operating systems

A machine learning engineer is training a transformer model for machine translation. The model's perplexity on the validation set is 8.5, and the BLEU score is 32. After increasing the number of encoder layers from 6 to 12, perplexity drops to 7.2 but BLEU decreases to 28. What is the MOST likely cause?

When implementing a vector store for a RAG system, which similarity search metric is MOST commonly used to find the most relevant document chunks for a given query embedding?

An AI risk manager is applying the NIST AI Risk Management Framework (AI RMF). In which function would the organization establish a risk management process and assign roles and responsibilities for AI oversight?

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate transactions and 1% fraudulent. The model achieves 99% accuracy but fails to catch most fraud. Which metric should the team prioritize to evaluate model performance?

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.)

When evaluating a binary classification model, which two metrics are most appropriate for imbalanced datasets? (Choose two.)

A data scientist trains a deep learning model on a large dataset. The training loss decreases steadily but the validation loss starts increasing after 20 epochs. The scientist uses early stopping with patience=5. Which of the following is the MOST likely cause and best corrective action?

A company is training a large language model and wants to reduce its carbon footprint. Which practice is MOST effective for reducing training energy consumption while maintaining model quality?

A healthcare organization uses an AI model to recommend treatment plans. The model was trained on data from a single hospital, and now treats patients from multiple demographics. Which ethical concern is most critical?

A developer is using Hugging Face Transformers to fine-tune a BERT model for sentiment analysis. They want to track experiments, log metrics, and compare runs. Which MLOps tool should they integrate?

A data scientist is preparing a dataset for a binary classification neural network. The dataset contains both numerical and categorical features, and some rows have identical entries. Which TWO preprocessing steps are most essential to improve model performance and avoid overfitting?

Which machine learning paradigm is best suited for training a model to play a game by learning from its own actions and rewards, without labeled data?

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?

Refer to the exhibit. A deep learning model is being trained. Based on the training log, which problem is most evident?

A company wants to recommend products to users based on their past purchase history. Which machine learning paradigm is BEST suited for this task?

A data scientist is cleaning a dataset. Which TWO actions are appropriate for handling missing data?

A team is implementing a RAG system for legal document retrieval. The documents are long (50-100 pages) with clear section headings. They want to ensure that retrieved chunks are semantically coherent and respect document structure. Which chunking strategy is MOST appropriate?

A developer is using a large language model via an API. They want the model to solve a math problem step by step. Which prompt engineering technique should they use?

A logistics company uses a machine learning model to predict delivery times based on historical data. The model was performing well, but recently it started making inaccurate predictions, especially for routes that have experienced new traffic patterns and road closures. The data engineering team receives an alert that the model's accuracy has dropped by 15% over the last week. They suspect data drift. The team has access to the original training data and a continuous stream of new data. What is the most appropriate first step for the team to take?

An organization deploys an AI system that processes personal data of EU citizens. Which regulatory framework imposes strict requirements on automated decision-making and profiling?

A data scientist is evaluating a logistic regression model for binary classification on highly imbalanced data. Which TWO metrics are most appropriate to assess model performance? (Choose TWO.)

A company wants to build a real-time anomaly detection system for IoT sensor data using edge AI. The model must run on resource-constrained devices with minimal power consumption. Which model optimization technique is MOST important?

A financial institution uses an AI model to approve loans. The model uses features including credit score and ZIP code. During an audit, it is discovered that the model has a high false positive rate for loan default predictions in certain ZIP codes. What should the institution do to address this?

A company is building a code generation assistant for internal developers. They want the assistant to generate code snippets consistent with the company's coding style and use private libraries. They have a few thousand examples of internal code. Which THREE considerations are critical when deciding between fine-tuning a base LLM and using RAG?

A company is developing an AI chatbot for customer service. They want to ensure the bot does not generate offensive or harmful responses. Which governance practice should be implemented first?

Refer to the exhibit. The model is a neural network for 10-class classification. The training log shows no improvement over 5 epochs. Which of the following is the most likely root cause?

A team is building a RAG system with a large repository of technical manuals. They want to ensure that each retrieved chunk is semantically coherent and that related concepts are grouped together. Which chunking strategy is BEST?

A data scientist is preparing a dataset for a binary classification model. The dataset has 95% majority class and 5% minority class. Which data preparation technique is BEST to address the class imbalance?

A marketing team wants to segment customers into groups based on purchasing behavior without predefined categories. Which algorithm should they use?

An AI agent is designed to book flights by calling an external API. The agent must decide which tool to call based on user input, then generate the correct API parameters. Which pattern is MOST appropriate for this workflow?

Which TWO techniques are commonly used to handle missing data in a machine learning dataset? (Choose TWO.)

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.)

A data scientist is building a classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraudulent cases. Which approach should the scientist use to evaluate model performance most effectively?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A company wants to deploy an LLM-based chatbot that can handle sensitive customer information. Which THREE measures should be implemented to mitigate prompt injection attacks? (Choose 3)

A machine learning team is deploying a sentiment analysis model for customer reviews. The model was trained on reviews from an e-commerce site but will be used for a social media platform. The team observes a drop in accuracy. Which concept best explains this issue?

An AI team is deploying a predictive maintenance model for industrial equipment. The model predicts failure within a 30-day window. The cost of a false positive is 10% of the cost of a false negative. Which evaluation metric should the team prioritize?

A healthcare organization is deploying an AI system to analyze patient records and recommend treatment plans. To comply with data privacy regulations, what is the most important security measure to implement?

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?

A company wants to reduce the carbon footprint of training large AI models. Which practice is MOST effective for achieving 'Green AI'?

Refer to the exhibit. A machine learning pipeline configuration is shown. During a deployment, the model evaluation passes with accuracy 0.86 and precision 0.79. However, the pipeline proceeds to deploy. What is the most likely reason for this behavior?

A company deploys a chatbot that frequently gives outdated information. They want to implement a defense against prompt injection that also ensures responses are based on verified knowledge. Which approach is best?

An organisation is developing a document intelligence system that extracts information from scanned invoices. Which THREE data preparation steps are critical to ensure high extraction accuracy? (Choose THREE.)

An organization's AI system uses a decision tree model for loan approval. The compliance team requires explanations for each decision. Which property of decision trees makes them suitable for this requirement?

A machine learning engineer is building a recommendation system for an e-commerce platform. The system should suggest products based on user purchase history and browsing behavior. Which model selection is BEST suited for this task?

An image classification model misclassifies a stop sign as a speed limit sign after a few pixels are altered. What is the most effective defense against such attacks?

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?

A financial firm deploys an LLM for automated trading advice. To prevent over-reliance, which combination of guardrails should be implemented? (Assume multiple options but choose the MOST comprehensive single approach.)

A company is considering using an open-source large language model for a commercial application. Which intellectual property consideration is MOST important when deciding between open-source and proprietary models?

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:

An e-commerce company uses a gradient boosting model to forecast daily sales. Recently, the model's predictions have become less accurate, showing a significant drop in R-squared on validation data. The data scientist checks for data drift but finds no significant changes in feature distributions. The model was trained on data from the past 24 months and is retrained monthly. Upon inspecting the feature importance, the data scientist notices that the top feature 'promotion_flag' has decreased in importance over time. What is the most likely cause of the performance degradation, and what should be done?

A financial institution uses a machine learning model to approve loans. They want to protect against membership inference attacks. Which THREE techniques are effective?

A company uses a large language model (LLM) to generate customer support responses. They notice the model sometimes produces harmful outputs. Which implementation strategy best reduces this risk while maintaining performance?

Which TWO are key requirements for AI governance under the EU AI Act for high-risk AI systems? (Choose two.)

A machine learning engineer wants to prevent data poisoning during the training of a model. Which practice is MOST effective for ensuring the integrity of the training data?

During testing of an AI system that classifies support tickets into categories, the team notices the model frequently misclassifies tickets about a new product feature that was introduced after the model was trained. Which type of testing should the team prioritize to catch this issue?

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?

A developer is fine-tuning a large language model for a code generation task. The available GPU has only 8GB of VRAM, and the base model is 7B parameters. Which fine-tuning technique is MOST feasible?

A data analyst needs to select two appropriate unsupervised learning techniques for clustering unlabeled data. (Choose two.)

A data scientist is building a natural language processing model to classify customer reviews as positive or negative. Which TWO preprocessing steps are most essential before tokenization? (Select two.)

Refer to the exhibit. A batch inference job fails with the given logs. What is the most likely root cause of the failure?

Which THREE practices are recommended for versioning machine learning models in a production environment?

An ML engineer wants to deploy a model as a REST API that can scale to handle thousands of inference requests per second. Which serving approach is most appropriate?

A machine learning team is training a large transformer model on a text corpus. They need to reduce training time while maintaining model accuracy. Which hardware configuration would be MOST effective for this task?

An AI system for fraud detection shows a gradual decline in precision over several weeks, though recall remains stable. Which type of model drift is most likely occurring?

A financial institution wants to use AI for loan approvals and must comply with fair lending laws. Which TWO practices should the institution adopt to mitigate bias and ensure compliance?

A machine learning engineer needs to containerize a PyTorch model for deployment on Kubernetes. Which THREE tools or formats should they use?

An AI system used for autonomous driving is found to have a lower accuracy in detecting pedestrians with darker skin tones. The development team wants to address this ethical issue. Which action is most effective?

What is the primary function of an AI ethics board within an organization?

During the evaluation phase of an AI project, the team measures the model's F1 score on a held-out test set. They find the F1 score is 0.92, but when deployed in production, the model performs poorly on new data. What is the MOST likely cause of this discrepancy?

A developer is building an AI agent that needs to call external tools (e.g., weather API, database) and reason about the results to answer user queries. Which THREE components are essential for implementing this agentic workflow?

A company wants to deploy an AI model for real-time inference on edge devices with limited computational resources. Which model architecture would be MOST suitable?

A manufacturing company is using a convolutional neural network (CNN) to detect defects on an assembly line. The model was trained on a balanced dataset of defective and non-defective parts. In production, the model shows high precision (95%) but very low recall (50%). The production line manager wants to minimize missed defects (false negatives). The data scientist has access to the original training data and can retrain the model. Which strategy is most effective for increasing recall while maintaining acceptable precision?

You are a security engineer at a large e-commerce company that uses an AI-based recommendation system. The system is deployed on a Kubernetes cluster and uses a TensorFlow model served via REST API. Recently, the security team detected unusual API calls that caused the model to return incorrect recommendations. Analysis shows that the inputs were crafted to maximize prediction error. The team suspects an adversarial attack. You need to implement a solution that detects and mitigates such attacks in real-time without requiring model retraining. Which approach should you take?

In unsupervised learning, which task involves grouping similar data points together based on feature similarities?

A self-driving car uses an AI model that learns by trial and error, receiving rewards for correct actions and penalties for mistakes. This type of learning is:

A company is building an AI-based resume screening tool. They want to ensure the system is secure against data poisoning attacks during the training phase. Which THREE of the following are appropriate defensive measures?

A company using an AI-based hiring tool receives a candidate request for explanation of an automated rejection. Which GDPR principle is most directly relevant?

A company trains a sentiment analysis model on customer reviews. An attacker submits hundreds of reviews with the word 'excellent' attached to negative feedback, causing the model to classify negative reviews as positive. This is an example of which attack?

A company is training a model on proprietary data and wants to prevent data poisoning. Which TWO practices are most important? (Select TWO.)

Which TWO of the following are appropriate uses of unsupervised learning?

A data scientist is deploying a machine learning model to production. The model was trained on an imbalanced dataset. Which technique should be used during deployment to mitigate bias without retraining the model?

A company deploys a machine learning model for fraud detection. After one month, the false positive rate has increased significantly. The model is retrained weekly on all historical data. What is the MOST effective immediate action?

A financial institution uses a deep learning model for loan approvals. Under the EU AI Act, this is considered a high-risk AI system. Which mandatory requirement must the institution fulfill before deployment?

A healthcare organization is deploying an AI model to predict patient readmission risk. They must comply with regulations that protect patient privacy. Which TWO techniques should they implement to enhance privacy preservation?

Which THREE are effective methods for ensuring data privacy in AI training? (Choose three.)

A company is fine-tuning a large language model using PEFT (Parameter-Efficient Fine-Tuning) to reduce GPU memory usage. They have limited hardware and need to fine-tune a 70B parameter model on a single GPU with 24 GB VRAM. Which technique is MOST suitable?

A data scientist notices that a binary classification model consistently predicts the majority class. Which data engineering technique should be applied?

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)

A team is training a recurrent neural network (RNN) with LSTM units to predict stock prices. The validation loss is significantly higher than the training loss. Which action is MOST likely to reduce the gap?

A team is fine-tuning a large language model using LoRA. They have limited GPU memory. Which technique can further reduce memory consumption while maintaining similar fine-tuning quality?

A data scientist is building a regression model to predict house prices. The dataset contains features such as square footage, number of bedrooms, and year built. Initial model performance is poor, and the scientist suspects that feature engineering could help. Which approach is most likely to improve model accuracy?

A data science team is preparing a dataset for a supervised learning task. They split the data into training and test sets. The team then normalizes the features using the mean and standard deviation calculated from the entire dataset before splitting. What issue does this introduce?

Which TWO strategies are effective for handling missing values in a dataset when the missingness is not random (MNAR)?

Refer to the exhibit. A data engineer notices that the batch processing step is taking too long and causing delays. Which change would most likely reduce the latency?

A data engineer is designing a pipeline to train a linear regression model on a dataset with 10 million rows and 50 features. The dataset fits in memory. Which approach should the engineer use to train the model efficiently?

A data scientist trains a deep neural network for image classification. The training loss decreases but validation loss starts increasing after 50 epochs. What should the data scientist do to improve generalization?

An LLM-based chatbot is being deployed for customer support. The security team wants to prevent the bot from generating toxic or harmful responses. Which defense is MOST appropriate?

