AI0-001 Practice Exam 62 — 20 Questions

Question 1

A financial services firm is designing an AI system to detect fraudulent transactions. The dataset is highly imbalanced, with fraud representing less than 0.1% of transactions. The team wants to build a model that reliably identifies fraud while minimizing false positives that inconvenience customers. Which TWO techniques are MOST appropriate to address the class imbalance and evaluation needs? (Choose two.)

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

Accepted Answer

Apply class weighting or resampling to give more importance to fraudulent transactions. The two appropriate techniques are using precision-recall AUC for evaluation and applying class weighting or resampling. Precision-recall AUC highlights performance on the rare fraud class, while class weighting or resampling ensures the model learns from fraud examples. Together they address both the training imbalance and the need for meaningful evaluation, supporting reliable fraud detection with controlled false positives.

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Increase the number of layers in the neural network to improve capacity

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Remove all non-fraud transactions to balance the dataset

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Optimize only for accuracy to ensure overall correctness

Question 2

An organization runs a customer-support LLM that calls internal tools to look up order status and issue refunds. Security testing reveals that a user can paste text into the chat that causes the model to invoke the refund tool with an attacker-controlled amount. The team wants to reduce this prompt-injection risk without removing tool functionality. Which control is MOST effective?

Accepted Answer

Enforce authorization and parameter validation in the tool backend so refund requests are validated against the authenticated user's entitlements and business limits.. Prompt injection cannot be fully solved at the model layer, so the durable control is to enforce authorization and business rules in the tool backend where the model cannot influence them. Validating the authenticated user's entitlements and refund limits means a manipulated model still cannot perform unauthorized actions. Prompt instructions, temperature changes, and fine-tuning all rely on model compliance and fail as security boundaries.

Answer

Fine-tune the model on a curated dataset of injection attempts so it learns to recognize and refuse malicious prompts.

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Increase the model's temperature to zero so that responses become deterministic and injection attempts produce consistent refusals.

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Add a system prompt instruction telling the model to ignore any user instructions that attempt to change its tool-use policy.

Question 3

A healthcare analytics team trains a model to flag patients at risk of readmission. The dataset contains 9,500 non-readmitted patients and 500 readmitted patients. The model predicts the majority class for every patient and reports 95 percent accuracy, yet it identifies no at-risk patients. Which evaluation approach best reveals the model's failure?

Accepted Answer

Compute the confusion matrix and examine recall and precision for the readmission class. With a 95-to-5 class split, a majority-class predictor achieves 95 percent accuracy while providing zero clinical value. The confusion matrix and minority-class recall and precision expose the absence of true positives, which is the evidence needed to justify resampling, class weighting, or threshold tuning.

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Measure training time and inference latency across the full dataset

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Report overall accuracy on a stratified holdout set

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Calculate the mean squared error between predicted probabilities and labels

Question 4

A media company fine-tunes a large language model on Azure Machine Learning to generate sports recaps. After deployment, the model occasionally emits statistics that were never in the source game data. The team wants a systematic way to reduce these unsupported claims without retraining the base model. Which approach BEST addresses this?

Accepted Answer

Implement a retrieval-augmented generation pipeline that retrieves verified game statistics and constrains the model to cite retrieved passages.. Unsupported claims in generated text are best mitigated by grounding generation in a verifiable source. A retrieval-augmented generation pipeline supplies authoritative game statistics at inference time and instructs the model to rely on them, which reduces fabrication without altering the base model's weights. Deterministic decoding, more fine-tuning data, and regex filtering either miss the root cause or violate the no-retraining constraint.

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Increase the fine-tuning dataset size by adding more sports articles and repeat the fine-tuning job.

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Lower the temperature parameter to 0 and rely on greedy decoding to eliminate fabricated statistics.

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Apply a post-processing regex filter that removes any numeric token not present in the prompt.

Question 5

A support team deploys a retrieval-augmented generation assistant that answers questions from internal policy documents. Users report that the assistant confidently invents policy details that do not appear in any document. The team wants to reduce these fabricated answers without retraining the language model. Which change is MOST effective?

Accepted Answer

Improve retrieval relevance and instruct the model to answer only from retrieved passages, returning a fallback when evidence is absent.. Hallucination in a retrieval-augmented assistant is typically a grounding failure. Improving retrieval relevance delivers the correct source passages, and a prompt that restricts answers to retrieved evidence with a defined fallback prevents the model from filling gaps from its own parameters. Sampling temperature, retrieval volume, and prompt length do not address the underlying grounding problem.

