AI0-001 Practice Test 30 — 15 Questions

Question 1

An ML team monitors a production model using a dashboard that shows daily performance metrics. Over the past month, the model's accuracy has dropped from 92% to 87%, while the data distribution of input features has remained stable according to statistical tests. Which type of model drift is most likely occurring?

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

Accepted Answer

Concept drift. Concept drift occurs when the relationship between input features and the target variable changes, even if the input data distribution remains stable. In this scenario, the model's accuracy declines from 92% to 87% while input feature distributions are unchanged, indicating that the underlying mapping from features to labels has shifted—a classic sign of concept drift.

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Data drift (covariate shift)

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Model decay

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Overfitting

Question 2

A machine learning engineer is building a spam filter. The dataset contains 10,000 emails, of which 1,000 are spam. The engineer decides to use a Random Forest classifier. Which preprocessing step is most critical to ensure the model generalizes well to new, unseen emails?

Accepted Answer

Split the data into training and testing sets before any other preprocessing. Splitting the data into training and testing sets before any other preprocessing prevents data leakage. If preprocessing like normalization or PCA is applied to the entire dataset first, the test set information influences the training process, leading to overly optimistic performance estimates and poor generalization to new, unseen emails.

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Apply Principal Component Analysis (PCA) to reduce dimensionality

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Normalize the numerical features to have zero mean and unit variance

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Encode all features using one-hot encoding

Question 3

Refer to the exhibit. An AI specialist reviews the model evaluation report for a binary classifier. The specialist wants to improve recall. Which action is most likely effective?

Accepted Answer

Collect more training data for the minority class. Collecting more training data for the minority class directly addresses class imbalance, which is a common cause of low recall. By providing more examples of the positive (minority) class, the model can learn better decision boundaries and reduce false negatives, thereby improving recall without altering the classification threshold.

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Decrease the classification threshold

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Increase the classification threshold

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Add more features

Question 4

A model serving endpoint is tested using curl commands. Based on the exhibit, what is the most likely issue?

Accepted Answer

The model is experiencing intermittent high latency leading to timeouts. The exhibit shows that the first curl request succeeds (HTTP 200), but subsequent requests fail with 'curl: (28) Operation timed out' after the default timeout of 30 seconds. This pattern of intermittent success followed by timeouts is characteristic of a model experiencing high latency spikes, not a persistent server error or configuration issue. The server is reachable and the model responds correctly some of the time, ruling out deployment or malformed input issues.

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The server is returning HTTP 500 errors

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The input features are malformed

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The model is not deployed on the server

Question 5

A startup is building a recommendation system that requires low-latency similarity search over millions of product embeddings. They need a vector database that offers high performance and has a managed cloud option. Which TWO databases are best suited for this requirement?

Accepted Answer

Weaviate. Weaviate and Pinecone are both purpose-built vector databases that natively support high-performance approximate nearest neighbor (ANN) search using algorithms like HNSW (Weaviate) or proprietary indexing (Pinecone). They offer managed cloud services with automatic scaling, making them ideal for low-latency similarity search over millions of product embeddings without requiring manual infrastructure management.

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Chroma

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pgvector (PostgreSQL extension)

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Amazon DynamoDB

Question 6

An AI engineer is fine-tuning a transformer-based language model for a domain-specific task. They want to improve the model's factual accuracy and reduce hallucinations. Which THREE strategies should they consider? (Select THREE)

Accepted Answer

Fine-tune the model on a curated domain-specific corpus. Fine-tuning on a curated domain-specific corpus directly aligns the model with the factual patterns and terminology of the target domain. This supervised learning process adjusts the model's weights to reduce the probability of generating incorrect or hallucinated content by reinforcing ground-truth examples from the domain.

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Increase the model's context window size beyond the training limit

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Use a higher temperature setting during generation

Question 7

A data scientist needs to train a deep learning model on a large image dataset. Which hardware is most suitable for parallel matrix operations and faster training compared to a CPU?

Accepted Answer

GPU with thousands of CUDA cores. A GPU with thousands of CUDA cores is the most suitable hardware for parallel matrix operations because deep learning training involves massive matrix multiplications and tensor operations that can be decomposed into thousands of independent threads. CUDA cores execute these threads in a massively parallel SIMT (Single Instruction, Multiple Thread) fashion, achieving significantly higher throughput than a CPU for such workloads, which leads to faster training times.

