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An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs to implement a solution that labels the dataset with the highest possible accuracy. Which combination of steps should the ML engineer take to meet these requirements? (Choose two.)
A. Train the deep learning model directly on the raw data. Let the model infer the labels by itself.
B. Use Amazon Mechanical Turk to complete the annotation job created by Amazon SageMaker Ground Truth.
C. Use Amazon Rekognition to automatically label the dataset.
D. Set up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth.
E. Use Amazon SageMaker Ground Truth to create an annotation job that specifies the labeling task and requirements.
Answer: D,E
Explanation:
To achieve the highest labeling accuracy with controlled employee-only access, the ML engineer should use Amazon SageMaker Ground Truth to define the annotation job and then assign it to a private workforce of employees for labeling and review. This ensures high-quality, secure labeling restricted to authorized personnel.
NEW QUESTION # 24
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.
Which solution will meet these requirements?
A. Prepare the data by using AWS Glue DataBrew.
B. Run an Amazon EMR job to change the sensitive data to random values.
C. Use Amazon Made to categorize the sensitive data.
D. Run an AWS Batch job to change the sensitive data to random values.
Answer: A
Explanation:
AWS Glue DataBrew provides an easy-to-use interface for preparing and transforming data, including masking or obfuscating sensitive information. It offers built-in data masking features, allowing the ML engineer to handle sensitive data securely while retaining its structure and meaning. This solution is efficient and requires minimal coding, making it ideal for ensuring sensitive data is masked before model building begins.
NEW QUESTION # 25
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?
A. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
B. Use the SageMaker Model Registry and model groups to catalog the models.
C. Use the SageMaker Model Registry and unique tags for each model version.
D. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
Answer: B
NEW QUESTION # 26
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format.
An ML engineer needs to convert the JSON files into a tabular format.
Which solution will meet this requirement with the LEAST operational overhead?
A. Create an AWS Glue PySpark job that uses the Relationalize transform to convert the files.
B. Write custom Scala code to convert the files. Use Amazon EMR Serverless to run the Scala code.
C. Create an AWS Lambda function that uses a Python runtime and invokes the reduce() function to convert the files. Invoke the Lambda function.
D. Create an Amazon Athena database that is based on the JSON files. Use the Athena flatten function to convert the data.
Answer: A
Explanation:
The AWS Glue PySpark Relationalize transform is purpose-built to convert nested JSON into tabular format with minimal operational overhead. It automates the flattening process without requiring custom code or complex infrastructure, making it the most efficient solution for preparing the data for XGBoost training.
NEW QUESTION # 27
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?
A. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.
B. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.
C. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross- account replication between the initial ECR repositories and the central catalog.
D. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
Answer: B
Explanation:
The Amazon SageMaker Model Registry is designed to manage and catalog ML models, including those hosted in Amazon ECR. By creating a model group for each model in the SageMaker Model Registry and setting up cross-account resource policies, the company can establish a central catalog in a new AWS account.
This allows all models from the initial accounts to be accessible in a unified, centralized manner for better organization, management, and governance. This solution leverages existing AWS services and ensures scalability and minimal operational overhead.
NEW QUESTION # 28
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