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Increase the number of retrieved documents returned to the prompt without changing ranking quality.

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Shorten the system prompt so the model has more room for its own reasoning.

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Raise the model's temperature setting to make responses more varied.

Question 6

A fraud-detection team at a bank trains a gradient-boosted tree model on two years of transaction data. Only 0.4% of transactions are fraudulent. The model achieves 99.7% accuracy but flags almost no fraud. Which approach best addresses the underlying problem with how the model is being trained and evaluated?

Accepted Answer

Optimize the model using precision-recall AUC and apply class weighting or resampling to the fraudulent class.. Accuracy is a poor metric when one class is extremely rare, because a trivial majority-class predictor scores deceptively high. The real fix is twofold: train with class weighting or resampling so the learner sees the minority class, and evaluate with precision-recall AUC, which reflects performance on the fraud class rather than being swamped by true negatives.

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Remove outliers using a z-score filter and standardize all numeric features before retraining the model.

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Increase the number of boosting rounds and lower the learning rate until training accuracy reaches 100%.

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Switch the evaluation metric to root mean squared error and report it alongside accuracy for each boosting round.

Question 7

A media company uses a reinforcement learning agent to schedule promotional banners on its homepage. The agent receives a reward when users click a banner, and it has learned to show the same sensational headline repeatedly because it historically generated high clicks. The editorial team is concerned that this harms long-term user trust. Which modification best aligns the agent's objective with long-term user satisfaction?

Accepted Answer

Add a reward penalty for showing the same banner repeatedly and include a signal for user retention or satisfaction.. The agent's behavior stems from a reward function that values clicks without regard for long-term consequences. Adding a repetition penalty and a retention or satisfaction signal reshapes the objective so the agent learns to avoid overexposing sensational content and to prioritize sustainable user engagement.

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Increase the discount factor so future rewards are weighted more heavily.

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Reduce the size of the experience replay buffer so the agent forgets older click patterns.

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Switch from an epsilon-greedy policy to a softmax exploration policy.

Question 8

A multinational corporation is developing an AI system that will be deployed in multiple countries with varying data protection laws. The legal team wants to ensure compliance with regulations such as the GDPR. Which of the following is the most appropriate action to take during the design phase?

Accepted Answer

Implement data minimization and purpose limitation principles from the outset.. Incorporating data minimization and purpose limitation during the design phase aligns with GDPR's principle of data protection by design and by default. It ensures that the AI system only processes necessary data for specified purposes, reducing compliance risks. Other options either violate regulations or are insufficient. This approach is a best practice for multinational deployments.

Answer

Use only synthetic data for training to avoid any privacy concerns.

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Obtain blanket consent from all users for any future use of their data.

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Store all data in a central repository to simplify management and auditing.

Question 9

A logistics company is preparing an AI governance program for a route-optimization model that influences driver schedules. The compliance team must demonstrate accountability to regulators. Which two practices best establish documented accountability for this system? (Choose two.)

Accepted Answer

Maintain a model card and system inventory entry recording intended use, training data provenance, and known limitations.. Accountability is demonstrated through artifacts that show who owns the system and how its risks were assessed over time. A model card and inventory entry document intended use, data provenance, and limitations, while a named owner with recurring recorded risk reviews and sign-offs proves that governance was actively exercised. Throughput scaling, publishing engineering metrics, and access hardening address performance, disclosure, and security rather than accountability.

Answer

Increase the model's inference throughput by deploying additional GPU nodes in each region.

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Publish the model's hyperparameters and training loss curves on the company's public website.

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Restrict model access to the data science team and rotate their credentials every thirty days.

Question 10

A financial services firm runs a real-time credit-scoring model on an Amazon SageMaker endpoint. The model must not degrade: the team needs automatic detection of distributional drift in the incoming feature data and an alert when drift exceeds a threshold, without retraining the model. Which SageMaker capability should they configure?

Accepted Answer

SageMaker Model Monitor with a data quality baseline and drift detection schedule. Model Monitor is designed to continuously evaluate endpoint input data against a statistical baseline and raise CloudWatch alarms when drift exceeds configured thresholds. It inspects live inference requests without modifying the model, satisfying the need for automatic distributional drift detection and alerting. The other services either retrain, tune, or explain the model rather than monitor production feature drift.

