AI0-001 Practice Test 4 — 25 Questions

Question 1

A recommendation system for an e-commerce site is producing stale suggestions that do not reflect recent user behavior. The system is updated offline every 24 hours. Which change would MOST directly address this issue?

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

Accepted Answer

Implement online learning to update the model incrementally in real time. Implementing online learning to update the model incrementally in real time directly addresses the staleness issue by allowing the model to incorporate recent user behavior as it happens. Online learning updates model parameters continuously or at short intervals, so recommendations reflect the latest interactions. This is the most direct solution to the problem of a 24-hour offline update cycle.

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Increase the number of features used in the model

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Add more training data from the past year

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Use a deeper neural network architecture

Question 2

A company is building a recommendation system that uses user embeddings stored in a vector database. The system must retrieve the top 10 most similar items for a given user query. Which vector database feature is MOST critical for this task?

Accepted Answer

Approximate nearest neighbor (ANN) search. Approximate nearest neighbor (ANN) search is the most critical feature because it enables the vector database to efficiently find the top-10 most similar items to a user query embedding without scanning the entire dataset. Unlike exact nearest neighbor search, ANN algorithms (e.g., HNSW, IVF) trade a small amount of accuracy for massive performance gains, which is essential for real-time recommendation systems handling millions of high-dimensional vectors.

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Built-in data versioning

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ACID transaction support

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SQL query interface

Question 3

A team is deploying an AI model for credit approval. Which TWO ethical considerations must be addressed?

Accepted Answer

Model interpretability. Model interpretability (B) is essential for credit approval because financial decisions must be explainable to regulators and customers under laws like GDPR or ECOA. A black-box model that cannot justify why a loan was denied violates compliance requirements, making interpretability a core ethical and legal necessity.

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Training speed

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Model accuracy

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Model size

Question 4

A data analyst wants to predict housing prices based on square footage, number of bedrooms, and location. Which machine learning approach is most suitable?

Accepted Answer

Linear regression. Linear regression is the most suitable approach because the problem involves predicting a continuous numeric target (housing prices) from multiple independent variables (square footage, bedrooms, location). Linear regression models the linear relationship between the features and the target, providing interpretable coefficients and efficient training for this type of regression task.

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K-means clustering

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Decision tree regression

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Association rule mining

Question 5

A retail analytics team is preparing a dataset for a demand forecasting model. The dataset contains a 'store_id' column with several thousand unique values, a 'product_category' column with about twenty values, and a 'day_of_week' column. The team wants to encode these categorical variables so a tree-based model can use them effectively without creating an enormous number of columns. Which TWO encoding approaches are most appropriate? (Choose two.)

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 6

A data engineer needs to combine two datasets, each with unique customer_id, to include all records from both datasets. Which join type should be used?

Accepted Answer

FULL OUTER JOIN. A FULL OUTER JOIN returns all records from both datasets, matching rows where the customer_id is present in both and filling in NULLs for missing matches. This is the only join type that guarantees every unique customer_id from either dataset appears in the result, which is exactly what the requirement specifies.

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RIGHT JOIN

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LEFT JOIN

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INNER JOIN

Question 7

A research lab is training a large language model and wants to minimize its environmental impact. Which THREE practices are most effective for reducing the carbon footprint of model training?

Accepted Answer

Apply model compression techniques like pruning and quantization. Model compression techniques like pruning and quantization directly reduce the computational requirements of training and inference. Pruning removes redundant weights, and quantization reduces the precision of weights (e.g., from 32-bit to 8-bit), which lowers the number of operations and memory bandwidth needed, thereby decreasing energy consumption and carbon emissions.

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Extend the number of training epochs to ensure convergence

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Increase the model size to achieve better accuracy faster

Question 8

An organization wants to use a pre-trained language model from a third-party vendor. What is the most important security step before deployment?

Accepted Answer

Vet the model for backdoors and malicious behavior. Vetting a third-party pre-trained model for backdoors and malicious behavior is the most important security step before deployment because the model could contain hidden triggers or biases intentionally inserted by the vendor or a compromised supply chain. Without this vetting, the organization risks deploying a model that behaves maliciously under specific conditions.

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Host the model on a public cloud

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Apply differential privacy to the model

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Fine-tune the model on internal data

Question 9

An AI team is developing a model that will make hiring recommendations. Which ethical principle requires that candidates be informed about how their data is used and have a way to challenge decisions?

Accepted Answer

Transparency and contestability.. Transparency and contestability directly require that candidates be informed about data use and have a way to challenge decisions. Beneficence, non-maleficence, and fidelity are ethical principles but do not specifically mandate disclosure and challenge mechanisms in automated hiring.

