MLA-C01 — questions

A recommendation model uses ML and calls an Amazon SageMaker AI endpoint to get recommendations. An ML engineer must ensure that the model stays available during an expected increase in user traffic. Which solution will meet these requirements?

An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary features. The ML engineer must reduce the overfitting and the impact of the unnecessary features. Which solution will meet these requirements?

An ML engineer wants to use Amazon SageMaker Data Wrangler to perform preprocessing on a dataset. The ML engineer wants to use the processed dataset to train a classification model. During preprocessing, the ML engineer notices that a text feature has a range of thousands of values that differ only by spelling errors. The ML engineer needs to apply an encoding method so that after preprocessing is complete, the text feature can be used to train the model. Which solution will meet these requirements?

An ML engineer is training a text generation model on Amazon SageMaker AI. After several epochs, the loss function does not converge, and the model’s accuracy on the validation dataset starts to show oscillating results. The ML engineer needs to ensure that the model achieves generalization. Which solution will meet this requirement?

A company uses an NFS-based data store to store data for ML training. Linux-based systems access the data store. The company needs a hybrid system to make the shared data store accessible to on-premises servers and Amazon SageMaker AI notebooks that will consume the data. File locking is required for the data producers. Which AWS storage solution will meet these requirements?

A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns. The company needs a no-code solution to transform the data. The solution must store the transformed data back to the same S3 bucket for model training. Which solution will meet these requirements?

A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible on AWS. Which feature of SageMaker AI should the company use to meet these requirements?

A company uses an Amazon QuickSight dashboard to track the sale prices of sneakers over time. The dashboard aggregates sale prices scraped from many retail websites. The company wants to determine which prices are unusually high outliers and to display the outliers visually. Which solution will meet these requirements?

An ML engineer is using Amazon QuickSight anomaly detection to detect very high or very low machine operating temperatures compared to normal. The ML engineer sets the Severity parameter to Low and above. The ML engineer sets the Direction parameter to All. What effect will the ML engineer observe in the anomaly detection results if the ML engineer changes the Direction parameter to Lower than expected?

A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs from its own standalone Docker image. The company needs to perform a lift and shift from the on-premises Kubernetes cluster to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Which solution will meet this requirement with the LEAST operational overhead?

A retail company is creating an AI-powered assistant for customers. The company has a large body of documentation that the assistant needs to use for general inquiries. The company wants any responses about prices to use only documentation that is less than 1 month old. Which solution will meet these requirements?

A travel company wants to create an ML model to recommend the next airport destination for its users. The company has collected millions of data records about user location, recent search history on the company’s website, and 2,000 available airports. The data has several categorical features with a target column that is expected to have a high-dimensional sparse matrix. The company needs to use Amazon SageMaker AI built-in algorithms for the model. An ML engineer converts the categorical features by using one-hot encoding. Which algorithm should the ML engineer implement to meet these requirements?

An ML engineer is configuring auto scaling for an inference component of a model that runs behind an Amazon SageMaker AI endpoint. The ML engineer configures SageMaker AI auto scaling with a target tracking scaling policy set to 100 invocations per model per minute. The SageMaker AI endpoint scales appropriately during normal business hours. However, the ML engineer notices that at the start of each business day, there are zero instances available to handle requests, which causes delays in processing. The ML engineer must ensure that the SageMaker AI endpoint can handle incoming requests at the start of each business day. Which solution will meet this requirement?

An ML engineer is using an Amazon SageMaker Studio notebook to train a neural network by creating an estimator. The estimator runs a Python training script that uses Distributed Data Parallel (DDP) on a single instance that has more than one GPU. The ML engineer discovers that the training script is underutilizing GPU resources. The ML engineer must identify the point in the training script where resource utilization can be optimized. Which solution will meet this requirement?

An ML engineer at an insurance company trains a regression model to predict the number of insurance policy sales each month. After training the model, the ML engineer uses Amazon SageMaker AI to deploy the model for inference. The ML engineer wants to monitor the model predictions to detect whether the production data distribution differs from the training data distribution when there are changes in customer behaviors. Which solution will meet these requirements?

A company is developing an internal cost-estimation tool that uses an ML model in Amazon SageMaker AI. Users upload high-resolution images to the tool. The model must process each image and predict the cost of the object in the image. The model also must notify the user when processing is complete. Which solution will meet these requirements?

A healthcare company uses an Amazon SageMaker AI endpoint to host a model that predicts patient readmission risk to hospitals. The company wants to predict patient readmissions with high accuracy and is willing to tolerate false positives. The current model performance has degraded over the previous year. The company trains and deploys a new model as a shadow variant for testing on live traffic from hospitals. The company monitors the performance of the new model for a month. During the month of testing, the shadow variant has a higher recall than the existing model but has a lower precision. What should the company do next?

An airline company uses an ML model to adjust ticket prices based on demand. The model runs on Amazon SageMaker real-time endpoints. During previous deployments, the model failed to scale quickly enough when website traffic increased, which caused delays in price adjustments. An ML engineer needs to configure auto scaling for the SageMaker endpoints to respond rapidly to traffic changes. The solution must use target tracking scaling policies. Which configuration will be MOST responsive to sudden changes in traffic?

An ML engineer is collecting data to train a classification ML model by using Amazon SageMaker AI. The target column can have two possible values: Class A or Class B. The ML engineer wants to ensure that the number of samples for both Class A and Class B are balanced, without losing any existing training data. The ML engineer must test the balance of the training data. Which solution will meet this requirement?

An ML engineer is building a logistic regression model to predict customer churn for subscription services. The ML engineer is using a dataset that contains two string variables: location and job_seniority_level. The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values. The ML engineer must perform preprocessing on the variables. Which solution will meet this requirement?