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SageMaker Pipelines with a conditional step that retrains the model

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SageMaker Automatic Model Tuning with a hyperparameter search job

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SageMaker Clarify with a bias configuration on the endpoint

Question 11

A retail company trains a gradient-boosted tree model to forecast weekly demand for 500 stores. After six months, forecast error rises sharply even though the model code and pipeline are unchanged. Store openings, promotions, and seasonality have shifted the underlying demand patterns. Which action best addresses the root cause?

Accepted Answer

Schedule periodic retraining with recent data and monitor for data drift.. When a deployed model's error grows without code changes and the business environment has shifted, the cause is drift between training and production data. Retraining on recent data restores alignment with current patterns, and continuous drift monitoring provides early warning. This is the standard MLOps response to concept drift in forecasting systems.

Answer

Increase the number of boosting rounds in the existing model.

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Replace the gradient-boosted model with a deeper neural network.

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Lower the learning rate and re-evaluate on the original test set.

Question 12

A retail company wants its customer support chatbot to answer questions about current promotions that change weekly. The team has an LLM API but does not want to retrain the model each week. Which implementation approach is MOST appropriate?

Accepted Answer

Use retrieval-augmented generation to inject current promotion content into the prompt at query time.. Retrieval-augmented generation separates knowledge from model weights, so weekly promotion changes are handled by updating the retrieval corpus rather than retraining. This keeps the LLM stable while ensuring answers reflect current content. Fine-tuning and prompt-stuffing either violate the no-retraining constraint or scale poorly, making retrieval the most appropriate implementation.

Answer

Deploy a smaller open-source model and periodically replace it with a newly trained version each week.

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Increase the model's context window and paste all historical promotions into every system prompt.

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Fine-tune the LLM on a weekly export of promotion documents.

Question 13

A media company runs a public API that serves a proprietary image-classification model. The security team suspects an adversary is attempting a model extraction attack and wants to deploy monitoring and defensive controls. Which two measures are MOST effective for detecting or slowing model extraction? (Choose two.)

Accepted Answer

Return only the top-1 predicted label instead of full confidence scores from the API.. Model extraction depends on abundant, information-rich queries, so the strongest defenses constrain both the volume and the detail of responses. Per-account rate limiting with behavioral monitoring detects and throttles systematic probing, while limiting outputs to the top-1 label reduces the signal available per request. Together they raise the attack cost and improve detection without harming normal users.

Answer

Enable TLS 1.3 with perfect forward secrecy on all API endpoints.

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Add a watermark that embeds a unique identifier in every model prediction.

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Store model weights in a hardware security module and require signed model artifacts.

Question 14

A financial services firm runs a credit-scoring model in production on a managed cloud inference endpoint. Over three months, the input distribution of applicant income has shifted substantially because of a regional economic downturn, and the model's predictions have become systematically lower than actual repayment outcomes. The MLOps team needs an operational mechanism that will detect this change automatically and raise an alert before business metrics degrade further. Which approach should the team implement?

Accepted Answer

Configure a data drift monitor that compares live inference feature distributions against the training baseline using a statistical distance metric and triggers an alert when the threshold is exceeded.. The described situation is covariate shift: the distribution of input features has moved away from the training baseline, causing systematically biased predictions. A data drift monitor that statistically compares live feature distributions with the training baseline detects this shift and alerts the team early. Because labels lag, input-distribution monitoring is the practical early-warning control, whereas retraining, scaling, or logging alone do not provide timely automated detection.

Answer

Increase the inference endpoint's autoscaling limits so the service can absorb higher request volumes during the economic downturn.

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Schedule a nightly retraining job that rebuilds the model on the most recent thirty days of labeled outcomes and redeploys it automatically.

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Enable verbose request logging on the endpoint and retain raw payloads so analysts can manually review a sample of predictions each quarter.

Question 15

A junior ML engineer is asked to evaluate a binary classifier that predicts whether a bank transaction is fraudulent. The model's precision is 0.92 and recall is 0.41. The team wants to improve recall without retraining the model. Which action should the engineer take?

Accepted Answer

Lower the classification threshold for the fraud class.. Recall measures the fraction of actual positives the model captures. When a model has high precision but low recall, it is being too conservative about predicting the positive class. Lowering the decision threshold shifts the operating point along the precision-recall curve toward higher recall, which is exactly what the scenario asks for without retraining the model.

Answer

Apply L2 regularization and retrain the classifier.

