A company uses Amazon SageMaker AI to create ML models. The data scientists need fine-grained control of ML workflows, DAG visualization, experiment history, and model governance for auditing and compliance.
Which solution will meet these requirements?

• A.Use AWS CodePipeline with SageMaker Studio and SageMaker ML Lineage Tracking.
• B.Use AWS CodePipeline with SageMaker Experiments.
• C.Use SageMaker Pipelines with SageMaker Studio and SageMaker ML Lineage Tracking.
• D.Use SageMaker Pipelines with SageMaker Experiments.

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An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

• A.Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
• B.Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
• C.Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
• D.Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.

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A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

• A.Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
• B.Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.
• C.Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.
• D.Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.

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A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

• A.Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
• B.Create a model group for each category. Move the existing models into these category model groups.
• C.Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
• D.Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

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A company uses AWS CodePipeline to orchestrate a continuous integration and continuous delivery (CI/CD) pipeline for ML models and applications.
Select and order the steps from the following list to describe a CI/CD process for a successful deployment.
Select each step one time. (Select and order FIVE.)
. CodePipeline deploys ML models and applications to production.
CodePipeline detects code changes and starts to build automatically.
. Human approval is provided after testing is successful.
. The company builds and deploys ML models and applications to staging servers for testing.
. The company commits code changes or new training datasets to a Git repository.

Correct Answer:

Explanation:
Step 1:
The company commits code changes or new training datasets to a Git repository.
This is the trigger point. A source code or data change initiates the CI/CD pipeline.
Step 2:
CodePipeline detects code changes and starts to build automatically.
CodePipeline monitors the Git repository (for example, AWS CodeCommit, GitHub, or Bitbucket) and automatically triggers the pipeline when changes are detected.
Step 3:
The company builds and deploys ML models and applications to staging servers for testing.
The pipeline runs build, training, and test stages (often using AWS CodeBuild and SageMaker) and deploys artifacts to a staging or test environment for validation.
Step 4:
Human approval is provided after testing is successful.
A manual approval action is a best practice for ML workflows to ensure governance, compliance, and quality checks before production deployment.
Step 5:
CodePipeline deploys ML models and applications to production.
After approval, the pipeline automatically deploys the validated model or application to the production environment.

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