A media company uses a natural language processing (NLP) model to classify news articles into topics. The model was trained on articles from 2015-2018. In 2023, the model's F1 score drops significantly. The data scientists find that the word embeddings no longer capture the meaning of some terms (e.g., 'covid', 'metaverse'). The model uses static word embeddings (Word2Vec) trained on the original corpus. Which solution BEST addresses the observed degradation? A. Replace static embeddings with contextual embeddings from a transformer model like BERT, then fine-tune the classifier. B. Retrain the static Word2Vec embeddings on a larger corpus from 2023. C. Apply data augmentation to the original training data by replacing words with synonyms. D. Increase the dimensionality of the static embeddings.

A data scientist is using a linear regression model to predict house prices and observes that the model performs well on training data but poorly on test data. Which regularisation technique is MOST appropriate to reduce overfitting?

Which TWO of the following are techniques used for reducing overfitting in neural networks? (Choose two.)

An organization is developing an AI system to approve loan applications. They want to ensure the model does not discriminate based on race or gender. Which technique BEST addresses this concern?

A bank uses an AI model to approve loans. During an audit, it is found that the model denies loans at a higher rate for a certain ethnic group. Which governance principle is primarily violated?

A company uses Azure OpenAI to generate customer support responses. The team notices that repeated queries with similar context incur high costs due to token usage. They want to reduce costs without affecting response quality. Which strategy is MOST effective?

An AI practitioner needs to measure the performance of a binary classification model for disease detection, where the cost of false negatives is very high. Which metric should be prioritized?

A data science team is training an image classification model for a medical imaging application. To prevent data leakage, they must partition the dataset correctly. Which approach ensures that no patient images appear in both training and test sets?

A data engineer is preparing a dataset for a binary classification model. The dataset has 10,000 samples with 100 features. To improve model performance and reduce training time, the engineer decides to perform feature selection. Which two techniques are appropriate for this task? (Select TWO).

Based on the exhibit, what is the most likely cause of the pod failure and its solution?

A data scientist is building a recommendation system using Apache Spark for feature engineering. They need to process streaming user click data in real-time before feeding into the model. Which tool should they use for the streaming data ingestion?

A cybersecurity firm is developing an AI system to detect zero-day malware using behavior analysis. The team collects a dataset of 1,000 malware samples and 10,000 benign files from corporate endpoints. The model is a random forest classifier. After deployment, the false positive rate is 5%, which is acceptable, but the detection rate for new malware variants drops to 30%. The security analyst suspects the model is overfitting to the specific malware families in the training set. Which improvement should the team implement first?

During data preparation for a classification model, the data scientist notices that one class has 95% of the samples and the other has only 5%. Which technique is MOST appropriate to address this imbalance?

A data engineer needs to design a data pipeline for a real-time fraud detection system. The system requires low-latency processing of streaming transactions. Which architecture is most appropriate?

A team is developing a sentiment analysis model and obtains the following performance on the test set: accuracy=0.92, precision=0.75, recall=0.80, F1=0.77. The baseline majority-class classifier achieves 0.85 accuracy. Which conclusion is MOST justified?

An AI system used for hiring has been found to exhibit racial bias against certain candidates. Which step should the organization take to mitigate this?

An organization is implementing an AI governance framework. Which THREE components are essential for compliance with ethical AI standards?

A data engineering team is designing a pipeline to train a model on streaming data. The data arrives in a time-series format. Which approach should they use to ensure the model reflects current trends without catastrophic forgetting?

A company is building a recommendation system for an e-commerce platform. They want the system to learn from user purchase history and browsing behavior to suggest products. Which type of machine learning is most appropriate for this task?

Which similarity metric is MOST appropriate for comparing dense vector embeddings in a vector store used for document retrieval, when the embeddings are normalized to unit length?

An e-commerce company deploys a recommendation system using collaborative filtering. After launch, the system shows high accuracy for popular items but fails to recommend niche products to users who would likely buy them. Which technique should the team implement to improve recommendations for long-tail items?

A hospital deploys an AI diagnostic assistant that analyzes medical images. The system has been in use for six months, and radiologists have reported that the AI is increasingly confident in its predictions, but sometimes misses rare conditions. The AI ethics board is concerned about overreliance and potential harm from false negatives. They want to implement a governance framework that ensures appropriate human oversight. The hospital has a limited IT budget. What is the best approach?

A machine learning engineer is training a neural network for image classification. The training loss decreases slowly and the model accuracy improves only marginally each epoch. Which hyperparameter adjustment is MOST likely to accelerate convergence?

A fraud detection model has high precision but low recall. The cost of false negatives is very high. Which threshold adjustment should be made?

A company is evaluating fairness metrics for a hiring model. They want to ensure that the model has similar true positive rates (TPR) across demographic groups. Which fairness metric should they use?

A hospital wants to train a diagnostic model using data from multiple hospitals without sharing raw patient data. Which technique allows model training across decentralised data while preserving privacy?

A bank wants to detect fraudulent transactions in real-time. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraud). Which evaluation metric is MOST appropriate for model performance?

A data scientist is selecting a model for a binary classification task where interpretability is critical because of regulatory requirements. The dataset has 20 features and 10,000 samples. Which model is MOST appropriate?

A financial institution uses an AI model to approve loan applications. The model was trained on historical data that included biased lending practices. The bank's ethics committee wants to mitigate bias without removing protected attributes. Which approach best balances fairness and model performance?

A data scientist is training a neural network to classify images of animals. The training accuracy is 99%, but validation accuracy is only 65%. Which technique should the data scientist use to address this issue?

A company is forming an AI ethics board to oversee the development of a high-stakes AI system for bail decision recommendations. Which THREE responsibilities should the board primarily undertake?

A data scientist is preparing a dataset for training a customer churn prediction model. To prevent train/test leakage, which TWO practices should be followed? (Select TWO)

A global retailer uses an AI model to forecast demand across thousands of stores. After deployment, the model's predictions become less accurate during holiday seasons. The training data included two years of holiday periods. What is the most effective operational strategy to handle this recurring seasonal drift?

A startup is building a chatbot to handle customer inquiries. They want the chatbot to understand context and provide accurate responses without requiring extensive labeled data. Which AI approach is most suitable?

A developer is using a pre-trained BERT model for a question-answering system. They want to ensure the model can handle out-of-vocabulary words. Which component of the BERT architecture is responsible for this?

Refer to the exhibit. Which model is NOT in full compliance with the policy?

A data science team is fine-tuning a large language model for a domain-specific task using LoRA. They have a limited GPU budget and want to minimize memory usage during training. Which technique should they use?

Which THREE are key principles of trustworthy AI according to the OECD?

Which THREE of the following are key components of an AI governance framework?

A developer is building an LLM-powered code assistant. They want to prevent the model from generating insecure code. Which OWASP LLM Top 10 category is most relevant to this risk?

A machine learning team is developing a model to predict server failure from telemetry data. They use a deep neural network with 3 hidden layers. After training, the model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the team apply to reduce the generalization error?

Which TWO of the following are common methods for mitigating bias in AI models?

A data scientist is training a convolutional neural network (CNN) for object detection. The training loss decreases rapidly but then plateaus at a high value, and the validation loss starts increasing. Which action should the scientist take to improve the model?

A company is deploying an LLM-based chatbot that must output responses in a structured JSON format for downstream processing. Which THREE prompt engineering techniques should the team use to ensure the output is valid and correctly structured? (Select three.)

Which THREE of the following are key components of an AI governance framework?

A credit risk model is being developed to predict loan defaults. The dataset has 95% non-default and 5% default instances. The data scientist trains a logistic regression model and obtains 95% accuracy, but the recall for defaults is only 10%. Which action is most appropriate to improve the model's ability to identify defaults?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

An AI ethics board is reviewing a model that recommends criminal sentencing lengths. They want to ensure that the model's false positive rates for different demographic groups are equal. Which fairness metric should they use?

A large e-commerce company has deployed a real-time product recommendation system using a neural collaborative filtering model. The model was trained on six months of user click and purchase data. For the first three months after deployment, the click-through rate (CTR) improved by 15%. However, starting in the fourth month, CTR began decreasing steadily despite no changes to the system infrastructure or data pipeline. The product manager suspects model decay but the engineering team insists the model is static and should not degrade. The data science lead suggests investigating further. They have access to production logs, A/B testing framework, and historical model versions. What is the BEST course of action to diagnose and address the issue?

A data scientist is evaluating a binary classifier for a hiring tool. They compute demographic parity and find that the selection rate for Group A is 0.2 and for Group B is 0.4. Which action would MOST directly address this disparity?

A team is deploying an AI model that predicts patient readmission risk. The model was trained on data from three hospitals but will be used in a fourth hospital with different patient demographics. What is the most important security risk to assess?

A healthcare startup is developing a diagnostic system using medical images. The team has collected 10,000 labeled images of skin lesions. They plan to train a convolutional neural network (CNN) from scratch. However, training converges slowly, and the validation accuracy plateaus at 70%. The data scientist suspects overfitting. The dataset contains 8,000 images of benign lesions and 2,000 of malignant. The team has limited GPU resources. Which of the following is the MOST effective course of action to improve validation accuracy? A. Reduce the number of convolutional layers. B. Apply transfer learning using a pre-trained model on ImageNet. C. Increase the learning rate by a factor of 10. D. Add more dropout after every convolutional layer.

A developer is integrating an AI microservice that accepts image uploads and returns classification labels. The service must handle spikes of up to 1,000 requests per minute but average 100 requests per minute. Which deployment architecture BEST meets these requirements with cost efficiency?

A data science team is developing a churn prediction model. Which TWO data preparation best practices are MOST important to prevent overfitting and ensure generalization?

A company's AI governance board requires each model to have a model card documenting intended use, performance metrics, and limitations. What is the primary purpose of a model card?

A company streams sensor data from IoT devices. The data arrives as JSON messages at high velocity. Which data pipeline architecture is BEST suited to handle this streaming data for near-real-time analytics?

An organization uses a machine learning model to approve loans. The model shows higher false positive rates for a protected group. Which data engineering step should be taken to mitigate this?

In the AI project lifecycle, which phase involves splitting the dataset into training, validation, and test sets while ensuring no data leakage?

A batch inference pipeline fails intermittently with out-of-memory errors when processing large datasets. The pipeline uses pandas DataFrames and feeds a pre-trained model. Which change would most effectively reduce memory consumption?

During a security audit of an AI system, the auditor applies the STRIDE threat model. Which threat category is MOST relevant to an attacker manipulating the training data to cause the model to misbehave on specific inputs?

A team is deploying an AI microservice for real-time object detection in streaming video. Which TWO integration patterns are most appropriate? (Choose two.)

A data scientist is training a model to classify customer support tickets into categories. The dataset has 10,000 labeled examples, but the 'billing' category contains 8,000 examples while the 'technical' category contains 2,000. Which technique is most appropriate to address this imbalance before training?

A data scientist is preparing a dataset for a text classification model. To prevent train/test leakage, which THREE practices should they follow?

An organization wants to classify support tickets into categories (billing, technical, etc.). Which type of machine learning is most suitable?

A company uses a neural network for fraud detection. The dataset has 99% legitimate, 1% fraudulent. The model achieves 99% accuracy but fails to detect most frauds. Which metric should they focus on?

Which NIST AI RMF function involves identifying the context, risks, and potential impacts of an AI system, including mapping the AI lifecycle and stakeholders?

A company deploys an LLM-based application that retrieves external web content to answer user queries. An attacker crafts a webpage that, when retrieved, injects a hidden instruction telling the LLM to ignore its system prompt and output sensitive internal data. What type of attack is this?

A data analyst wants to use a model that provides feature importance scores to understand which factors most influence customer churn. They also need the model to handle both numerical and categorical data with minimal preprocessing. Which algorithm is BEST suited?

A hospital uses an AI system to prioritize patient triage based on vital signs and medical history. During a trial, the system consistently assigns lower urgency to elderly patients with chronic conditions, even when their symptoms suggest high risk. Which approach best addresses this bias?

A company wants to use AI to automatically detect anomalies in server log data. The data is time-series and labeled with 'normal' and 'anomaly' for the past year. Which TWO techniques are appropriate for this use case?

An organization is building a recommendation system that requires low-latency vector similarity search. They need to store and query millions of embeddings. Which THREE technologies are appropriate for this task?

A self-driving car company is testing an AI model for pedestrian detection. During simulation, the model fails to detect pedestrians in low-light conditions. The safety team wants to improve robustness without retraining the entire model from scratch. Which approach is most appropriate?

A company wants to train a language model on sensitive customer data without transferring the raw data to a central server. Which privacy-preserving technique should they use?

An AI team is concerned about their model leaking sensitive information from its training data when queried. Which privacy-preserving technique adds noise to the training process to limit what can be inferred about any individual record?

A developer is building a natural language processing system to classify customer reviews as positive, neutral, or negative. They have 50,000 labeled reviews. Which model architecture is MOST appropriate for this task?

A company trains a large language model on a dataset that includes copyrighted books. Under current legal interpretations, which statement about copyright infringement is MOST accurate?

While training a deep neural network, the loss function fails to converge and oscillates wildly. Which adjustment is most likely to stabilize training?

Refer to the exhibit. A stream processor ingests events. One event arrives with missing "user_id". What will happen?

Which THREE factors are most critical to consider when designing a continuous integration/continuous deployment (CI/CD) pipeline for machine learning?

A company wants to roll out a new recommendation model to production. They decide to run an A/B test where 10% of users see the new model and 90% see the old model. After one week, the new model shows a 5% improvement in click-through rate. What is the next best action?

A data scientist is preparing a dataset for supervised learning. Which TWO steps are essential?

A team is building a regression model to predict house prices. The dataset includes numerical features (square footage, number of bedrooms) and categorical features (neighborhood, roof type). The categorical features have high cardinality (neighborhood has 200+ unique values). Which encoding strategy should the team use to avoid overfitting and maintain model interpretability?

An e-commerce company needs to update its recommendation model continuously as user preferences change. The model currently retrains from scratch every night, but the training time is too long. Which approach would reduce training time while keeping the model up-to-date?

Which THREE are common pitfalls when operationalizing AI models? (Select THREE.)

A healthcare startup is building an AI system to predict patient readmission risk. The team collects structured data from electronic health records (EHR) including age, diagnosis codes, lab results, and previous admissions. During initial training, the model achieves 95% accuracy on the validation set but only 60% accuracy on a holdout test set from a different hospital. The data scientist suspects overfitting. Which action should the team take first to improve generalization?