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Switch the evaluation metric from F1 score to accuracy.

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Increase the number of training epochs and re-evaluate.

Question 16

A software company uses a pre-trained open-source LLM to build a customer support chatbot. Before deployment, the security team wants to verify that the model does not contain hidden backdoors that could be triggered by specific phrases. Which approach is MOST appropriate for this verification?

Accepted Answer

Use neural cleanse to detect potential triggers. Neural Cleanse is specifically designed to detect backdoors in neural networks by reverse-engineering potential triggers and analyzing their effect. It provides a systematic way to verify whether a pre-trained model contains hidden malicious behaviors, which is essential before deploying a third-party model.

Answer

Conduct red teaming with prompt injection tests

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Perform static analysis of the model's architecture

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Review the model's training data for anomalies

Question 17

A retail company uses an AI system to dynamically adjust prices based on individual browsing behavior. The system occasionally offers different prices to different customers for the same product. A customer advocacy group raises concerns under the EU AI Act. Which statement BEST describes the compliance obligation?

Accepted Answer

The company must provide clear information about the use of AI for personalized pricing and ensure it does not exploit vulnerabilities of specific groups.. The EU AI Act emphasizes transparency and prohibits AI systems from exploiting vulnerabilities of specific groups. Personalized pricing that adjusts based on browsing behavior can exploit economic or social vulnerabilities, so the company must clearly inform customers about the AI use and ensure it does not unfairly target protected groups.

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The company must obtain prior authorization from a national supervisory authority before deploying any AI system that adjusts prices.

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The company must ensure the AI system does not use subliminal techniques to distort behavior, as such techniques are prohibited under the EU AI Act.

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The company must publish the source code of the pricing algorithm to demonstrate compliance with the EU AI Act.

Question 18

A financial institution is deploying an AI model for credit scoring. The model must be explainable to regulators, and the team needs to understand which features contribute most to individual predictions. Which TWO techniques should they use? (Choose two.)

Accepted Answer

SHAP (SHapley Additive exPlanations). For explaining individual predictions in a credit scoring model, local explanation techniques are necessary. SHAP and LIME both provide per-instance feature importance, which can be presented to regulators to justify decisions. SHAP offers a solid theoretical foundation, while LIME is flexible and model-agnostic. Together, they cover the need for explainability.

Answer

Principal Component Analysis (PCA)

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Feature importance from a random forest

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t-SNE (t-Distributed Stochastic Neighbor Embedding)

Question 19

A marketing team wants to deploy a generative AI assistant that writes product descriptions. Before launch, they must ensure the assistant does not produce copyrighted text or brand-inappropriate claims. Which implementation step best addresses this requirement at generation time?

Accepted Answer

Add an output filter that checks generated text for restricted phrases and policy violations before display.. The requirement is to prevent non-compliant text from reaching users, which calls for a runtime control on the generated output. An output filter scans for restricted phrases, copyright matches, and policy violations and blocks or flags them before display. Training data, temperature, and manual review do not provide an automated generation-time check that enforces brand and legal policy.

Answer

Train the assistant only on the company's own historical product descriptions.

Answer

Reduce the model's temperature to zero so it always produces the most likely, safest token sequence.

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Ask users to review each description manually before publishing it.

Question 20

A media company wants to automatically generate concise summaries of lengthy earnings-call transcripts. The transcripts average 45 minutes of speech and contain domain-specific financial terminology. The team needs a solution that captures long-range dependencies and produces fluent, abstractive summaries without training a model from scratch. Which approach is most appropriate?

Accepted Answer

Fine-tune a pretrained encoder-decoder transformer (e.g., BART or T5) on the earnings-call corpus using abstractive summarization objectives.. Abstractive summarization of long, domain-specific transcripts is best served by fine-tuning a pretrained encoder-decoder transformer. Models like BART and T5 are pretrained on vast text corpora, giving them strong language understanding, and their architecture handles long-range dependencies through self-attention. Fine-tuning on earnings-call data adapts them to financial terminology and summary style without the prohibitive cost of training from scratch. This balances quality, fluency, and practicality.

Answer

Apply latent Dirichlet allocation (LDA) topic modeling to identify key themes, then generate summaries from the top topics.

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Use an extractive summarization algorithm like TextRank to select the most important sentences from each transcript.

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Train a large LSTM-based sequence-to-sequence model from scratch on the transcripts with attention.