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Fidelity.

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Non-maleficence.

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Beneficence.

Question 10

A media company is deploying an AI service that transcribes customer support calls and then summarizes them for agents. The transcription model runs on-premises and produces text, but the summarization LLM is hosted in a public cloud. Compliance requires that no raw call audio or verbatim transcript ever leaves the company network. Which deployment pattern best satisfies this requirement while still using the cloud LLM?

Accepted Answer

Run the transcription on-premises, redact or abstract the transcript locally, and send only the redacted, non-verbatim text to the cloud LLM for summarization.. The requirement is a data-egress boundary: raw audio and verbatim transcripts must stay on-premises. The only pattern that respects that boundary while still using a cloud LLM is to perform transcription locally, then redact or abstract the text before sending only sanitized content to the hosted model. Encryption, private endpoints, and prompt instructions do not change where the sensitive data is processed, so they cannot satisfy the constraint.

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Transcribe in the cloud and store the raw audio in a cloud object store, but encrypt the bucket with a customer-managed key.

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Use a cloud-hosted transcription model and a cloud-hosted summarization model, and configure a private VPC endpoint between them.

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Send the raw audio to the cloud LLM and instruct the model via the system prompt to ignore the audio and only produce a summary.

Question 11

A company is deploying a fraud detection model that must return predictions within 100ms to avoid transaction delays. The team is deciding between batch and real-time inference. Which factor most strongly supports a real-time inference architecture?

Accepted Answer

The application requires immediate feedback for each transaction. Real-time inference is required when the application must return predictions within strict latency bounds (e.g., 100ms) to avoid transaction delays. The need for immediate feedback per transaction directly aligns with a real-time architecture, where each request is processed individually as it arrives, rather than waiting for a batch window. Batch inference would introduce unacceptable latency because it processes groups of records on a schedule, not on-demand.

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The model requires large amounts of historical data for each prediction

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The infrastructure budget is limited and must be optimized

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The model can be retrained weekly using gathered data

Question 12

A financial institution is deploying an AI system to approve personal loans. To comply with the EU AI Act's high-risk AI requirements, the bank must ensure meaningful human oversight. Which implementation BEST satisfies this requirement?

Accepted Answer

Require a human to review and approve every loan decision before it becomes final. The EU AI Act's high-risk requirements (Article 14) mandate that humans can effectively oversee AI systems, including the ability to intervene, override, or halt decisions. Requiring a human to review and approve every loan decision before it becomes final embeds a genuine human-in-the-loop control at the decision point, which is the strongest form of meaningful oversight for a high-risk credit-scoring use case. This ensures no automated output becomes binding without human judgment, directly satisfying the oversight obligation.

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Use a separate AI model to audit the primary AI's decisions weekly

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Allow applicants to appeal AI decisions through a customer service process

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Provide a dashboard showing the AI's confidence score for each application

Question 13

An organization wants to detect if someone is trying to steal their proprietary machine learning model by querying its API. Which monitoring technique is MOST effective?

Accepted Answer

Monitoring for anomalous query patterns, such as high volume or systematic variations. Model extraction attacks rely on systematically querying the API to reconstruct the model's decision boundary. Monitoring for anomalous query patterns—such as high request volume, uniform input distributions, or systematic variations (e.g., grid-like sampling of feature space)—directly detects the behavioral signature of extraction attempts, unlike passive controls that do not address the attack vector.

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Output filtering to remove sensitive information from responses

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Rate limiting on the number of API requests per user

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Input validation to reject malformed requests

Question 14

A team is building a recommendation system using collaborative filtering. They have a sparse user-item matrix. Which technique should they use to handle the sparsity and improve recommendations?

Accepted Answer

Matrix factorization. Matrix factorization (B) is the correct technique because it decomposes the sparse user-item matrix into lower-dimensional latent factor matrices, effectively capturing underlying patterns and filling in missing entries. This directly addresses sparsity by learning dense representations that generalize beyond observed interactions, which is a core strength in collaborative filtering for recommendation systems.

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Association rule mining

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k-nearest neighbors

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Content-based filtering

Question 15

A data scientist is building a binary classification model to predict customer churn. The dataset has 90% non-churn and 10% churn. After training, the model achieves 90% accuracy, but the recall for the churn class is only 20%. Which metric should the team primarily focus on to evaluate the model's effectiveness?

Accepted Answer

Recall for the churn class. With 90% non-churn and 10% churn, a model that predicts 'no churn' for everyone achieves 90% accuracy but 0% churn recall. The current model's 20% churn recall means it misses 80% of actual churners, which is the business-critical failure. Recall for the churn class directly measures how many true churners are caught, making it the primary metric.