A hospital is implementing an AI system to analyze patient X-rays for potential fractures. The hospital must comply with HIPAA regulations. Which privacy-preserving technique allows the model to be trained on data from multiple hospitals without sharing raw patient data?

An AI system uses a pre-trained image classification model to detect defects in manufacturing. The team wants to deploy the model in an edge device with limited GPU memory. Which technique should they consider first?

A company uses a third-party LLM API to power its customer support chatbot. To prevent prompt injection attacks, which defense is MOST effective at the application layer?

A company is implementing an AI ethics board. Which TWO responsibilities should the board typically have?

A company wants to deploy a chatbot that uses natural language understanding (NLU) to answer customer queries. Which AI technique is most suitable for understanding the intent of user input?

An operations team monitors a classification model in production. The confusion matrix for the model shows the following values: TP=1500, FN=500, FP=600, TN=2400. Which metric should the team calculate to assess the model's ability to avoid false positives?

An AI engineer is selecting a PEFT technique to fine-tune a large language model. Which TWO are examples of PEFT (Parameter-Efficient Fine-Tuning)?

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?

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with 99% legitimate and 1% fraudulent. Which evaluation metric should be prioritized to assess model performance?

Refer to the exhibit. An AI auditor reviews the fairness configuration. What is the purpose of this policy?

A team is designing a secure API for an AI model. They want to prevent data leakage through overly detailed error messages. Which principle should they follow?

Which stage of the AI project lifecycle involves splitting data into training, validation, and test sets?

A data scientist is building a model to predict whether a credit card transaction is fraudulent, using labeled historical data. Which machine learning paradigm is being used?

A large e-commerce company uses a recommendation engine trained on millions of user interactions. Recently, the marketing team noticed a sharp increase in click-through rates for a particular product category. Upon investigation, an engineer found that a competitor had injected fake user profiles that consistently clicked on their products, skewing the training data. The company needs to remediate the attack and prevent future occurrences. The team has limited time and budget. Which course of action should the company take first?

An AI system is being developed to diagnose diseases from medical images. The model achieves 99% accuracy on the test set, but when deployed in a different hospital, performance drops significantly. Which of the following is the MOST likely cause?

An organization wants to centralize experiment tracking, model versioning, and deployment management across its data science team. Which MLOps platform is specifically designed for experiment tracking and model registry?

An AI system for autonomous vehicles uses reinforcement learning (RL) to navigate. The reward function encourages reaching the destination quickly but penalizes collisions heavily. The agent learns to drive aggressively, causing minor accidents. Which modification to the reward function would best align the agent's behavior with desired safe driving?

A developer is building an AI agent that needs to call external APIs (e.g., get weather, send email) based on user requests. Which pattern is BEST for enabling the agent to autonomously decide when to call these APIs?

A team is implementing a machine learning pipeline to classify images for a defect detection system. They are considering using a pre-trained convolutional neural network (CNN) and fine-tuning it on their small dataset. What is the primary advantage of transfer learning in this scenario?

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

A financial institution deploys an AI credit scoring model. After six months, the model's performance drops significantly. Analysis shows that the relationship between features and labels has changed. Which term describes this phenomenon?

Refer to the exhibit. A security auditor identifies a critical vulnerability that could allow an attacker to manipulate model inputs to cause misclassification. Which configuration setting is most directly responsible for this vulnerability?

A developer is implementing a RAG system and needs to chunk large legal documents. The documents contain nested clauses and cross-references that should not be split across chunks. Which chunking strategy is MOST suitable?

A retail company wants to build a model to predict customer churn based on purchase history and demographics. The dataset includes categorical features like region and gender, and numerical features like total spend. What is the best initial step before training the model?

A generative AI model produces images from text prompts. The outputs are often blurry and lack fine details. Which model type is MOST likely being used, and which improvement would best address this issue?

An ML operations team needs to monitor a deployed model's performance. Which TWO metrics are most useful for detecting concept drift in a regression model? (Choose two.)

An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?

An ML team uses the model registry above. After deploying version 3 to production, they discover it has a critical bug. What is the fastest way to roll back to a stable version without re-deploying from scratch?

Which THREE components are essential for implementing a successful MLOps pipeline for a continuously deployed AI system?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A data scientist is training a large language model and wants to reduce the carbon footprint. Which practice is MOST effective for reducing energy consumption during training?

Which privacy-preserving technique allows a model to be trained across decentralized data sources without the raw data ever leaving each source?

Based on the exhibit, which action is most likely to resolve the memory issue?

A developer is building an AI-powered code completion tool. They want to ensure that the tool does not inadvertently suggest insecure code patterns. Which practice is MOST effective for reducing this risk?

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

Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?

An AI development team is building a system to detect fraudulent transactions. They want to ensure the model complies with regulations requiring that individuals can question automated decisions. Which governance element is most relevant?

A company develops an AI model that recommends job candidates. The model inadvertently discriminates against a protected group. Which approach is most effective for mitigating this bias?

A team is evaluating an LLM-based code generation assistant. They want to measure the quality of generated code for correctness, security, and efficiency. Which evaluation framework is BEST suited for this task?

A hospital wants to deploy a machine learning model to predict patient readmission risk within 30 days. They have a dataset with 10,000 records, 70 features including demographics, lab results, and past admissions. The target variable is binary (readmitted or not). The data scientist trains a logistic regression model and achieves an AUC of 0.85 on the test set. However, the hospital's clinicians require interpretability of predictions to trust the model. Which action should the data scientist take to ensure the model meets the interpretability requirement while maintaining performance?

A data scientist is preparing a dataset for a classification task. The dataset contains 10,000 rows and 50 features, but many features have missing values. Which approach should the scientist take first to address the missing data?

A team is training a deep neural network on a large image dataset. They observe that the training loss decreases smoothly but validation loss oscillates. Which regularization technique should be applied?

A financial institution is implementing an AI-based fraud detection system. The compliance officer is concerned about potential bias in the model that could lead to unfair treatment of certain customer groups. Which governance practice should be prioritized to address this concern?

A startup is training a large language model and wants to reduce its environmental impact. Which TWO practices are considered green AI?

A data scientist is training a large language model on a custom dataset using PyTorch on AWS. The training is taking too long due to GPU memory constraints. The team wants to use multiple GPUs across instances with minimal code changes. Which AWS service should they use?

A data science team deployed a model for real-time predictions. After two weeks, the model's accuracy dropped from 92% to 80%. The monitoring system shows no data drift in features, but the target variable distribution has shifted. Which approach should the team use to detect this issue?

A data scientist is preparing a dataset for training a classification model. The dataset has a column with missing values in 5% of rows. Which action should the data engineer take to minimize bias?

An organization is deploying an LLM-based customer support agent. They want to protect against prompt injection attacks. Which THREE measures should they implement? (Select THREE.)

A data science team is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate and 1% fraudulent examples. Which TWO techniques should the team apply to improve model performance on the minority class?

After deploying a model for fraud detection, the data scientist observes a steady decline in precision over two months. Which issue is most likely occurring?

A security researcher demonstrates that by adding small perturbations to an image of a stop sign, an autonomous vehicle's AI misclassifies it as a speed limit sign. This is an example of which type of attack?

A healthcare startup is building a diagnostic support system using a large language model. The system must provide accurate, evidence-based answers and avoid generating harmful or fabricated information. Which THREE techniques should be implemented to achieve this? (Choose 3)

A data scientist discovers that a model trained to predict loan defaults is denying loans at a higher rate for a particular demographic group. Which type of bias is MOST likely present?

A healthcare startup needs to deploy an AI model for real-time patient monitoring on IoT devices with limited battery and compute. The model must run locally with minimal latency. Which TWO strategies are most appropriate?

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?

An AI system is designed to automatically execute actions on behalf of users, such as sending emails. The security team is concerned about excessive agency. Which mitigation is most effective?

During testing a chatbot, the QA team observes that the bot sometimes responds with harmful content when given adversarial prompts. Which type of testing should be prioritised to catch these edge cases?

An AI practitioner is fine-tuning a large language model for a domain-specific task using a small labeled dataset (500 examples). They have limited GPU memory. Which technique is MOST suitable?

A company deploys an AI chatbot that generates product descriptions. The company wants to be transparent about AI-generated content. Which practice should they follow?

An AI system must extract text from scanned invoices and output structured fields (invoice number, date, total amount). Which type of AI application is this?

During feature engineering, a data scientist creates a new feature that is a linear combination of two existing features. What risk does this pose to the model?

An AI system for detecting anomalies in manufacturing sensor data uses a model trained on normal operation data only. During monitoring, the model flags many false positives. Which adjustment is MOST likely to reduce false positives?

A developer is implementing a RAG system for legal document review. The documents are long (50-100 pages) with dense sections. They need to chunk the documents in a way that preserves semantic coherence while keeping chunks small enough for effective retrieval. Which chunking strategy is MOST appropriate?

A financial institution uses a machine learning model to approve personal loans. The model was trained on historical data that includes applicant age, income, credit score, and loan amount. Compliance officers have received customer complaints suggesting the model may be discriminating against applicants over 60 years old. Initial analysis shows that the approval rate for applicants over 60 is 20 percentage points lower than for younger applicants with similar credit profiles. The data science team has been asked to investigate and remediate any bias. They have access to the training data, model coefficients, and can retrain or modify the model. What is the FIRST step the team should take?

An operations team sees the log entries above for a production ML model. What is the MOST likely root cause of the latency spike?

A healthcare AI system diagnosing diabetic retinopathy from retinal images shows high accuracy overall but significantly lower recall for patients with darker skin tones. Which fairness metric would BEST capture this disparity by comparing true positive rates across groups?

An AI system is being designed to automatically detect fraudulent transactions in real-time. The system must have low latency and high precision to minimize false alarms. Which algorithm is most appropriate?

A team is using an API from a cloud AI service to generate text. They notice that repeated requests with the same prompt return different outputs. They want consistent responses for testing. Which parameter should they adjust?

A company is deploying a chatbot using a large language model. They want to mitigate the risk of prompt injection attacks. Which TWO measures should be implemented?

A financial institution is building a fraud detection system using a supervised learning model. The dataset is highly imbalanced with 99.9% legitimate transactions and 0.1% fraudulent ones. Which approach would be MOST effective to train the model to detect fraud?

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?

Which principle ensures that AI decisions can be traced back and understood by humans?

A company is deploying an LLM-powered application that answers questions based on internal documents. They want to minimize prompt injection attacks where users trick the model into ignoring instructions. Which THREE measures should they implement? (Select THREE)

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?

A hospital deploys an AI system to detect pneumonia from chest X-rays. The model achieves 95% accuracy on the test set but later is found to be less accurate for patients under 18. The development team suspects bias. Which step should be taken first to investigate?

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?

During an AI model deployment, the operations team notices that inference requests are taking longer than expected. Which component is most likely causing the bottleneck?

An organization is deploying an AI model on edge devices with limited computational resources. Which model optimization technique is most appropriate?

During model training, the data science team discovers that many input features contain missing values. Which step should be taken to improve data quality?

Which TWO of the following are effective techniques for detecting bias in an AI model?

A data scientist is building a model to predict credit default using historical loan data. The dataset contains 100,000 records with 50 features, including income, debt-to-income ratio, and loan amount. The target variable is binary (default vs. no default). The goal is to maximize interpretability while maintaining high accuracy. Which algorithm is MOST appropriate?

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?

A credit union uses an AI model to approve personal loans. The model was trained on historical data from the past five years. A recent internal review shows that the model approves loans predominantly for white applicants compared to other ethnicities, even when income and credit scores are similar. The credit union wants to comply with fair lending laws without significantly reducing overall approval rates. The data science team has access to the training data. What is the most appropriate remediation step?

Which TWO techniques are commonly used to prevent overfitting in deep neural networks?

A financial institution deploys an AI model for loan approval. To meet regulatory requirements under the EU AI Act for high-risk AI systems, they must ensure human oversight. Which implementation best satisfies the requirement for meaningful human intervention?

A company wants to generate realistic images of new product designs. They have a large dataset of existing product images. Which generative AI approach is MOST suitable for creating novel, high-quality images?

A large enterprise is developing an internal LLM-powered assistant that can access the internet and execute code. To mitigate risks from excessive agency (e.g., the model performing unauthorized actions), which THREE security measures should be implemented?

Which TWO statements correctly describe the difference between supervised and unsupervised learning?

An organization wants to integrate an AI-powered summarization feature into their existing web application. The AI service will be called via API. Which factor is MOST important to consider for cost management?

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?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A healthcare AI system is subject to GDPR because it processes patient data. Which THREE requirements must the system satisfy?

Refer to the exhibit. An auditor reports that the model's fairness check was bypassed in a recent deployment. Based on the policy, what is the most likely cause?

A company is building a recommendation system for an e-commerce site. They have historical user-item interaction data. Which approach is most appropriate?

A team is deploying a fine-tuned LLM for code generation. They need to ensure the model output is always valid JSON. Which prompt engineering technique should they use?

An organization uses a batch prediction pipeline that processes daily customer data to generate marketing recommendations. One month after deployment, the model's performance degrades significantly. The data pipeline logs show that the input data schema has changed — a new categorical feature 'customer_segment' has been added, and the existing feature 'age_group' is now missing. Which step should the operations team take first?

A company deploys an LLM-based chatbot that retrieves data from external databases. An attacker embeds malicious instructions in a database record. When the chatbot retrieves that record, it executes the instructions, overriding its system prompt. Which type of attack is this?

An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?

A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset contains 1% fraudulent and 99% legitimate transactions. The goal is to maximize recall for the fraud class while maintaining a precision above 0.5. Which data preparation strategy is MOST effective?

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?

A data scientist is fine-tuning a large language model for a domain-specific task using QLoRA. Which TWO statements correctly describe QLoRA's advantages?

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?

An organization uses a fine-tuned LLM for generating financial reports. An attacker gains access to the model's API and sends a series of queries that gradually reconstruct the training data of the fine-tuned model. This is an example of which attack?

