MLA-C01 — questions

A company needs to deploy a custom-trained classification ML model on AWS. The model must make near real-time predictions with low latency and must handle variable request volumes. Which solution will meet these requirements?

A company runs a neural network model and retrains the model when the performance degrades. The company uses a training job that uses Amazon SageMaker AI distributed data parallelism (DDP). The training job takes several hours to run. The company wants to decrease the required time for the training job. Which solution will meet this requirement?

An ML engineer decides to use Amazon SageMaker AI automated model tuning (AMT) for hyperparameter optimization (HPO). The ML engineer requires a tuning strategy that uses regression to slowly and sequentially select the next set of hyperparameters based on previous runs. The strategy must work across small hyperparameter ranges. Which solution will meet these requirements?

A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes. Which algorithm and hyperparameter should the company use to meet this requirement?

An ML engineer at an entertainment company is refining an ML model to predict audience preferences. As the model learns from data, the ML engineer notices that the model’s performance peaks early and then begins to gradually decline. The ML engineer must prevent the performance degradation after initial success. Which solution will meet this requirement?

An ML engineer is tuning an image classification model that shows poor performance on one of two available classes during prediction. Analysis reveals that the images whose class the model performed poorly on represent an extremely small fraction of the whole training dataset. The ML engineer must improve the model’s performance. Which solution will meet this requirement?

An ML engineer needs to develop an AI assistant by using Retrieval Augmented Generation (RAG) in Amazon Bedrock. The company stores a collection of PDF text files in an Amazon S3 bucket. The ML engineer must create an Amazon Bedrock knowledge base to process the PDF files and to store the processed files in a vector store. Which solution will meet these requirements?

An ML engineer needs to build a processing pipeline to identify and remove personally identifiable information (PII) from petabytes of unstructured data. The ML engineer will use the processed data to train ML models in Amazon SageMaker AI. Which solution will meet these requirements?

A company develops a recommendation model and hosts the model on an Amazon SageMaker AI endpoint. The model uses the SageMaker AI endpoint to perform near real-time inference to deliver personalized product recommendations to customers based on browsing history, purchase records, and in-app user interactions. After a major marketing campaign, the company observes a sharp drop in the model’s performance. The company needs a solution to proactively monitor, detect, and validate model performance before future marketing campaigns. Which solution will meet these requirements?

A company is using Amazon SageMaker AI to create a classification model to categorize the company’s sales performance for each month of the previous 20 years on a scale from 1 to 5. The dataset includes fields for month, sales region, regional aggregate sales, and the number of stores in each sales region. The company notices that during two months of every year, the aggregate sales values are unexpectedly high. The company performs one-hot encoding on all non-numerical features in the training and validation datasets. The company uses the training dataset to train the classification model. When the company evaluates the model against the validation dataset, the results are less accurate than expected. The company must improve the model’s accuracy on the validation dataset. Which solution will meet this requirement?

A company is creating an ML model to identify defects in a product. The company has gathered a dataset and has stored the dataset in TIFF format in Amazon S3. The dataset contains 200 images in which the most common defects are visible. The dataset also contains 1,800 images in which there is no defect visible. An ML engineer trains the model and notices poor performance in some classes. The ML engineer identifies a class imbalance problem in the dataset. What should the ML engineer do to solves this problem?

An ML engineer is building an ML model in Amazon SageMaker AI. The ML engineer needs to load historical data directly from Amazon S3, Amazon Athena, and Snowflake into SageMaker AI. Which solution will meet this requirement?

A company is developing an application that reads animal descriptions from user prompts and generates images based on the information from the prompts. The application reads a message from an Amazon Simple Queue Service (Amazon SQS) queue. Then the application uses Amazon Titan Image Generator on Amazon Bedrock to generate an image based on the information in the message. Finally, the application removes the message from SQS queue. Which IAM permissions should the company assign to the application’s IAM role? (Choose two.)

An ML engineering team is spread across multiple locations. When the lead ML engineer opens an Amazon SageMaker Al notebook, the ML engineer does not see the latest merged notebook made by other team members from a Git repository. The lead ML engineer must see the latest SageMaker AI notebook updates. Which solution will meet this requirement?

A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends. Which algorithm and hyperparameters should the company use to develop the model?

An ML engineer is designing an AI-powered traffic management system to adjust traffic lights during predicted congestion. The system must use near real-time inference to generate predictions to help prevent traffic collisions. The system must use a batch processing pipeline to perform historical analysis of the predictions to continuously refine and improve the model. The historical analysis will take several hours to evaluate how well the predictions correlate with actual outcomes. The system must be able to scale inference endpoints appropriately to meet demand. Which combination of solutions will meet these requirements? (Choose two.)

A company wants to launch a new website feature that predicts home prices based on user-supplied home attributes. The attributes include location, square footage, and number of bedrooms and bathrooms. An ML engineer has trained a regression model by using the Amazon SageMaker AI XGBoost algorithm. The model performs well with training data. However, the model significantly underperforms when it is validated against real-world data. Which solution will improve the model’s validation score with the LEAST implementation effort?

A company is performing A/B testing on a model that provides product recommendations. The company has deployed two versions of the model and is showing each version of the model to 50% of users randomly. Which metric should the company use to evaluate whether users act on a recommendation?

A company is using Amazon SageMaker AI to build an ML model to predict customer behavior. The company needs to explain the bias in the model to an auditor. The explanation must focus on demographics data of the customers. Which solution will meet these requirements?

A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events. Which solution will meet these requirements with the LEAST operational overhead?