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Accuracy

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Area Under the ROC Curve (AUC-ROC)

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Precision for the non-churn class

Question 16

A manufacturing company uses a computer vision AI to inspect products on an assembly line for defects. The AI model was trained on images from a single camera angle under bright, uniform lighting. Recently, the company moved the inspection station to a different part of the factory where lighting is dimmer and varies due to nearby windows. The model now misclassifies many non-defective products as defective, causing false alarms and production delays. The team has limited labeled data from the new environment. Which action should the team take to restore inspection accuracy while minimizing downtime?

Accepted Answer

Apply domain adaptation techniques using a small set of labeled images from the new environment. Domain adaptation techniques allow a model trained on a source domain (bright, uniform lighting) to generalize to a target domain (dim, variable lighting) using only a small set of labeled images from the new environment. This approach minimizes downtime because it avoids the need for large-scale data collection or retraining from scratch, and it directly addresses the distribution shift that causes false positives.

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Increase the defect classification threshold to reduce false positives

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Revert to the previous lighting setup by reinstalling bright, uniform lights

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Retrain the model from scratch using a large dataset of images from the new environment

Question 17

A team is building an AI-powered recommendation system for an e-commerce platform. They want to test the system before deployment. Which TWO types of testing are MOST relevant for this AI system? (Select TWO)

Accepted Answer

Evaluation frameworks for model output quality. Option C (Evaluation frameworks for model output quality) is correct because AI/ML systems produce probabilistic outputs that cannot be validated by traditional assertions; frameworks such as accuracy, precision/recall, F1, BLEU, or ROUGE metrics are needed to assess whether the recommendation model generates relevant, high-quality predictions before deployment. Option D (Unit tests for data pipelines) is correct because the recommendation system depends on feature engineering and ETL/data ingestion pipelines; unit tests verify transformations, schema conformance, null handling, and data integrity so that garbage data does not silently degrade model behavior. Option A (Load testing the web server) is not selected because it measures infrastructure throughput and concurrency rather than the AI system's model correctness or data quality. Option B (Integration tests for API calls) is not selected because, while useful generally, it tests service-to-service communication rather than the AI-specific concerns of model output quality and data pipeline correctness. Option E (Regression testing on the UI) is not selected because it validates front-end behavior and visual consistency, which is peripheral to testing the AI model and its data pipeline.

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Load testing the web server

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Integration tests for API calls

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Regression testing on the UI

Question 18

A team is developing an AI agent to assist users with multi-step tasks such as booking a flight, reserving a hotel, and scheduling a car rental. The agent needs to reason about the order of steps and handle dependencies. Which pattern is BEST suited?

Accepted Answer

ReAct pattern (Reasoning and Acting). The ReAct pattern (Reasoning and Acting) is best suited because it interleaves reasoning traces with tool calls, allowing the agent to dynamically plan and adjust steps based on intermediate results. For multi-step tasks with dependencies (e.g., booking a flight before a hotel), ReAct enables the agent to reason about order, handle failures, and call external APIs step-by-step, which is essential for robust task completion.

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Simple tool use without reasoning

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Using a single prompt with all instructions

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Fine-tuning a model to output all steps at once

Question 19

A retail bank has deployed a credit-risk scoring model as a REST endpoint behind an API gateway. The model was trained on data from 2019–2023. Compliance now requires the bank to detect when input feature distributions drift away from the training baseline and to trigger retraining before approval rates degrade. Which approach should the bank implement FIRST?

Accepted Answer

Enable population stability index (PSI) monitoring on each input feature against the stored training distribution and alert when PSI exceeds a set threshold.. Covariate drift is detected by comparing live inference feature distributions with the training baseline, and population stability index is the established metric for tabular models. It operates without ground-truth labels, so it fires early, before approval-rate decay confirms the problem. Fine-tuning on unlabeled traffic, autoscaling, or threshold manipulation all fail to measure distributional change and therefore cannot satisfy the compliance requirement to trigger retraining.

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Lower the classification threshold from 0.50 to 0.40 so more applicants are approved and the approval rate stays stable.

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Continuously fine-tune the deployed model on every new loan application that arrives at the endpoint.

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Increase the API gateway rate limit and add horizontal replicas so the endpoint can absorb higher application volume.

Question 20

A team is designing a deep learning pipeline for a computer vision task. They want to reduce overfitting. Which two techniques are specifically effective for this purpose? (Select TWO.)

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise that improves generalization.

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Using a smaller batch size

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Adding more layers

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Increasing the learning rate