A company deploys a deep learning model for real-time image classification. After deployment, they notice high inference latency exceeding the 100ms SLA. Which action would most likely reduce latency without significantly impacting accuracy?

A data scientist needs to explain why a black-box model denied a loan application. Which explainability technique generates local feature importance values using a simpler interpretable model around the prediction?

A company uses an AI model to predict equipment failures. The model outputs a probability of failure. To minimize false alarms, the operations team wants a high precision. Which deployment strategy should they implement?

Which THREE are common data preprocessing steps in a machine learning pipeline? (Choose 3)

A security team is reviewing an LLM-powered application that can execute SQL queries based on user requests. They want to implement defenses against prompt injection that could lead to unauthorized database access. Which TWO controls are MOST effective? (Select two.)

A large financial services company deploys multiple AI models on a shared Kubernetes cluster with GPU nodes. The models serve real-time fraud detection and credit scoring. Recently, the operations team observed frequent out-of-memory (OOM) errors during peak hours, causing inference failures. The monitoring dashboards show GPU memory utilization averaging 90% during peak times, and pods are being evicted. The team has allocated 8GB per pod and the total cluster GPU memory is 32GB. The models require at least 4GB each, but the fraud detection model occasionally spikes to 7GB. Which course of action best resolves the OOM errors while maintaining high availability?

An ML engineering team has a retraining pipeline that triggers automatically when model accuracy drops below a threshold. Recently, the model's accuracy has been fluctuating, causing frequent retraining and high compute costs. The team suspects the data distribution is changing slowly. Which approach should the team implement to reduce unnecessary retraining while maintaining model performance?

A developer is building an AI agent that needs to call external APIs to complete user requests. The agent must decide which API to call based on the user's natural language input. Which technique should the developer use to enable the agent to invoke APIs?

A developer is building an AI-powered code completion tool. To ensure the model does not output malicious code when prompted with 'Write code to delete all files on the system', which defense is most effective?

An e-commerce company deploys a deep learning model for product recommendation. After a new data pipeline is implemented, the model's online performance degrades: recall drops by 20% and the click-through rate decreases. The data scientists suspect data drift. They compare the distribution of the input features between the training data and recent production data. The Kolmogorov-Smirnov test shows significant differences for two numerical features (price and rating). The team also notices that the frequency of categorical feature 'category' has changed. Which of the following is the MOST appropriate first step? A. Immediately retrain the model on all available data including new production data. B. Roll back to the previous data pipeline and investigate the root cause of drift. C. Use feature selection to remove the drifting features and retrain. D. Implement a monitoring dashboard to track drift over time and set up alerts.

A company operating in the EU must comply with GDPR. An AI model processes personal data for customer segmentation. Which of the following ensures compliance?

Based on the exhibit, what issue should the team address?

A company is deploying a pre-trained image classification model for facial recognition in a security system. They are concerned about adversarial examples. Which TWO of the following are effective defenses against adversarial examples?

Under the EU AI Act, an AI system that uses subliminal techniques to materially distort a person's behaviour, causing psychological or physical harm, would be classified under which risk tier?

A team fine-tunes a 7B parameter LLM using LoRA on a custom instruction dataset. After training, they observe that the model's outputs are only marginally different from the base model. Which is the MOST likely cause?

A machine learning engineer is deploying a model to production. Which TWO practices are essential for ensuring reproducibility of model predictions?

A security analyst notices that an AI model used for facial recognition is returning unusually high confidence scores for certain individuals while consistently misidentifying others. Which type of attack is most likely occurring?

A team is deploying a multi-modal AI model that processes both text and images. They need to ensure that inference requests are handled quickly even during traffic spikes. Which integration pattern is BEST suited for this use case?

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?

A data scientist is training a neural network to classify images of handwritten digits. The model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the scientist apply first to address this issue?

A company wants to fine-tune a 70B-parameter LLM for a specialized domain but has limited GPU memory (e.g., 24 GB VRAM). Which technique allows fine-tuning with minimal memory footprint?

Which chunking strategy for RAG is MOST appropriate when documents have a natural hierarchical structure (e.g., sections, subsections)?

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?

An AI model achieves high accuracy on training data but performs poorly on new test data. The data scientist suspects the model has memorized noise. Which technique directly adds a penalty term to the loss function to address this?

A company is deploying a generative AI application that produces structured JSON output for downstream processing. They want to ensure the output is consistently valid JSON and matches a specific schema. Which THREE techniques should they use? (Select THREE)

A financial institution uses a regression model to predict credit risk. The model has a high R-squared on training data but low R-squared on test data. Which of the following is the most likely cause?

Refer to the exhibit. A data scientist defines a model configuration in JSON. Which component is missing from the configuration for a complete machine learning pipeline?

A data scientist is training a supervised learning model for customer churn prediction. Which TWO types of bias are most likely to affect the model's fairness and accuracy if not addressed?

Which THREE are common activation functions used in neural networks? (Choose THREE.)

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?

A data scientist is using a Hugging Face transformer model for a sentiment analysis task. They want to optimize inference latency for a mobile app. Which model format and framework combination is BEST suited for on-device deployment?

Which THREE are common causes of data leakage in machine learning pipelines?

A developer is implementing a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model produces normalized vectors. Which metric is computationally efficient and equivalent to cosine similarity for normalized vectors?

Under the EU AI Act, an AI system used for credit scoring is classified as high-risk. Which THREE obligations apply to the deployer of such a system?

A company serves a large language model (LLM) on a Kubernetes cluster. The inference latency is acceptable but the cost is high due to GPU usage. The model is 7 billion parameters and requires 16GB GPU memory. The team wants to reduce cost without increasing latency. Which strategy should they implement?

A machine learning engineer needs to choose an algorithm for grouping customers into segments based on purchasing behavior without any labels. Which algorithm should the engineer use?

A company uses an LLM API to generate customer support responses. They want to prevent the LLM from generating harmful content, even when users attempt jailbreaking. Which defense is MOST effective at the application layer?

A company uses a pre-trained language model for a legal document classification task. They have limited labeled data (500 documents). Which strategy is MOST effective for adapting the model to this domain?

A financial institution is developing a fraud detection model using historical transaction data. The dataset contains over 10 million records, but only 0.01% of transactions are fraudulent. The current model uses a neural network trained with standard cross-entropy loss, and the team applies random undersampling of the majority class to create a balanced training set. However, the model still produces a high number of false positives (legitimate transactions flagged as fraud) and misses approximately 30% of actual fraud cases. The business requires that at least 95% of frauds be caught, and the false positive rate must be below 1% to avoid overwhelming fraud analysts. The team has limited resources to collect additional data and cannot change the model architecture significantly. Which approach should the team take to best meet the business requirements?

During testing of a customer service chatbot, the team notices that the model sometimes generates plausible-sounding but factually incorrect answers about company policies. Which evaluation approach is BEST to systematically detect and quantify this issue?

A data scientist trains a linear regression model to predict house prices. The model has high bias and low variance. Which action would most likely reduce bias?

A team is training a language model using a large text corpus. They want to ensure the model does not learn biased associations between gender and professions. Which data engineering technique should they apply?

A data analyst is cleaning a dataset and finds that 20% of the values for the 'age' column are missing. Which imputation method is most robust if the data is not normally distributed?

A team is deploying a generative AI model for a real-time customer-facing application. They need to balance cost and latency. Which deployment strategy is MOST suitable?

A data scientist needs to explain why a specific loan application was rejected by a tree-based model. The model is complex and not inherently interpretable. Which method should the data scientist use to provide a local explanation for this single prediction?

A team is setting up a test suite for an AI system that includes a data pipeline, an LLM API call, and an output evaluation step. Which TWO types of tests should they prioritize to ensure the system's reliability?

An organization wants to fine-tune a 7B parameter LLM for a specialized legal document summarization task. They have a small labeled dataset (500 examples) and limited GPU budget. Which THREE techniques should they consider? (Choose three.)

A company deploys an LLM chatbot that has access to a database of customer orders. They want to prevent the LLM from revealing order details unless the user is authenticated as the owner. Which security control should be implemented?

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?

A team deploys a machine learning model as a REST API. They want to monitor model drift. Which metric is MOST appropriate for detecting drift in the input data distribution?

A team is developing a threat model for an AI system that processes user uploads. Using STRIDE, which threat involves an attacker modifying the model's training data to cause misclassification?

A machine learning engineer is deploying a real-time anomaly detection system for manufacturing sensor data. The system must process thousands of readings per second with minimal latency. Which deployment architecture is BEST suited?

Which TWO actions are most appropriate for managing model drift in a production AI system?

A financial services firm has deployed an AI model for real-time credit scoring. The operations team needs to ensure the model remains reliable and compliant over time. Which TWO actions should the team prioritize? (Choose two.)

An organization is deploying a deep learning model in production. Which THREE components are essential for maintaining model performance over time?

A financial institution is training a risk assessment model. The dataset includes customer credit scores, income, age, and past loan defaults. During feature engineering, a data engineer creates a new feature 'income_to_debt_ratio'. Which type of feature engineering technique is this?

A company is choosing between fine-tuning and RAG for a legal document assistant. Which TWO factors would MOST strongly favor RAG over fine-tuning?

An AI practitioner needs to extract key phrases from a large collection of customer support emails for trend analysis. Which technique is MOST suitable?

A data scientist is preparing a dataset for a regression model. The dataset contains 100 features, some of which are highly correlated. To improve model performance and reduce overfitting, which TWO techniques should the data scientist apply? (Select TWO)

A healthcare AI startup has developed a model to detect diabetic retinopathy from retinal images. The model achieved 96% sensitivity and 94% specificity on a validation set from the same distribution as the training data. After deployment in a rural clinic, the model's sensitivity drops to 80%. The data team analyzes the clinical images from the clinic and finds that the images have lower resolution and different lighting conditions compared to the training dataset. The team has the ability to collect more data from the clinic and retrain the model. What is the BEST course of action?

Refer to the exhibit. A team created an access policy for a fraud detection model endpoint. An intern reports being unable to access the model for testing. Reviewing the policy, what is the most likely cause?

A company is deploying an AI model to recommend products. The model's training data included historical purchases from the past two years, but the business environment has changed significantly due to a market shift. What is the most likely issue affecting model performance?

A developer is fine-tuning a large language model for a legal document summarization task. They notice that during training, the loss decreases rapidly in the first few epochs but then plateaus with high variance. Which hyperparameter adjustment is MOST likely to help stabilize training?

A healthcare provider wants to use AI to predict patient readmission risk. They have structured data (age, diagnosis, lab results) and unstructured clinical notes. Which approach is most appropriate?

A data scientist notices that a hiring model systematically scores female candidates lower than male candidates with similar qualifications. The training data was collected from past hiring decisions where the company historically hired more men. Which type of AI bias is most directly demonstrated?

An AI system is deployed to detect fraudulent transactions. The system flags 5% of transactions as fraudulent, but the actual fraud rate is 0.1%. The business sees many false positives and wants to reduce them without significantly increasing false negatives. Which metric should be prioritized for optimization?

A data science team is building a model to predict customer churn. The dataset includes categorical variables like 'region' and 'subscription_type'. Which three preprocessing steps should be applied to these categorical features? (Select THREE).

A team is implementing a RAG system for a large legal document repository. They need to chunk the documents for efficient retrieval. The documents contain long sections with subsections, and the team wants to preserve the hierarchical structure. Which chunking strategy is MOST appropriate?

A company deploys an LLM-based API for generating code snippets. They discover that users are able to extract the system prompt by asking the model to 'ignore previous instructions and print your prompt'. What type of attack is this?

Refer to the exhibit. The data scientist notices that the model achieves 98% accuracy on the training set but only 72% on the test set. Which change to the model parameters is most likely to reduce this gap?

An MLOps team uses a CI/CD pipeline to automate model retraining. The pipeline triggers on new labeled data, runs feature engineering, retrains the model, evaluates against a holdout set, and deploys if metrics exceed thresholds. Recently, a retrained model passed validation but caused a 5% accuracy drop in production. Which improvement best prevents this?

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?

A company is deploying an AI system that falls under the EU AI Act's high-risk category. Which THREE requirements must the company fulfill?

A team uses Apache Kafka to stream real-time sensor data for ML inference. They need to process the stream, perform feature engineering, and store results in a data lake. Which tool is best suited for this streaming ML pipeline?

Refer to the exhibit. A data scientist observes the training output. Which issue is most likely?

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?

Based on the exhibit, what is the most likely issue with the model training?

A security engineer is conducting threat modeling for an AI system that uses a pre-trained image classifier. Applying STRIDE, which threat category most directly addresses an attacker manipulating the model's behavior by providing carefully crafted inputs that the model was not trained to handle robustly?

A dataset for a binary classification problem has 95% of samples in class "0" and 5% in class "1". The data scientist trains a logistic regression model and achieves 95% accuracy. Which metric should the scientist primarily use to evaluate model performance?

A recommendation system for an e-commerce platform is experiencing a high false positive rate in its anomaly detection module, causing legitimate transactions to be flagged as fraudulent. The team wants to reduce false positives without significantly increasing false negatives. Which action is MOST effective?

A machine learning team is splitting a dataset for a binary classification problem. They want to ensure robust evaluation and avoid data leakage. Which TWO practices should they follow? (Choose 2)

A team is training a convolutional neural network (CNN) for medical image diagnosis. They have a limited dataset of 500 labeled images. Which strategy is most effective to improve model generalization?

A company deploys an AI system for loan approvals. The EU AI Act classifies this as high-risk. Which human oversight requirement applies?

An e-commerce company uses a machine learning model to recommend products to users. The model is retrained weekly and deployed to production. For the past three weeks, the model's click-through rate (CTR) has been stable except on Mondays, when it drops by 15%. Analysis reveals that the training data is extracted on Sundays and includes only weekday behavior. On Mondays, user behavior shifts due to weekend browsing patterns not captured in the training data. The team wants to maintain a weekly retraining cadence but fix the Monday performance drop. Which solution best addresses the Monday CTR drop without changing the retraining frequency?

A team is building a regression model to predict house prices. Which data transformation is most appropriate if the target variable exhibits right skewness?