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TPU designed for TensorFlow

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CPU with high clock speed

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FPGA for reconfigurable logic

Question 8

A deep learning engineer is training a transformer model and notices that validation perplexity increases after a few epochs while training perplexity continues to decrease. Which of the following is the MOST likely cause?

Accepted Answer

The model is overfitting the training data. The described pattern—decreasing training perplexity alongside increasing validation perplexity—is the classic signature of overfitting. The model is memorizing the training data rather than learning generalizable patterns, causing its performance on unseen validation data to degrade after a certain point in training.

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The temperature parameter is set too high

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The batch size is too small

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The learning rate is too low

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. 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.

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

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Add 10 more nodes to the cluster to increase total memory

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

Question 10

A bank deploys an AI system to approve loan applications. During testing, the model denies a disproportionate number of applicants from a particular demographic group, even after controlling for credit history. Which ethical principle is being violated?

Accepted Answer

Fairness. The AI system's disparate impact on a demographic group, even after controlling for credit history, directly violates the principle of fairness. Fairness in AI requires that models do not produce biased outcomes that systematically disadvantage protected groups, regardless of whether the bias stems from training data, feature selection, or algorithmic design. This scenario describes a clear case of algorithmic bias, which fairness principles aim to prevent.

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Transparency

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Privacy

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Accountability

Question 11

A healthcare AI system uses patient data to predict disease risk. To comply with HIPAA and reduce the risk of re-identification, which technique should be applied to the training data before model development?

Accepted Answer

Differential privacy with a carefully chosen epsilon. Differential privacy (Option C) is the correct technique because it adds calibrated noise to the training data or model outputs, providing a mathematical guarantee against re-identification even if an attacker has auxiliary information. This directly addresses HIPAA's requirement to protect patient privacy while preserving statistical utility for disease risk prediction.

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Pseudonymisation by replacing patient names with random IDs

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Data augmentation to create synthetic samples

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Data minimisation by removing all features except age and gender

Question 12

A data scientist wants to develop a computer vision model using transfer learning. They need a framework that provides pre-trained models and easy-to-use APIs for data augmentation and training. Which TWO frameworks are best suited for this task?

Accepted Answer

PyTorch. PyTorch (option B) is correct because it offers a rich ecosystem of pre-trained models via `torchvision.models`, along with built-in data augmentation transforms in `torchvision.transforms` and a flexible training loop that is ideal for transfer learning. Its dynamic computation graph makes it easy to modify model architectures for fine-tuning, which is a core requirement for the task.

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Hugging Face Transformers

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scikit-learn

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Keras

Question 13

A company deploys a chatbot using a large language model (LLM). After launch, users report that the chatbot sometimes generates plausible but false information. This phenomenon is known as:

Accepted Answer

Hallucination. Hallucination in LLMs refers to the generation of plausible but factually incorrect or nonsensical information. This occurs when the model's probabilistic next-token prediction produces confident-sounding outputs that deviate from training data or real-world facts, often due to insufficient grounding or training data gaps.

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Gradient explosion

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Overfitting

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Concept drift

Question 14

A data scientist is using differential privacy to protect individual privacy in a training dataset. Which TWO actions are correct implementations of differential privacy?

Accepted Answer

Set a privacy budget (epsilon) to limit information leakage. Setting a privacy budget (epsilon) is a core mechanism in differential privacy that quantifies and limits the amount of information leaked about any individual in the dataset. By controlling epsilon, the data scientist can formally bound the privacy loss, ensuring that the model's outputs do not reveal whether any specific individual's data was included in training.

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Train the model on a small subset of data to reduce exposure

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Remove all personally identifiable information (PII) from the dataset

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Aggregate data into groups before training

Question 15

A team is implementing an ML pipeline using a feature store. Which benefit does a feature store primarily provide in an AI operations context?

Accepted Answer

Consistency of feature computation between training and inference. A feature store ensures that feature engineering logic is stored, versioned, and reused consistently across both training and inference pipelines. This eliminates training-serving skew, a common cause of model degradation in production, by guaranteeing that the same transformations are applied to data regardless of when or where it is computed.

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Automated scaling of inference endpoints

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Real-time monitoring of model performance

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Automatic model versioning and rollback