MLA-C01 exam

You are an ML Engineer working for a logistics company that uses multiple machine learning models to optimize delivery routes in real-time. Each model needs to process data quickly to provide up-to-the-minute route adjustments, but the company also has strict cost constraints. You need to deploy the models in an...

You are a data scientist at a financial services company tasked with deploying a lightweight machine learning model that predicts creditworthiness based on a customer’s transaction history. The model needs to provide real-time predictions with minimal latency, and the traffic pattern is unpredictable, with occasional spikes during business hours. The...

Which benefits might persuade a developer to choose a transparent and explainable machine learning model? (Select two)A . They foster trust and confidence in model predictionsB . They enhance security by concealing model logicC . They simplify the integration process with other systemsD . They facilitate easier debugging and optimizationE...

Which of the following strategies best aligns with the defense-in-depth security approach for generative AI applications on AWS?A . Relying solely on data encryption to protect the AI training dataB . Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilitiesC . Using...

You are a machine learning engineer at a biotech company developing a custom deep learning model for analyzing genomic data. The model relies on a specific version of TensorFlow with custom Python libraries and dependencies that are not available in the standard SageMaker environments. To ensure compatibility and flexibility, you...

A company uses a generative model to analyze animal images in the training dataset to record variables like different ear shapes, eye shapes, tail features, and skin patterns. Which of the following tasks can the generative model perform?A . The model can classify multiple species of animals such as cats,...

You are a machine learning engineer at a fintech company tasked with developing and deploying an end-to-end machine learning workflow for fraud detection. The workflow involves multiple steps, including data extraction, preprocessing, feature engineering, model training, hyperparameter tuning, and deployment. The company requires the solution to be scalable, support complex...

You are tasked with building a predictive model for customer lifetime value (CLV) using Amazon SageMaker. Given the complexity of the model, it’s crucial to optimize hyperparameters to achieve the best possible performance. You decide to use SageMaker’s automatic model tuning (hyperparameter optimization) with Random Search strategy to fine-tune the...

You are a data scientist at a retail company responsible for deploying a machine learning model that predicts customer purchase behavior. The model needs to serve real-time predictions with low latency to support the company’s recommendation engine on its e-commerce platform. The deployment solution must also be scalable to handle...

You are a data scientist at a financial technology company developing a fraud detection system. The system needs to identify fraudulent transactions in real-time based on patterns in transaction data, including amounts, locations, times, and account histories. The dataset is large and highly imbalanced, with only a small percentage of...