Which TWO of the following are common techniques to improve the transparency and interpretability of an AI model?

A data scientist is preparing a dataset for a classification model. The dataset has missing values in several features and features with very different scales. Which two data preparation steps should be applied?

A company wants to build a code generation tool that helps developers write Python functions. The tool must generate syntactically correct code. Which prompt engineering technique is MOST effective?

Which three techniques are commonly used to mitigate overfitting in neural networks? (Choose three.)

A data science team is building a model to detect fraudulent transactions. They have a dataset of 1 million normal transactions and 1,000 fraudulent ones. What is the MOST effective data preparation step to handle this imbalance?

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?

A security team discovers that an AI-based anomaly detection system frequently misclassifies benign network traffic as malicious when the source IP is from a specific geographic region. Which type of AI vulnerability is most likely being exploited?

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?

A social media platform uses an AI system to moderate content. The system incorrectly flags legitimate posts as hate speech, disproportionately affecting minority groups. Which type of bias is likely present?

An organization deploys a machine learning model for credit scoring. An attacker submits carefully crafted loan applications that are slightly outside normal ranges but cause the model to approve high-risk loans. What type of attack is this?

An organization is adopting a third-party pre-trained language model for internal use. To assess supply chain security, which document should they request to understand the components and dependencies of the model?

A startup has developed a natural language processing model for sentiment analysis. Their CI/CD pipeline includes a step that runs unit tests on the model's output format and a validation step that checks accuracy on a static test dataset. Recently, the pipeline often fails during the validation step, but the failures are inconsistent—sometimes the same model version passes, sometimes fails. The team suspects the test dataset is small and randomly sampled. They need a reliable validation process to deploy models with confidence. Which approach should the team implement?

A team is developing a natural language processing model to classify customer feedback. The dataset contains text in multiple languages. Which THREE preprocessing steps are essential to ensure the model performs well across all languages?

A data scientist trains a regression model and notices the training loss is low but validation loss is high. Which technique should be applied FIRST to address this issue?

A company wants to build a system that can generate new product images for an online catalog. Which TWO generative AI approaches are most suitable?

A healthcare startup deploys an AI model to predict patient readmission rates. An internal audit reveals that the model consistently underestimates readmission risk for non-native English speakers. According to AI ethics principles, what is the most appropriate course of action?

A healthcare AI system uses patient data to predict disease risk. To comply with privacy regulations, the organization wants to ensure that the model cannot reveal whether a specific patient's data was used in training. Which technique should they implement?

A team is deploying a sentiment analysis model for social media posts. The model currently performs well on English text but poorly on code-switched text (e.g., Spanglish). Which approach is MOST effective for improving performance on code-switched data without starting from scratch?

An AI operations team notices that the accuracy of a deployed fraud detection model has been declining over the past month. Which action should the team take to address this issue proactively?

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?

A data scientist is training a customer churn prediction model using sensitive customer data. To comply with data privacy regulations, they want to minimize the risk of membership inference attacks. Which TWO techniques should they consider?

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

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?

A machine learning team notices that their model's performance degrades when deployed to a new geographic region. The data distribution in the new region differs from the training data. Which concept best describes this issue?

A company is evaluating a vendor's AI system for hiring. The vendor claims the system is fair because it achieves demographic parity. However, the company discovers that the system has significantly different false positive rates across groups. Which fairness issue does this indicate?

Which TWO are valid techniques to reduce overfitting in a deep neural network? (Choose TWO.)

A company wants to build a conversational agent that can handle complex multi-step tasks such as booking a flight, reserving a hotel, and scheduling a car rental in a single session. The agent must be able to break down the user's request into sub-tasks, call external APIs, and reason about the results. Which design pattern is BEST suited for this requirement?

A team is using k-fold cross-validation to evaluate a model. They observe high variance in performance scores across folds. Which TWO actions are most likely to reduce this variance? (Choose TWO.)

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?

Which TWO are best practices for deploying AI models in a containerized production environment? (Select TWO.)

Which TWO of the following are common activation functions used in deep neural networks?

A data engineer needs to process streaming clickstream data for real-time feature engineering in an ML pipeline. Which data pipeline technology is BEST suited for this task?

A company wants to use machine learning to recommend products to customers based on their purchase history. Which TWO techniques are appropriate for this task? (Select TWO)

A startup is building a chatbot for customer service. They have 500 recorded conversations and want to use a pre-trained language model to generate responses. However, they have limited computational resources and need the chatbot to respond in real-time. They are considering fine-tuning a large model like GPT-3 or using a smaller model like DistilBERT. The conversation data contains industry-specific jargon. Which approach should they take?

A security analyst is reviewing logs from an AI-powered recommendation system and notices an unusually high number of requests for products from a specific vendor. The analyst suspects data poisoning. Which mitigation strategy should be implemented first?

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

Which TWO are common types of adversarial attacks on AI models?

Which metric is most appropriate for evaluating a binary classification model where the positive class is rare and false positives are costly?

A deep learning model for image classification is overfitting the training data. The team has already tried data augmentation and dropout. Which additional technique should they implement to reduce overfitting?

An AI engineer is tuning a deep learning model and observes that the training loss decreases very slowly. The learning rate is set to 0.001. Which adjustment is most likely to speed up convergence?

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?

A cybersecurity team is red-teaming their internal LLM-powered code assistant. They want to test the model's resistance to jailbreaking techniques that bypass safety guardrails. Which TWO of the following should they include in their red teaming exercise to effectively evaluate jailbreak resilience?

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?

An AI system experiences degraded accuracy over time due to changes in user behavior. Which monitoring metric should be prioritized to detect this issue earliest?

A data scientist is monitoring a deployed image classification model. Which TWO actions are best practices for detecting model drift? (Choose 2.)

Refer to the exhibit. An AI governance review finds that a model was deployed without required ethics approval. Based on the audit log, who is most responsible for the compliance failure?

A security engineer is hardening an LLM application against indirect prompt injection attacks. Which TWO controls are MOST effective? (Select two.)

A large hospital system deploys an AI triage system for emergency rooms. The system uses patient vitals and symptoms to recommend treatment priority. Six months after deployment, complaints arise that the system frequently underestimates the severity of symptoms for patients from certain ethnic backgrounds. A data scientist runs a bias audit and finds that the model's false negative rate is 20% higher for the minority group. The hospital's AI governance board requires immediate corrective action. The data science team has limited resources and cannot retrain the entire model from scratch. They have access to the training data, which is imbalanced. The model is a gradient boosted tree. Which course of action best addresses the bias while minimizing operational impact?

A team is implementing a RAG system. They are designing the document loading and chunking strategy. Which TWO techniques are commonly used for chunking documents? (Select two.)

A data scientist is tuning a deep learning model. Which TWO hyperparameters directly affect the model's capacity to overfit?

A company deploys an AI model for loan approval. The model shows bias against a protected group. The team decides to use adversarial debiasing. What is the PRIMARY advantage of this approach?

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?

An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?

An AI model's performance drops significantly in production compared to testing. The data shows distribution shift. What is the best first step?

A company is training a large language model from scratch and wants to minimise its environmental impact. Which practice aligns with green AI principles?

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

Based on the exhibit, what is the most likely cause of the accuracy drop?

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal images. The model is trained on a dataset of 10,000 labeled images. During initial testing, the model achieves 99% accuracy on the training set but only 85% on the test set. The startup wants to deploy the model in a clinical setting where false negatives (missing a disease) are critical. The team has access to additional unlabeled retinal images from multiple sources. Which strategy should the team use to improve the model's generalization and reduce false negatives?

A company uses the above policy to control AI model access. A data scientist tries to run inference with model "llama-3-70b" at 150 requests in 30 minutes. What will happen?

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?

During a security audit of an AI-powered code generation tool, the audit team discovers that the system prompt (which contains sensitive internal instructions) can be leaked through carefully crafted user inputs. Which THREE OWASP LLM Top 10 categories are MOST directly relevant to this finding?

Which technique adds controlled noise to query results or training data to prevent an attacker from inferring whether a specific individual's data was included in the dataset?

A company wants to build a system that automatically tags uploaded images with objects they contain (e.g., 'car', 'tree', 'person'). Which AI application type is this?

Refer to the exhibit. What is the recall of the model?

A team is training a deep learning model for image classification. The training loss decreases steadily but the validation loss plateaus after 20 epochs and then starts to increase. Which action is MOST likely to improve generalization?

An e-commerce company uses an AI system to set dynamic prices for products. A customer complains that the price they see is higher than the price shown to a friend for the same product at the same time. The company wants to ensure pricing fairness. Which ethical principle should guide the redesign of the pricing algorithm?

An AI team is deploying a large language model for a customer-facing application. They need to ensure that the model's output is always in valid JSON format for downstream processing. Which prompt engineering technique should they use?

A data scientist is training a resume screening model to rank job applicants. The training data includes historical hiring decisions from the past 10 years. The company wants to avoid unfair bias against underrepresented groups. Which type of bias is most likely present in the training data?

Which TWO actions should be taken to ensure an AI model complies with GDPR requirements when processing personal data?

A developer wants to secure an AI API service. Which practice is MOST effective for preventing unauthorized access to the model?

A startup wants to add an AI-powered virtual assistant to their mobile app. They have limited in-house AI expertise and need a solution that can be integrated quickly with minimal infrastructure management. Which deployment pattern is MOST suitable?

A data science team uses Git for version control of model code and DVC for data versioning. They want to implement a model registry to track trained models, their hyperparameters, and performance metrics. Which tool is specifically designed for this purpose and integrates with the existing workflow?

An organisation is developing an AI policy. According to the NIST AI RMF, which function involves establishing policies and procedures to ensure the organisation governs AI responsibly?

Which embedding type is MOST suitable for capturing semantic meaning of text in a RAG pipeline?

A company has a TensorFlow model trained on-premises and wants to deploy it on AWS SageMaker for scalable inference. What is the BEST way to package the model for deployment?

An AI security team is conducting a threat model for a new document summarization service. They want to identify threats related to spoofing of the AI's identity. Which STRIDE category should they consider?

A team is implementing a RAG system for a legal document Q&A. They need to chunk documents effectively. Which THREE chunking strategies should they consider to improve retrieval accuracy for legal texts that contain hierarchical sections (clauses, sub-clauses, definitions)?

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal fundus images. The dataset contains 50,000 images, but only 5% are labeled as positive for the disease. The team uses a convolutional neural network (CNN) with a final sigmoid layer and binary cross-entropy loss. After training for 20 epochs, the model achieves 95% accuracy on the test set, but the recall for the positive class is only 10%. The team suspects the model is biased toward the negative class due to class imbalance. The data is stored in a secure environment, and no additional labeled data can be obtained. The team has access to the following techniques: oversampling the minority class, undersampling the majority class, using class weights in the loss function, applying data augmentation, and using a different architecture. Which course of action is most likely to improve recall for the positive class while maintaining reasonable overall performance?

During inference, a model served via a REST API occasionally returns high latency due to cold starts. The team uses a containerized service on Kubernetes with horizontal pod autoscaling. Which solution minimizes cold start impact while controlling cost?

A company wants to deploy a machine learning model that requires continuous learning as new data arrives. The model must be able to adapt to changing patterns without retraining from scratch. Which approach should be used?

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.)

A research lab trains a language model using DP-SGD. What primary privacy risk does this technique mitigate?

You are an AI governance officer at a bank that uses a machine learning model to predict credit risk. The model was developed by an external vendor and uses a proprietary algorithm. The bank's compliance team has determined that the model must be explainable to meet regulatory requirements. However, the vendor claims the model is a 'black box' and cannot provide explanations. You need to ensure compliance while maintaining the model's performance. What is the best course of action?

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?

Which TWO of the following are common activation functions used in neural networks? (Choose two.)

An AI operations team is monitoring a deployed image classification model. They notice a gradual increase in prediction confidence but a drop in accuracy. Which THREE actions should they take to diagnose the issue?

An organization wants to implement an AI system to automatically categorize support tickets into predefined categories. They have a labeled dataset of 10,000 tickets. Which approach is MOST appropriate?

In the AI project lifecycle, which phase involves partitioning the dataset into training, validation, and test sets?

A company is building an AI-powered document intelligence system to extract key fields from scanned invoices. The data contains 95% of invoices from one vendor and 5% from others. During model training, the F1 score is 0.95 on the overall test set, but the performance on the minority vendor invoices is very poor. What is the MOST likely cause?

A model trained on a dataset with imbalanced classes achieves 98% accuracy but only 50% recall for the minority class. Which technique should be applied first to address the imbalance?

Which THREE of the following are types of machine learning paradigms? (Choose three.)

A company uses AI to generate marketing images. They want to ensure that the images are clearly identified as AI-generated to comply with transparency obligations. Which approach is most effective?

An AI model is being developed for medical diagnosis from X-ray images. The dataset contains only frontal chest X-rays. The model achieves high accuracy on test set but fails on lateral views. What is the most likely cause?

In the AI lifecycle, which phase involves splitting data into training, validation, and test sets?

A model's training accuracy is 99% but validation accuracy drops to 60%. What is the most likely issue?

A retail company uses a time-series model to forecast daily sales for inventory management. The model is a seasonal ARIMA trained on three years of daily data. It performed well during initial validation but after deployment, forecasts became inaccurate during holiday seasons, often underestimating demand by up to 40%. The data science team examined the features and found that the training data did not include any holiday indicators. They also discovered that the model's residuals show strong autocorrelation during holiday weeks. The company needs to improve the forecast for the upcoming holiday season. They have access to historical sales data with holiday dates and are considering several approaches. Which approach will BEST address the issue?

A data engineer is reviewing an S3 bucket policy for a machine learning project. The policy is intended to allow access to training data only from the corporate network (10.0.0.0/16). However, users in the corporate network report access denied. Which issue is most likely causing the problem?

A government agency is deploying an AI model to screen loan applications. The model uses features like income, credit score, employment history, and zip code. During fairness auditing, the model is found to deny a disproportionately high number of applicants from a particular demographic group, even when controlling for legitimate financial factors. The agency wants to mitigate this bias without significantly reducing overall accuracy. Which approach should the data scientist prioritize?

