D. Train models using Amazon EMR for cost efficiency, deploy the models using AWS Lambda for serverless inference, and manually monitor the system using CloudWatch to reduce operational overhead Explanation: Correct option: Use Amazon SageMaker for both training and deployment, leverage auto -scaling endpoints for real-time inference, and apply SageMaker Pipelines for orchestrating end-to-end ML workflows, ensuring scalability and automation Amazon SageMaker provides a managed service for both training and deployment, which simplifies the infrastructure and reduces operational overhead. Auto -scaling endpoints in SageMaker ensure the system can handle increasing demand without manual intervention. SageMaker Pipelines automates the entire ML workflow, enabling continuous integration and delivery (CI/CD) practices, making the infrastructure scalable, maintainable, and cost -effective. Incorrect options: Implement Amazon SageMaker for model training, deploy the models using Amazon EC2 with manual scaling to handle inference, and use AWS CloudFormation for managing infrastructure as code to ensure repeatability - Using Amazon SageMaker for training and Amazon EC2 for inference with manual scaling can work, but it requires more effort to manage scaling, and manually managing infrastructure is less maintainable. Auto-scaling and automation would be more effective for a growing startup. Train models using Amazon EMR for cost efficiency, deploy the models using AWS Lambda for serverless inference, and manually monitor the system using CloudWatch to reduce operational overhead - While Amazon EMR is cost-effective for big data processing, it’s not optimized for ML model training in the same way that SageMaker is. AWS Lambda is useful for serverless inference but may not scale effectively for high -volume, real -time recommendations. Manual monitoring adds operational overhead. Use Amazon SageMaker for training, deploy models on Amazon ECS for flexible scaling, and implement infrastructure monitoring with a combination of CloudWatch and AWS Systems Manager to ensure maintainability - Amazon ECS offers flexible scaling, but SageMaker’s auto - scaling capabilities and built-in integration with ML workflows make it more suitable for this use case. Additionally, SageMaker Pipelines offers better orchestration for ML tasks compared to a manually managed solution. References: https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint -auto-scaling -prerequisites.html https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html You are working on a machine learning project for a financial services company, developing a model to predict credit risk. After deploying the initial version of the model using Amazon SageMaker, you find that its performance, measured by the AUC (Area Under the Curve), is not meeting the company’s accuracy requirements. Your team has gathered more data and believes that the model can be further optimized. You are considering various methods to improve the model’s performance, including feature engineering, hyperparameter tuning, and trying different algorithms. However, given the limited time and computational resources, you need to prioritize the most impactful strategies. Which of the following approaches are the MOST LIKELY to lead to a significant improvement in model performance? (Select two)
SageMaker Debugger provides tools to debug training jobs and resolve such problems to improve the performance of your model. Debugger also offers tools to send alerts when training anomalies are found, take actions against the problems, and identify the root cause of them by visualizing collected metrics and tensors. SageMaker Debugger: via - https://docs.aws.amazon.com/sagemaker/latest/dg/train -debugger.html Incorrect options: Increase the size of the training dataset by incorporating synthetic data and then retrain the existing model - Increasing the size of the dataset with synthetic data can improve model performance, but it also introduces the risk of adding noise or bias if the synthetic data is not carefully generated. This approach may not guarantee a significant performance boost unless the original dataset was severely lacking in size. Switch to a more complex algorithm, such as deep learning, and use transfer learning to leverage pre -trained models - Switching to a more complex algorithm or using transfer learning could improve performance, but it also increases the risk of overfitting, especially if the new algorithm is not well suited to the data. Additionally, deep learning models require more data and tuning, which may not be feasible given the time and resource constraints. Perform hyperparameter tuning using Bayesian optimization and increase the number of trials to explore a broader search space - Hyperparameter tuning, especially using Bayesian optimization, can help optimize the model’s performance, but the gains might be marginal if the underlying features are not informative. It’s a valuable approach, but may not be the most impactful first step. References: https://docs.aws.amazon.com/sagemaker/latest/dg/train -debugger.html https://aws.amazon.com/sagemaker/clarify/ You are a data scientist at a healthcare company developing a machine learning model to analyze medical imaging data, such as X-rays and MRIs, for disease detection. The dataset consists of 10 million high-resolution images stored in Amazon S3, amounting to several terabytes of data. The training process requires processing these images efficiently to avoid delays due to I/O bottlenecks, and you must ensure that the chosen data access method aligns with the large dataset size and the high throughput requirements of the model. Given the size and nature of the dataset, which SageMaker input mode and AWS Cloud Storage configuration is the MOST SUITABLE for this use case?
