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NEW QUESTION # 237
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?
Answer: A
NEW QUESTION # 238
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
This solution is the most efficient and involves the least operational overhead:
Amazon Kinesis data streams efficiently handle real-time ingestion of high-volume streaming data.
Amazon Managed Service for Apache Flink provides a fully managed environment for stream processing with built-in support for RANDOM_CUT_FOREST, an algorithm designed for anomaly detection in real- time streaming data.
This approach eliminates the need for deploying and managing additional infrastructure like SageMaker endpoints, Lambda functions, or external tools, making it the most scalable and operationally simple solution.
NEW QUESTION # 239
An ML engineer needs to deploy a trained model based on a genetic algorithm. Predictions can take several minutes, and requests can include up to 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
SageMaker Asynchronous Inference is designed for long-running inference workloads and large payloads (up to 1 GB). Requests are queued, processed asynchronously, and results are written to Amazon S3.
Real-time endpoints have payload and timeout limits. EC2 and ECS require infrastructure management, increasing operational overhead.
AWS documentation explicitly recommends asynchronous inference for workloads with large inputs and long execution times.
Therefore, Option C is the correct and most efficient solution.
NEW QUESTION # 240
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 must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?
Answer: C
Explanation:
To implement a manual approval-based workflow ensuring that only approved models are deployed to production endpoints, Amazon SageMaker provides integrated tools such as SageMaker Pipelines and the SageMaker Model Registry.
SageMaker Pipelines is a robust service for building, automating, and managing end-to-end machine learning workflows. It facilitates the orchestration of various steps in the ML lifecycle, including data preprocessing, model training, evaluation, and deployment. By integrating with the SageMaker Model Registry, it enables seamless tracking and management of model versions and their approval statuses.
Implementation Steps:
Define the Pipeline:
Create a SageMaker Pipeline encompassing steps for data preprocessing, model training, evaluation, and registration of the model in the Model Registry.
Incorporate a Condition Step to assess model performance metrics. If the model meets predefined criteria, proceed to the next step; otherwise, halt the process.
Register the Model:
Utilize the RegisterModel step to add the trained model to the Model Registry.
Set the ModelApprovalStatus parameter to PendingManualApproval during registration. This status indicates that the model awaits manual review before deployment.
Manual Approval Process:
Notify the designated approver upon model registration. This can be achieved by integrating Amazon EventBridge to monitor registration events and trigger notifications via AWS Lambda functions.
The approver reviews the model ' s performance and, if satisfactory, updates the model ' s status to Approved using the AWS SDK or through the SageMaker Studio interface.
Deploy the Approved Model:
Configure the pipeline to automatically deploy models with an Approved status to the production endpoint.
This can be managed by adding deployment steps conditioned on the model ' s approval status.
Advantages of This Approach:
Automated Workflow: SageMaker Pipelines streamline the ML workflow, reducing manual interventions and potential errors.
Governance and Compliance: The manual approval step ensures that only thoroughly evaluated models are deployed, aligning with organizational standards.
Scalability: The solution supports complex ML workflows, making it adaptable to various project requirements.
By implementing this solution, the company can establish a controlled and efficient process for deploying models, ensuring that only approved versions reach production environments.
References:
Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines Update the Approval Status of a Model - Amazon SageMaker
NEW QUESTION # 241
A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns.
Which solution will meet these requirements?
Answer: C
Explanation:
The primary requirement is dimensionality reduction on high-dimensional structured data to uncover hidden patterns. Principal Component Analysis (PCA) is a linear dimensionality reduction technique specifically designed for this purpose and is available as a built-in algorithm in Amazon SageMaker.
PCA transforms the original features into a smaller set of orthogonal components that preserve the maximum possible variance. This makes PCA ideal for large tabular datasets such as census data, where hundreds of correlated variables are common.
t-SNE (Option B) is mainly used for visualization in very low dimensions (2D or 3D) and does not scale well for large datasets or production analysis. k-means (Option C) is a clustering algorithm, not a dimensionality reduction method. Random Cut Forest (Option D) is used for anomaly detection.
Therefore, PCA is the correct and AWS-recommended solution.
NEW QUESTION # 242
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