An AI chatbot is being developed to assist with customer support. The team is concerned about prompt injection attacks where malicious users try to override the system's instructions. Which defense is MOST effective against direct prompt injection?

A data engineer needs to store training data in a format that supports columnar pruning during model training. Which storage format should they use?

A company built a speech-to-text model using a recurrent neural network (RNN). During deployment, the model performs poorly on accented speech. Which action would most effectively improve model robustness?

Which AI governance framework is specifically designed by the U.S. National Institute of Standards and Technology (NIST) to help organizations manage AI risks?

A company is building a RAG-based Q&A system for a large collection of technical manuals. They need to choose an embedding model and a similarity search method. Which TWO choices are most appropriate for this scenario? (Select TWO)

Which TWO techniques are specifically designed to protect individual privacy when training AI models?

A company wants to create an AI system that can identify objects in images. They have a large dataset of labeled images. Which type of neural network architecture is most suitable?

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?

A data scientist is training a binary classifier and observes that the training accuracy is 99% but the test accuracy is only 70%. Which of the following is the MOST likely cause?

A retail company uses a machine learning model to predict daily sales. The model takes features like past sales, promotions, holidays, and weather data. Recently, the model's accuracy dropped significantly. The data engineer checks the data pipeline and finds that the weather data source changed from a free API to a new paid API that provides more detailed data. The new data includes additional attributes like humidity and wind speed, but the existing pipeline only ingests temperature and precipitation. Also, the time zone format changed from UTC to local time. The model was trained on the old format. Which action should the engineer take first to restore model performance?

A team is deploying a deep learning model that uses a convolutional neural network (CNN) for image recognition. The model achieves high accuracy but is very slow to infer on edge devices. Which THREE optimization techniques should the team consider to speed up inference without significant accuracy loss? (Select three.)

A data scientist submits a model training job to a cloud ML platform. The job fails with an error: "Out of memory: Killed process." The training code is proven to work on the developer's local machine with 16GB RAM. The cloud instance has 32GB RAM. What is the most likely cause?

A team is designing an AI agent that needs to interact with external APIs, search the web, and perform multi-step reasoning. Which TWO architectural components are essential for this agentic workflow? (Choose TWO.)

A financial institution needs to integrate an AI-based credit scoring model into an existing mainframe system that processes transactions in COBOL. The model is deployed as a REST API. What is the best strategy to ensure minimal disruption and maintain data integrity?

A model's predicted probabilities are well-calibrated overall but the model systematically assigns higher probabilities to one demographic group even when the actual outcome likelihood is the same. Which fairness issue is present?

Which TWO of the following are effective defenses against adversarial examples in AI systems?

Which TWO of the following are key characteristics of unsupervised learning?

Which TWO techniques should be considered when optimizing a deep learning model for deployment on edge devices with limited computational resources?

A hospital's radiology department uses an AI model to detect lung nodules in CT scans. The model was trained on data from a specific brand of scanners and patient demographics common in Europe. Recently, the hospital acquired new scanners from a different manufacturer and started serving a more diverse patient population. Over the past month, the model's false-positive rate has increased by 15% and false-negative rate by 8%. The radiologists are losing confidence and are considering abandoning the AI tool altogether. The IT team has verified that the model inference is running correctly and the hardware is performing as expected. The data science team suspects the problem is related to the change in input data distribution. The hospital's AI operations policy requires that any model update must be validated on at least 500 recent cases before deployment. What is the BEST course of action for the AI operations team?

A machine learning engineer is tuning a neural network for image classification. The training loss decreases steadily, but the validation loss starts increasing after 50 epochs. Which action best addresses this issue?

Which THREE data quality dimensions are critical for ensuring model reliability?

A small business launched a customer support chatbot powered by a pre-trained language model. The chatbot was fine-tuned on a dataset of past support tickets. For the first week, it performed well, accurately answering 85% of queries. After a routine software update that included a new version of the underlying language model library, the chatbot's accuracy dropped to 60% and it began giving nonsensical responses to some questions. The update did not change any code or configuration specific to the chatbot. The business has a backup of the previous environment. What is the MOST appropriate immediate action?

A data science team uses Vertex AI for model training and deployment. They want to implement CI/CD for ML pipelines. Which THREE Google Cloud services should they integrate?

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.)

An organization uses a third-party pre-trained model for a medical diagnosis system. Which supply chain security measure is MOST critical to verify before deployment?

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?

A DevOps team is deploying a machine learning model using a CI/CD pipeline. They want to ensure the model is reproducible and traceable. Which TWO practices should they implement?

A team is designing a RAG system for a large collection of PDFs. They need to choose document chunking strategies. Which TWO strategies are considered best practices? (Choose two.)

An organization wants to train a machine learning model on sensitive patient data without exposing individual records. Which privacy-preserving technique allows the model to learn from data distributed across multiple hospitals without raw data leaving each site?

A data scientist is preparing a dataset for a supervised learning model. The dataset contains missing values in 15% of the rows for a numeric feature. Which preprocessing technique should be applied to minimize bias?

Which TWO are characteristics of supervised learning?

An AI system trained on historical medical records shows that certain racial groups have higher predicted risk for a disease. The data reflects real-world differences in diagnosis rates due to unequal access to healthcare. Which type of bias is this?

An AI model for skin cancer detection achieves high accuracy but performs poorly on dark skin tones. The team wants to evaluate whether the model is calibrated across skin tones. Which fairness metric should they use?

A company uses an LLM to generate code. They want to ensure that the model does not accidentally output sensitive internal logic. Which practice should they implement?

A team is considering whether to fine-tune a base LLM or use RAG for a question-answering system over a large, static corpus of scientific papers. The answer must be highly accurate and grounded in the papers. Which approach is BEST and why?

Which of the following best describes the difference between narrow AI and general AI?

A hospital wants to train a diagnostic model using patient data from multiple hospitals without sharing raw patient records. Which technique enables collaborative model training while keeping data decentralised?

A data scientist is preparing a dataset for a classification model. The dataset contains a column "Age" with 10% missing values and a column "Income" with 30% missing values. Which imputation strategy is MOST appropriate to minimize bias?

A company wants to store unstructured text data for AI model training while enabling SQL-based queries for analytics. Which storage solution should they use as the primary data source?

Which TWO of the following are effective defenses against adversarial evasion attacks on image classifiers?

The exhibit shows a model configuration for a classification task with 10 classes. What is wrong with this setup?

A team is using a cloud AI service with a pay-per-token pricing model. They want to minimize costs while maintaining response quality. Which strategy is MOST effective?

A data science team needs to implement privacy-preserving ML for a healthcare model. They require that individual patient records cannot be distinguished in the training output. Which technique should be applied?

An AI engineer is training a deep neural network for image recognition. The training loss decreases steadily for the first few epochs but then plateaus and starts to oscillate. Which adjustment is most likely to improve convergence?

A large e-commerce company uses a recommendation system based on collaborative filtering. The system uses a matrix factorization model that is trained nightly on the entire user-item interaction history. Recently, the company launched a flash sale with thousands of new products. Users are reporting that the recommendations are not showing the new products, even for users who have purchased them during the sale. The data engineering team notices that the new products have very few interactions in the training data. The model's loss on the validation set has increased, and the recall@10 metric has dropped from 0.45 to 0.32. The team needs to improve the recommendation of new items without retraining the entire model from scratch every hour. Which approach should the team take?

A company implements a chatbot using a rule-based system. Users complain the chatbot cannot handle new queries. Which AI approach should be considered to improve flexibility?

An organization uses an LLM to generate financial reports. They want to ensure the model does not output sensitive customer data that it may have memorized during training. Which technique should be implemented in the AI pipeline to detect and block such outputs?

An organization is deploying an image classification model to detect defects on a production line. Which TWO steps are essential during the model monitoring phase of the AI project lifecycle?

An AI developer observes that the training accuracy of a neural network is high, but the test accuracy is low. The model uses a ReLU activation function and Adam optimizer. Which approach is most likely to improve test accuracy?

A team is evaluating an LLM-based chatbot that frequently hallucinates when answering questions about internal policies. Which testing approach would MOST effectively quantify this issue?

A machine learning engineer notices that a fraud detection model's false positive rate has increased significantly over the past week. The model was retrained two weeks ago with new data. Which attack is MOST likely responsible?

An organization needs to store sensitive customer data for training a machine learning model. The data must be encrypted at rest and in transit, and access must be audited. Which combination of practices should be implemented?

An AI security analyst is reviewing the OWASP LLM Top 10. Which of the following is listed as the top vulnerability?

A data scientist wants to group customers into segments based on purchasing behavior without predefined labels. Which type of machine learning is most appropriate?

During a security review, an auditor finds that an LLM application can call external functions (e.g., send emails, update databases) based on user prompts. Which risk is MOST concerning?

A company deploys an AI model to predict equipment failure. The model performs well on historical data but fails to generalize to new data from a different factory. Which concept best describes this issue?

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?

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?

An AI system in a self-driving car misinterprets a stop sign due to a small sticker placed on it. This is an example of which security vulnerability?

Refer to the exhibit. A team deploys a sentiment analysis model with this policy. After one month, the monitoring system triggers an alert for feature drift. Which action should the team take first?

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?

An AI system is being implemented in a healthcare setting. Which TWO ethical considerations should be prioritized?

A developer is building an AI microservice that processes document intelligence requests asynchronously. Users upload PDFs, and the service extracts text and analyzes it with an LLM. The processing time per document can be up to 5 minutes. Which integration pattern is MOST appropriate?

A company is building a document intelligence system that extracts key fields from scanned invoices. They have a labeled dataset of 10,000 invoices but need to decide between a traditional OCR+rule-based pipeline and an AI-based model. Which use case characteristic STRONGLY favors the AI-based approach?

A team trained a ResNet-50 model with the configuration shown. The high training accuracy and lower validation accuracy suggest overfitting. Which change to the training configuration is MOST likely to reduce overfitting?

Which TWO are best practices for versioning machine learning models? (Choose 2)

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?

An organization wants to implement a robust MLOps pipeline. Which THREE components are essential for a complete MLOps framework? (Choose three.)

A company is developing an AI-powered recruitment tool. To prevent bias and ensure fairness, they want to audit the model's training data and outputs. Which TWO practices should they implement as part of secure AI development?

A model serving pod is failing with OOMKilled. What is the most likely cause?

A company deploys an AI model via a REST API that handles sensitive customer data. To secure the endpoint, the security team requires that only authenticated and authorized applications can invoke the API. Which mechanism should be implemented?

A machine learning team is evaluating a logistic regression model for a binary classification task. The dataset has 1,000 samples and 20 features. Which TWO metrics are most appropriate for evaluating model performance? (Choose 2)

You are an AI engineer at a financial services firm. The company has deployed a gradient boosting model to predict loan default risk. The model takes features such as credit score, debt-to-income ratio, loan amount, and employment length. In production, the model processes about 10,000 predictions per day with an average latency of 50ms. Recently, the accuracy has dropped from 92% to 85%. You also notice that the average credit score of applicants has increased significantly because the marketing team launched a campaign targeting prime borrowers. The model was originally trained on data from the past three years, which included a mix of prime and subprime borrowers. You need to restore model performance while minimizing downtime and retraining cost. Which action should you take first?

An attacker repeatedly queries a public LLM API with carefully crafted inputs to reconstruct the model's architecture and approximate weights. This is an example of which attack?

A data science team is preparing a dataset for a binary classification model. The dataset has 95% negative class and 5% positive class. Which technique should they apply to avoid biased model predictions?

A chatbot application uses a system prompt to set the assistant's behavior. The developer wants the LLM to output structured JSON for downstream processing. Which technique BEST ensures the output is valid JSON?

A chatbot developer uses a transformer-based model for customer service. Users complain that the chatbot sometimes gives offensive responses. Which technique should be applied first to mitigate this issue?

A data scientist is preparing a dataset for training a classification model. The dataset contains 10,000 records with a binary target variable where 9,500 belong to class A and 500 belong to class B. Which technique should the scientist use to address the class imbalance?

A security analyst at a fintech company is alerted to anomalous API requests to their deployed LLM chatbot. The requests contain carefully crafted inputs that cause the model to generate responses that include internal system prompts. Which type of attack is MOST likely occurring?

An AI team notices that their model's performance degrades over time because the statistical relationship between input features and the target variable changes. This issue is called:

A healthcare company must deploy a diagnostic AI model that uses protected health information (PHI). To comply with HIPAA, the operations team needs to ensure data privacy during model inference. Which practice should be implemented?

Which TWO are common attack vectors against AI systems? (Choose two.)

An organisation needs to deploy PyTorch models on mobile devices with minimal latency. Which framework or tool should they use to convert and optimise the model for on-device inference?

A company is using Google Cloud Vertex AI for model training. They want to automate the retraining pipeline when new data arrives in BigQuery. Which Vertex AI feature should they use?

A data scientist is training a multi-class classifier with 10 classes. The training log shows the above output for the first two epochs. What is the most likely cause?

A company uses an AI system to screen job applicants. Under the GDPR, if the system makes automated decisions that have a legal or similarly significant effect on individuals, the data subject has the right to obtain an explanation of the decision. What is this right commonly called?

A company deployed an AI chatbot that started generating offensive responses after a data update. The security team needs to quickly mitigate the issue. What should they do first?

Which TWO of the following are key stages in the AI lifecycle?

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.)

An AI system used for hiring is found to have a disparate impact on a protected group. What is the first step in addressing this under the NIST AI RMF?

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?

An AIOps platform monitors server metrics and triggers alerts. The team notices too many false positives. Which adjustment should be made to the anomaly detection model?

A healthcare AI system used for diagnosis shows a significant accuracy difference between demographic groups. Which technique should be applied to directly reduce this bias during model training?

A machine learning engineer needs to train a deep neural network on a large image dataset. Which hardware component is specifically optimized for this task due to its high parallel processing capability and is commonly used in AI training?

A financial services company is developing an AI model to detect fraudulent transactions. The dataset contains 99.9% legitimate transactions and 0.1% fraudulent ones. Which technique should the data scientist use to address the class imbalance problem?