Incorrect options: Use the File input mode to download the entire dataset from Amazon S3 to the training instances' local storage before starting the training process, ensuring that all data is available locally during training - The File input mode downloads the entire dataset to the training instance before starting the training job. For a dataset as large as yours, this would lead to significant delays and require large amounts of local storage, which is not optimal for efficiency or cost. Implement the FastFile input mode with FSx for Lustre, to enable on -demand streaming of data chunks from Amazon S3 with low latency and high throughput - FastFile mode is useful for scenarios where you need rapid access to data with low latency, but it is best suited for workloads with many small files. You should note that FastFile mode can be used only while accessing data from Amazon S3 and not with Amazon FSx for Lustre. So, this option acts as a distractor. Use the File input mode with EFS (Amazon Elastic File System) to mount the dataset across multiple instances, ensuring data is shared and accessible during distributed training - Using Amazon EFS for the given use case requires transferring the medical imaging data from Amazon S3 into Amazon EFS, which leads to unnecessary data transfer as well as data storage costs. So, this option is ruled out. References: https://docs.aws.amazon.com/sagemaker/latest/dg/model -access -training -data.html https://aws.amazon.com/about -aws/whats -new/2021/10/amazon -sagemaker -fast-file-mode/ You are working as a machine learning engineer for a startup that provides image recognition services. The service is currently in its beta phase, and the company expects varying levels of traffic, with some days having very few requests and other days experiencing sudden spikes. The company wants to minimize costs during low -traffic periods while still being able to handle large, infrequent spikes of requests efficiently. Given these requirements, you are considering using Amazon SageMaker for your deployment. Which of the following statements is the BEST recommendation for the given scenario?
via - https://docs.aws.amazon.com/sagemaker/latest/dg/serverless -endpoints.html Incorrect options: Use Amazon SageMaker Asynchronous Inference that minimizes costs during low -traffic periods while managing large infrequent spikes of requests efficiently - Asynchronous Inference is ideal for handling large and long -running inference requests that do not require an immediate response. However, it may not be as cost-effective for handling fluctuating traffic where immediate scaling and low-latency are priorities. Use Amazon SageMaker Real -time Inference that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently - Real-time inference is ideal for inference workloads where you have real -time, interactive, low latency requirements. Use Batch transform to run inference with Amazon SageMaker that minimizes costs during low - traffic periods while managing large infrequent spikes of requests efficiently - To get predictions for an entire dataset, you can use Batch transform with Amazon SageMaker. References: https://docs.aws.amazon.com/sagemaker/latest/dg/serverless -endpoints.html https://docs.aws.amazon.com/sagemaker/latest/dg/how -it-works -deployment.html 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 environment where performance, cost, and latency are carefully balanced. There may be slight variations in the access frequency of the models. Any excessive costs could impact the project’s profitability. Which of the following strategies should you consider to balance the tradeoffs between performance, cost, and latency when deploying your model in Amazon SageMaker? (Select two)
suitable for low-latency, cost-effective deployments on devices with limited resources. In this scenario, the need for scalable, cloud -based infrastructure is more important. References: https://docs.aws.amazon.com/sagemaker/latest/dg/multi -model -endpoints.html https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint -auto-scaling.html You are a machine learning engineer at an e-commerce company that uses a recommendation model to suggest products to customers. The model was trained on data from the past year, but after being in production for several months, you notice that the model's recommendations are becoming less relevant. You suspect that either data drift or model drift could be causing the decline in performance. To investigate and resolve the issue, you need to understand the difference between these two types of drift and how to monitor them using Amazon SageMaker. Which of the following statements BEST describes the difference between data drift and model drift, and how you would address them using Amazon SageMaker?
➢ TOTAL QUESTIONS:125 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 dependencies between tasks, and provide robust monitoring and versioning capabilities. Additionally, the workflow needs to integrate seamlessly with existing AWS services. Which deployment orchestrator is the MOST SUITABLE for managing and automating your ML workflow?
