleading. F1 score balances precision and recall, giving a better picture of performance on the minority class. AUC-ROC is also good but F1 directly optimizes for positive class.

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Perplexity

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BLEU score

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Accuracy

Question 6

A security team needs to ensure that all data used for AI model training in the cloud is encrypted at rest and in transit. Which set of measures meets this requirement on AWS?

Accepted Answer

Enable S3 default encryption with SSE-S3 and use HTTPS for API calls. AWS provides KMS for at-rest encryption and TLS for in-transit encryption. These are standard practices to secure data across the AI pipeline.

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Use Security Groups and Network ACLs

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Use client-side encryption and store keys in AWS Secrets Manager

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Enable VPC peering and use VPN connections

Question 7

A data scientist is building a model to predict whether a loan application will default. The dataset has 10,000 labeled examples with 1,000 defaults. Which metric is MOST appropriate for evaluating this highly imbalanced binary classification?

Accepted Answer

AUC-ROC. AUC-ROC is robust to class imbalance because it measures the trade-off between true positive rate and false positive rate across all thresholds. Accuracy is misleading when classes are imbalanced. Precision and recall focus on one class but are threshold-dependent.

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Precision

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Recall

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Accuracy

Question 8

A company is deploying a computer vision model to smartphones for offline object detection. The model was trained in PyTorch. Which format should they use for deployment on iOS devices?

Accepted Answer

Core ML. Core ML is Apple's framework for on-device machine learning on iOS, and PyTorch models can be converted to Core ML format.

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TorchScript

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ONNX

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TensorFlow Lite

Question 9

A research team is training a deep learning model for image classification using a small dataset of 1,000 labeled images. They are concerned about overfitting. Which combination of regularisation techniques would be MOST effective?

Accepted Answer

Dropout with a rate of 0.5 and L2 regularisation. Dropout randomly disables neurons during training to prevent co-adaptation, and L2 regularisation penalises large weights. Both are standard regularisation techniques. L1 promotes sparsity but is less common for dense layers. Batch normalisation helps convergence but is not primarily a regularisation method.

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Use early stopping without any other regularisation

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L1 regularisation and batch normalisation

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Increase learning rate and use momentum

Question 10

A team is using a pre-trained BERT model for a sentiment analysis task on product reviews. They want to adapt it to their specific domain with limited labeled data. Which approach is MOST effective?

Accepted Answer

Fine-tune the pre-trained BERT model on the small labeled dataset. Fine-tuning the pre-trained model on the small labeled dataset is standard for transfer learning with BERT. Training from scratch would require massive data; feature extraction with a linear classifier is possible but less effective with limited data; data augmentation alone does not adapt the model.

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Use BERT as a feature extractor and train a logistic regression on top

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Apply data augmentation to increase the dataset and then train from scratch

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Train a new BERT model from scratch on the domain data

Question 11

A data scientist is building a recommendation system for an e-commerce platform. The dataset includes user purchase history, product descriptions, and user demographics. The goal is to recommend products that a user is likely to purchase. Which TWO techniques are most appropriate for this task? (Select TWO.)

Accepted Answer

Content-based filtering. Collaborative filtering uses user-item interactions; content-based filtering uses item features. Association rule mining is for basket analysis; regression is not typically used for recommendations; anomaly detection is for outliers.

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Association rule mining

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Linear regression

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Anomaly detection

Question 12

A team uses Kubeflow to manage ML workflows on Kubernetes. They want to automate hyperparameter tuning for a training job. Which Kubeflow component should they use?

Accepted Answer

Kubeflow Katib. Katib is the hyperparameter tuning component in Kubeflow. Pipelines orchestrate workflows; KFServing is for inference; Notebooks are for development.

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KFServing

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Kubeflow Notebooks

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Kubeflow Pipelines

Question 13

A team is training a generative adversarial network (GAN) to generate realistic images of furniture. The generator loss decreases sharply while the discriminator loss increases. What is the MOST likely issue and recommended action?

Accepted Answer

The generator is too strong; train the discriminator more frequently. If the generator loss drops too fast and discriminator loss rises, the generator is overpowering the discriminator. The typical remedy is to train the discriminator more often (e.g., 5 steps per generator step) or adjust the learning rates.

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Mode collapse has occurred; increase the generator's learning rate

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The discriminator is overfitting; decrease its capacity

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The learning rates are too high; reduce both

Question 14

A company uses Azure OpenAI to generate marketing copy. They need to manage costs and ensure consistent response quality. Which TWO actions should they take?

Accepted Answer

Use prompt caching to avoid reprocessing identical inputs. Implementing rate limits prevents exceeding token budgets; prompt caching reduces repeated API calls. Fine-tuning is expensive; increasing max tokens may increase costs; using a less capable model may harm quality.

Answer

Fine-tune the model on previous marketing copy

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Switch to a cheaper, less capable model

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Increase max tokens per response to ensure completeness

Question 15

A data scientist needs to select a regression model to predict house prices. The dataset contains many features, some of which are irrelevant. Which TWO algorithms are BEST suited for this scenario, and why? (Select TWO)

Accepted Answer

Lasso regression (L1 regularization). Random Forest handles irrelevant features well via feature importance. Lasso (L1) regression performs automatic feature selection by shrinking coefficients to zero.

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Ridge regression (L2 regularization)

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Linear regression

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K-Nearest Neighbors