Refer to the exhibit. A data scientist reviews the pipeline and notes that the model performance degraded. Which change to the pipeline would most likely improve model performance?

A team is deploying a BERT-based question-answering model using a REST API endpoint with gRPC for internal microservices. They notice high latency for small payloads. Which optimization is MOST likely to reduce latency?

A company deployed a machine learning model on a cloud inference service. Users report high latency during peak hours. The model is deployed on a single instance. Which action should the team take to reduce latency without significant architectural changes?

An organization wants to ensure its AI systems comply with new regulations requiring explanations for automated decisions. Which governance practice is most directly relevant?

A data engineer discovers that a dataset contains duplicate rows. Which data cleaning step is MOST appropriate?

A company is required to disclose that content has been generated or significantly modified by AI. Which practice directly addresses this transparency obligation?

A team deploys a real-time fraud detection model on a streaming platform. The model must produce predictions within 100 milliseconds per event. Initial latency is 150 ms. Which optimization is most likely to meet the latency requirement?

Refer to the exhibit. A system administrator reviews the deployment. Which action should be taken to meet the SLA?

A team is building a document intelligence application that extracts key fields from invoices. They have 10,000 labeled invoices. What is the first step in the AI project lifecycle?

Which TWO of the following are common techniques to reduce overfitting in a neural network?

A hospital uses an AI system to prioritize patient treatment. They want to ensure fairness across demographic groups. Which TWO fairness metrics should they apply to evaluate the model?

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?

A security analyst discovers that an attacker has been querying a production LLM API with thousands of carefully crafted prompts and using the responses to build a local copy of the model. Which attack is occurring?

Which TWO techniques are commonly used for feature selection in machine learning? (Choose 2)

An AI application needs to generate structured JSON output from an LLM. The development team wants to ensure the output always conforms to a specific schema. Which prompt engineering technique is MOST suitable?

An organization uses an AI-based hiring tool. To prevent bias, they want to ensure the model's decisions are explainable. Which approach is most suitable?

A company uses a third-party pre-trained language model for a sentiment analysis API. They want to ensure the model has not been backdoored. Which supply chain security practice is MOST effective?

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?

Which THREE of the following are key considerations when deploying an AI model in a production environment?

A hospital uses an AI system to predict patient deterioration from vital signs. The system currently uses a logistic regression model trained on data from the past year. Recently, the hospital adopted a new patient monitoring device that provides more accurate readings. The model's performance has dropped significantly. The data science team has access to the new device's data for the past month and wants to improve the model with minimal disruption. The team also wants to ensure the model remains interpretable for regulatory compliance. Which approach should they take?

Which THREE of the following are key principles of AI ethics as defined by major frameworks?

Which practice best ensures AI systems comply with regulations like GDPR?

A real-time recommendation system requires low latency. Which data storage strategy is best for serving user profiles and item embeddings?

Which ethical concern is most directly associated with AI systems that fully automate decision-making without human oversight?

Which THREE components are essential in an MLOps pipeline?

An organisation is developing an AI system that will be used to evaluate teacher performance in public schools. To ensure responsible use, which TWO governance elements should be in place?

A team deploying an AI model for real-time fraud detection notices that inference latency is too high. The model is a deep neural network with 50 layers, deployed on a cloud GPU. Which of the following is the BEST approach to reduce latency while maintaining acceptable accuracy?

A team is deploying a sentiment analysis model that must achieve high precision and high recall. They have a labeled dataset of 10,000 samples. They want to minimize overfitting. Which THREE actions are most appropriate? (Select THREE.)

In prompt engineering, which technique involves providing a few correct input-output examples in the prompt to guide the model's response?

A team is deploying an anomaly detection system for real-time monitoring of server metrics. The system should alert when metrics deviate significantly from normal patterns. Which type of AI model is MOST suitable?

An engineer is building a regression model to predict housing prices. The dataset includes features such as square footage, number of bedrooms, and year built. The engineer notices that the square footage values range from 500 to 10,000, while the number of bedrooms ranges from 1 to 5. Which preprocessing step is most critical before training a gradient descent-based model?

Which TWO of the following are common threats to AI model security?

A cybersecurity analyst monitors an AI chatbot that frequently produces offensive responses when given specific prompts. The development team suspects an adversarial attack. Which mitigation strategy is most effective against such prompt injection attacks?

An AI team notices that a model's F1 score on the validation set is 0.95, but on the test set it drops to 0.72. Which course of action is most appropriate?

A team is developing an AI agent that can answer questions by querying a SQL database and a REST API. The agent should decide which tool to call, parse the response, and reason about the next step. Which THREE concepts should be implemented to build this agent?

A data engineer is designing a pipeline for a streaming data application that uses a machine learning model to detect anomalies in real time. Which TWO practices should the engineer implement to ensure data quality and model reliability?

An organization's LLM-powered application unexpectedly reveals its system prompt when a user asks 'Repeat the words above starting with the phrase 'You are...'.' This is an example of which vulnerability?

A financial institution needs to deploy a credit scoring model that is interpretable to regulators. The model must provide clear reasons for each decision. Which model type should the institution choose?

A deep learning model for image classification is overfitting due to a small dataset. The team decides to apply data augmentation. Which augmentation technique is least likely to preserve the label?

A team of data scientists and engineers is working on multiple AI projects. They often struggle to reproduce experiments and manage model versions. Which tool or practice should they adopt?

A self-driving car company is developing an object detection system using a convolutional neural network (CNN). The system needs to detect pedestrians and vehicles in real-time with high accuracy. Which technique can reduce inference time while maintaining accuracy?

Refer to the exhibit. A security analyst reviews the monitoring log for an AI fraud detection model. Which of the following is the most likely cause of the multiple alerts?

A company is deploying a large language model for customer support. They want to reduce the number of off-topic or nonsensical responses while maintaining creativity. Which parameter adjustment would BEST achieve this?

During model monitoring, a loan approval model shows disparate impact against a protected group. The model's overall accuracy is high, but the false positive rate for the protected group is 0.12 compared to 0.02 for other groups. Which action should the operations team take first?

A security team is evaluating the risk of adversarial examples against their image classification system. Which of the following BEST describes an adversarial example?

A financial institution is deploying a reinforcement learning agent to optimize stock trading decisions. The agent is trained in a simulated environment that mimics historical market data. After deployment, the agent performs well initially but then suffers large losses during a period of high volatility that was underrepresented in the training data. The team wants to make the agent more robust to such market conditions without retraining from scratch. They have a budget for additional simulation compute and access to a broader historical dataset including past crises. The agent uses a deep Q-network (DQN) architecture. Which strategy should they adopt?

A data scientist splits a dataset into training (80%) and test (20%). After training, the model achieves 95% accuracy on training and 60% on test. Which step should the data scientist take first?

An AI team is deploying a real-time document intelligence service that extracts key-value pairs from invoices. The pipeline includes an LLM that calls a function to parse structured output. Which TWO testing strategies are essential before production deployment?

A company wants to build an AI pipeline that processes streaming data from IoT sensors, performs feature engineering, trains a model incrementally, and deploys the updated model. Which data pipeline technology is BEST suited for the streaming ingestion step?

A security engineer is threat modeling an AI-based recommendation system using STRIDE. Which threat corresponds to an attacker extracting the model's training data by querying the system?

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

A machine learning model for credit card fraud detection is deployed. The model's precision is 0.95 and recall is 0.60. The business cost of missing a fraud is very high. Which of the following should the team prioritize to reduce the number of false negatives?

A data scientist needs to explain a single prediction from a complex ensemble model to a business stakeholder. Which technique generates local, interpretable explanations by perturbing input features and fitting a simple surrogate model?

A machine learning engineer is designing a pipeline to train a computer vision model using PyTorch on a large dataset stored in an S3 data lake. They need to preprocess images (resize, normalize) and stream them efficiently to GPUs. Which THREE components are essential in this pipeline? (Select THREE.)

A financial services firm deploys an AI system to screen loan applications. The model was trained on historical data that reflected biased lending practices. After deployment, a regulatory body investigates and finds that the model denies loans at a disproportionately higher rate to a protected demographic group. The firm must address this issue while maintaining compliance with fair lending laws. The Chief AI Officer proposes four possible actions. Which action is the most appropriate first step?

An AI system used for resume screening is found to consistently rank male candidates higher than female candidates with similar qualifications. The HR director wants to remediate this bias without significantly reducing model accuracy. Which technique should be applied?

A company is adopting a secure development lifecycle for its new AI product. Which THREE activities are essential for secure AI development? (Select three.)

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?

An AI team uses SageMaker Pipelines to orchestrate their ML workflow. They need to version the pipeline and track experiments across runs. Which complementary MLflow feature should they integrate?

A company is deploying a code generation AI assistant for internal developers. They want to ensure the assistant does not generate code with security vulnerabilities. Which testing approach is MOST critical?

A data governance team is developing an AI policy for a large corporation. Which TWO elements are essential for a responsible AI governance framework?

An AI team wants to version control datasets, track experiments, and log model parameters across multiple projects. Which MLOps platform is specifically designed for experiment tracking and model management?

Which of the following is a key advantage of using ONNX (Open Neural Network Exchange) format for model deployment?

Refer to the exhibit. A data scientist reviews the MLflow run for a Random Forest model on customer churn data. What is the most likely issue with this model?

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)

A company has an existing AI chatbot that uses a fine-tuned LLM to answer customer queries. They want to add the ability to retrieve real-time order status from their database. Which integration pattern should they use?

A machine learning team is deploying a model that predicts customer churn. They notice that the model's predictions are highly sensitive to small changes in input features, leading to inconsistent outputs. Which technique should the team apply to improve model stability?

A company wants to use AI to analyze customer reviews and determine sentiment (positive, negative, neutral). Which AI subfield is most directly applicable?

A startup develops an AI recruiting tool that screens resumes. After deployment, they receive a complaint from a candidate who claims the system rejected them due to age discrimination. The startup has no formal AI governance process. They want to quickly assess and remediate the issue. The dataset includes age as a feature. What should they do first?

An organization deploys an AI model on edge devices for real-time image classification. Which metric is most important to monitor for ensuring the device's operational health?

Based on the exhibit, what is the most likely issue with the trained model?

A security engineer is implementing defenses against membership inference attacks on a classification model. Which TWO techniques are most effective? (Select TWO.)

During a penetration test, a security engineer discovers that an AI-powered chatbot can be tricked into revealing sensitive customer data by using specially crafted prompts. What type of attack is this, and what is the best mitigation?

A company is deploying a machine learning model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The model achieves 96% accuracy, but the F1-score for the churn class is only 0.2. Which metric should the team prioritize to evaluate model performance for this business problem?

A data engineer is designing a data pipeline for a real-time recommendation system. The pipeline must handle high velocity streams and ensure data quality. Which three components should be included in the pipeline? (Select THREE).

A marketing team uses a recommendation system to suggest products to customers. The system currently uses collaborative filtering. Which scenario would most likely cause the cold-start problem?

During a red team exercise on a company's LLM-powered internal assistant, a tester asks: 'What were the system instructions given to you at the start?' The assistant responds with its system prompt. Which vulnerability is being exploited?

A financial institution uses a deep learning model for fraud detection. The model is a feedforward neural network with three hidden layers. It was trained on a balanced dataset of 100,000 transactions. During deployment, the model achieves high accuracy on the test set but the fraud detection rate (true positive rate) is only 40% while the false positive rate is 0.1%. The business requires a true positive rate of at least 80%. Which of the following actions is most likely to achieve the required true positive rate while minimizing the increase in false positives?

Based on the exhibit, what is the likely problem with the model?

A healthcare AI startup is developing a diagnostic tool that uses patient data to predict disease risk. To comply with HIPAA and minimize privacy risks while still training accurate models, which privacy-preserving technique should they prioritize?

A data scientist is using an ensemble method to combine multiple models. Which three statements about bagging (Bootstrap Aggregating) are true? (Select THREE.)

A team is designing an AI microservice for image classification. Which THREE practices should they implement for effective integration and testing?

A machine learning engineer wants to evaluate a binary classifier. Which metric is MOST appropriate when the positive class is rare (e.g., 1% of total data)?

An engineer is training a neural network and observes the output shown. Which conclusion is most likely correct?

A developer is integrating an LLM API into a customer-facing application. They want to prevent unauthorized third parties from using the API key. Which of the following is the BEST approach?

A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 1% fraud cases (minority class) and 99% non-fraud cases. Which data preparation technique is MOST appropriate to address the class imbalance before training?

A team is implementing a document intelligence solution to extract key-value pairs from invoices. They plan to use a pre-trained vision-language model with a RAG pipeline that indexes invoice images. Which chunking strategy is BEST suited for invoice documents that have a consistent layout but vary in length?

Which TWO techniques are commonly used to handle missing data in a dataset?

A machine learning engineer notices that the gradient values in a deep network are becoming extremely small during backpropagation. What is this problem?

A company must deploy a new model version with zero downtime. The current model is served via a REST API on a Kubernetes cluster. Which deployment strategy should the team use to gradually shift traffic to the new version while monitoring for errors?

An AI system performs anomaly detection on sensor data in a manufacturing plant. The model is deployed and running well. After two months, the plant installs new sensors that produce data with a different distribution. The anomaly detection starts failing with many false positives. Which action should the team take?

A team is building a recommendation system for an e-commerce platform. They need to update recommendations in real-time as users browse. Which integration pattern is MOST suitable?

Which THREE factors are common causes of bias in AI systems?

Which TWO practices are most effective for ensuring the security of an AI model against adversarial attacks?

Which similarity search metric is BEST for comparing dense vector embeddings when the magnitude of the vectors is not important, only the direction?

An organisation is deploying an AI system for credit scoring, which is considered high-risk under the EU AI Act. Which requirement is NOT typically mandated for high-risk systems?

An organization is implementing an AI-powered chatbot for customer service. The chatbot must comply with GDPR and handle data subject access requests (DSARs). Which design approach best ensures compliance?

A data scientist is training a deep learning model for image classification. The training loss decreases steadily but the validation loss starts increasing after 10 epochs. Which technique should the scientist apply to address this issue?

A company is developing an AI chatbot for customer service. The legal team is concerned that the chatbot might generate responses that violate privacy regulations. Which governance mechanism should be implemented to mitigate this risk?

During model deployment, a data engineer notices that the model's predictions are consistently lower than expected due to a shift in the distribution of one feature between training and production. Which technique should be used to detect and quantify this shift?

During an audit of an AI system, the auditor requests documentation on the model's intended use, performance metrics, and limitations. Which tool is designed to provide this information in a standardized format?

Which THREE of the following are key principles of trustworthy AI as defined by major regulatory bodies?

A company needs to store large volumes of unstructured data (PDFs, images, logs) for future AI model training. The data must be easily accessible by data scientists using Spark and must support cost-effective storage. Which data infrastructure is MOST appropriate?

A team is deploying a fine-tuned LLM for generating code snippets. They want to test the system thoroughly before production. Which THREE testing types should they include in their test plan? (Select THREE)

Which TWO of the following are effective techniques to detect data poisoning attacks in a training dataset?

An organization is deploying an AI agent that uses the ReAct pattern to answer customer queries by calling external APIs. Which THREE components are essential in this agentic workflow?

Which hardware accelerator is specifically designed by Google for training and inference of machine learning models, particularly their TensorFlow framework?

An ML team uses Kubeflow to orchestrate a pipeline that includes data preprocessing, model training, and evaluation. The pipeline runs on a Kubernetes cluster. After a cluster upgrade, the pipeline fails at the training step with an 'OOMKilled' error. What is the MOST likely cause?

An AI developer is selecting a model architecture for a real-time video surveillance system that must detect objects in each frame and also track movement patterns across frames. Which TWO architectures should the developer combine? (Choose 2)

A company is implementing an AI solution for fraud detection. The dataset is highly imbalanced (only 1% fraudulent transactions). Which THREE techniques are most appropriate to address class imbalance? (Select three.)

An autonomous vehicle system uses a deep reinforcement learning agent to navigate. The agent's reward function gives +1 for reaching the destination and -0.1 for each time step. After training, the agent learns to circle the block repeatedly without reaching the destination. Which modification is most likely to fix this behavior?

Which similarity measure is commonly used in vector search to find the angle between vectors, making it well-suited for high-dimensional embeddings?

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)

A company uses an AI system to recommend products. The recommendation accuracy is high, but users complain about lack of diversity. Which strategy should the team adopt to improve diversity without significantly sacrificing accuracy?

A data engineer is building a pipeline to process streaming clickstream data and feed it into a real-time ML feature store. Which tool is BEST suited for the streaming ingestion?

A real-time recommendation system uses a model retrained daily. The operations team notices that click-through rate drops sharply at 8 AM each day and recovers by noon. The retraining job runs at midnight. What is the most likely cause?

A company is deploying a text generation model for customer service emails. They want to ensure the model's responses are factual and based on internal knowledge bases. Which technique is most effective?

A team is training a neural network for image classification. They observe that training loss decreases steadily but validation loss starts increasing after 20 epochs. What is the most likely issue?

A developer is building a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model they use produces normalized vectors (unit vectors). Which similarity metric is equivalent to cosine similarity in this case?

Which THREE are common machine learning algorithms used for regression?

Which component in a RAG system is responsible for converting document chunks into numerical representations that enable similarity search?

A developer is building a mobile app that uses a pre-trained image classification model on-device. Which framework should they use to run the model on iOS devices?

A company is deploying an AI system that screens job applications. According to the EU AI Act, this system is likely classified as high-risk because it affects employment opportunities. Which requirement must the company implement for high-risk AI systems?

During a penetration test, a security analyst crafts a prompt that tricks an LLM into revealing its system prompt. Which OWASP LLM Top 10 vulnerability does this exploit?

A security team is threat modeling an AI-powered recommendation system. Using STRIDE, which THREE threats are MOST relevant to the model's training data pipeline?

A company is preparing a dataset for training a supervised machine learning model. The dataset contains missing values, outliers, and categorical features. Which two preprocessing steps are typically performed to prepare the data? (Choose two.)

A company implements an AI-based chatbot for customer service. After deployment, customers report that the chatbot sometimes uses offensive language. The development team reviews the training data and finds no explicit offensive content. What is the most likely explanation?

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

A model trained on customer reviews achieves 98% accuracy on the test set. However, when deployed, it performs poorly on real-world data. The data scientist suspects distribution shift. Which action is MOST important to address this?

An AI team is deploying a fine-tuned LLM for a code generation assistant. They need to ensure the model outputs only syntactically valid JSON for integration with downstream systems. Which prompt engineering technique is MOST effective for enforcing structured output?

A security team is threat modeling an AI system that recommends financial products. They want to analyze threats unique to the ML pipeline using STRIDE. Which threat is LEAST applicable to the data collection and preprocessing stage?

An AI engineer is designing a system to detect unusual patterns in network traffic that may indicate a security breach. The system should learn from normal traffic patterns and flag deviations. Which machine learning approach is MOST appropriate?

In the AI project lifecycle, after a model is trained and evaluated, it is deployed to a production environment. What is the NEXT critical step to ensure the model continues to perform well over time?

An organization is evaluating a third-party large language model to integrate into their customer-facing application. As part of supply chain security, which THREE steps should they take to vet the model before deployment?

An AI team is developing a model that approves loan applications. The dataset contains historical loan decisions where a protected group was disproportionately denied loans. The team wants to ensure the model does not perpetuate this bias. Which fairness metric should be used during validation to directly measure whether the model's positive prediction rate is equal across groups?

An AI developer is building an agent that can book flights and hotels by calling external APIs. The agent needs to decide which API to call and in what order based on user requests. Which pattern is BEST suited for this multi-step reasoning and tool use?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A company deployed a chatbot using a pre-trained language model. Users report that the chatbot provides incorrect answers to domain-specific questions. Which approach should the AI team prioritize to improve accuracy without retraining the entire model?

A startup wants to identify unusual patterns in network traffic to detect potential security breaches. They have a large dataset of normal traffic but very few labeled attacks. Which machine learning approach is MOST suitable?

An AI system that can perform any intellectual task that a human being can is referred to as:

Which THREE of the following are best practices for preventing overfitting in deep learning models?

A healthcare organization uses an AI model to predict patient readmission risk. To comply with patient privacy regulations, they apply differential privacy during training. What is the primary trade-off of using differential privacy?

A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?

A company is developing an AI-driven recruitment tool. To comply with the EU AI Act's high-risk requirements, which TWO of the following are mandatory obligations?

An AI team is evaluating whether to use AI for a customer segmentation task. They have a dataset of customer demographics and purchase history. Which TWO conditions would make AI a better choice than a traditional rule-based approach? (Select two.)

A financial institution uses a machine learning model to approve loan applications. The model was trained on historical data that inadvertently encoded a bias against applicants from certain zip codes, leading to discriminatory lending practices. A recent audit reveals that the model's decisions are unfair, and regulators require the bank to remediate the bias without significantly reducing overall approval accuracy. The data science team has access to the training data, the model, and a set of fairness metrics. They also have a small, unbiased validation set. Which course of action should the team take to satisfy regulatory requirements?

A machine learning engineer is deploying a production model that requires strict monitoring. Which TWO monitoring strategies should be implemented to detect data drift and model degradation? (Choose TWO.)

An AI system used for resume screening is found to consistently reject female candidates for technical roles. The data science team retrains the model after removing the 'gender' feature, but the bias persists. What is the most likely cause?

A company is developing a chatbot that helps users write code. They are concerned about the chatbot being used to generate malicious code. Which defense should they implement to reduce this risk?

An organisation is deploying a fine-tuned LLM for internal use. They need to ensure the API endpoint is secure and cost-effective. Which TWO measures should they implement? (Choose 2)

A retail company wants to implement a recommendation system using collaborative filtering. The dataset contains user-item interactions (ratings) for 10,000 users and 5,000 products. The matrix is very sparse (99% missing values). The team plans to use matrix factorization to predict missing ratings. However, the training time is excessively long, and the model is not converging. The data engineer suggests using a smaller learning rate and more iterations. Which additional technique should the team apply to speed up training and improve convergence?

A data scientist is using LIME to explain a black-box model. Which TWO characteristics of LIME are true?

A national security agency uses AI to analyze surveillance data for threat detection. The system is deployed in a high-stakes environment where false negatives could lead to missed threats, and false positives waste analyst time. Recently, a known hacker group attempted to evade detection by subtly modifying their communication patterns over time, a form of adversarial evasion. The agency wants to harden the system while maintaining performance. The system uses a deep neural network. Which mitigation strategy is most appropriate?

During a red-team exercise on an AI model, testers successfully extracted training data. Which vulnerability is this?

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?

An AI system misclassifies rare but critical events. The team considers using synthetic data. Which consideration is MOST important for ensuring the synthetic data improves performance on real rare events?

A data scientist is tuning hyperparameters for a support vector machine (SVM) with an RBF kernel. Which two hyperparameters most significantly affect model performance? (Select TWO.)

Refer to the exhibit. A system administrator sees these logs from an AI inference pipeline. What is the most likely sequence of events?

During the data preparation phase of an AI project, a data scientist discovers that the target variable in a binary classification dataset is heavily imbalanced: 95% negative class and 5% positive class. Which technique should be applied to improve model performance on the minority class?

A team is building a natural language processing (NLP) model to analyze customer feedback. They have a large corpus of unlabeled text data and want to generate word embeddings that capture semantic meaning. Which approach should they use?

A data engineer is building a pipeline to ingest streaming data from IoT sensors. Which data storage solution is best suited for real-time analytics on timestamped sensor readings?

An AI governance team is implementing the NIST AI Risk Management Framework. They have identified a high-risk AI system and are in the 'Measure' function. Which activity is most appropriate for this function?

A company is deciding between fine-tuning and RAG for a domain-specific legal assistant that must provide accurate answers based on a changing set of statutes and regulations. The statutes are updated quarterly. Which approach is PREFERRED and why?

A security analyst is evaluating adversarial threats to a deployed image classifier. Which attack involves making tiny, often imperceptible changes to input images to cause misclassification?

A company uses an AI system to generate marketing images. They are concerned about copyright ownership of the generated content. According to current US copyright law, who typically owns the copyright for AI-generated work?

A team monitors a production model for bias. They measure the selection rate for two demographic groups and find a significant difference. Which TWO actions should the team take to mitigate bias? (Choose two.)

Which type of neural network is BEST suited for processing sequential data such as time series or natural language?

A team is building a recommendation system for an e-commerce platform. They want to use collaborative filtering but have a cold-start problem for new users. Which hybrid approach BEST addresses cold start while leveraging collaborative signals?

A bank uses an AI system for credit scoring. To meet fairness requirements, they want to ensure the model predicts similar outcomes for individuals who are similar with respect to the target variable, regardless of protected attributes. Which fairness metric addresses this?

A data science team is deploying a real-time fraud detection model on edge devices in retail stores. The model must infer under 10ms and fit within 50MB memory. Which combination of techniques should the team apply?

Which TWO of the following are best practices for securing an AI model against adversarial attacks?

An AI model is trained to predict loan default. The training data contains 95% non-default and 5% default. Which metric is most appropriate to evaluate model performance given the imbalanced dataset?

An organization is deploying a machine learning model that classifies loan applications. They want to prevent an attacker from reconstructing individual customer records from the model's predictions. Which type of attack should they defend against?

A data scientist is training a model to detect fraudulent transactions. To protect customer privacy, the team wants to ensure that the model does not inadvertently memorize and reveal sensitive information about individuals in the training set. Which technique should be applied during training?

A healthcare AI model is subject to GDPR because it processes data of EU patients. The model makes automated decisions about treatment plans. Under GDPR, patients have the right to obtain an explanation of the decision. Which approach BEST satisfies this 'right to explanation'?

An organization wants to implement an AI ethics board. Which composition best ensures independence and expertise?

A retail company uses a gradient boosting model to predict customer lifetime value (CLV). The model currently uses 50 features including purchase history, demographics, and web behavior. The model's RMSE on the test set is 120. The data science team wants to improve the model's accuracy without increasing training time significantly. They have access to additional data: customer support interaction logs (text), social media sentiment (text), and third-party credit scores (numeric). They also have the ability to perform feature engineering, hyperparameter tuning, and ensemble methods. Which approach is most likely to yield the best improvement in predictive performance with minimal increase in training time?

A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?

A data scientist notices the model overfits. Which change to the exhibit's configuration would most likely reduce overfitting?

Which TWO deployment strategies allow for testing a new model version before fully rolling it out?

An e-commerce company deploys a recommendation model that must serve predictions with sub-100 ms latency for millions of users during peak hours. The model is a large neural network. Which architecture is most suitable?

A team is training a image classification model. They split the dataset into training, validation, and test sets. After training, the model achieves 98% accuracy on the training set but only 72% on the test set. Which step in the AI project lifecycle should the team focus on?

A data scientist notices that a model's performance on the training set is excellent, but validation accuracy is poor. The team used the same dataset for feature engineering and model selection. What is the MOST likely cause?

A financial institution wants to deploy an AI system that automates loan approvals. Under the EU AI Act, this system would be classified as high-risk. Which of the following is a MANDATORY requirement for high-risk AI systems before market placement?

Under the GDPR, individuals have the right to not be subject to a decision based solely on automated processing if it produces legal effects. Which of the following is a typical safeguard that organisations must provide to comply with this right?

An MLOps team observes that their production inference API experiences increasing latency as more concurrent requests arrive. They need to scale horizontally while maintaining session state of preprocessing steps. Which deployment strategy should they implement?

An MLOps team automates model deployment with a CI/CD pipeline. A performance regression is detected after deploying a new model version. The team needs to automatically roll back to the previous version. Which approach best enables safe automated rollback?

A team is using a pre-trained language model for sentiment analysis. They want to adapt it to a specific domain with limited labeled data. Which approach is most efficient?

A company has developed a deep learning model for image classification. The team wants to deploy the model to production with high availability and scalability. Which approach should they use?

Which neural network architecture is specifically designed to handle sequential data and mitigate the vanishing gradient problem?