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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Deployment and Inference | 20% | - A/B testing and shadow mode deployment - SageMaker endpoints configuration - Model versioning and rollback - Model deployment strategies (real-time, batch) - Inference optimization (latency, throughput) |
| Topic 2: Data Processing | 22% | - Data preprocessing and feature engineering - Handling imbalanced data - Data ingestion and transformation - Data validation and quality assessment - Data pipelining with AWS services ( Glue, Data Brew, etc.) |
| Topic 3: Model Development | 30% | - Distributed training - Training and validation strategies - Transfer learning and fine-tuning - ML frameworks (SageMaker, built-in algorithms) - Algorithm selection and model architecture - Hyperparameter optimization |
| Topic 4: MLOps and Monitoring | 28% | - Model monitoring and drift detection - Model lineage and reproducibility - CI/CD pipelines for ML - Security and access management for ML - Incident response and remediation - Cost optimization for ML workloads |
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NEW QUESTION # 150
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.
Answer:
Explanation:
Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.Step 2: SageMaker retrains the model by using the data in the S3 bucket.Step 3: The pipeline deploys the model to a SageMaker endpoint.
* Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
* Why?The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
* How?Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
* Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
* Why?The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
* How?Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
* Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
* Why?Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
* How?Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
* An S3 event notification invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.
NEW QUESTION # 151
A company uses a batching solution to process daily analytics. The company wants to provide near real-time updates, use open-source technology, and avoid managing or scaling infrastructure.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon MSK Serverless provides a fully managed Apache Kafka-compatible service that automatically handles provisioning, scaling, and capacity management. AWS documentation states that MSK Serverless is designed for customers who want Kafka functionality without managing infrastructure.
Option B requires capacity planning and scaling management. Option C uses proprietary technology rather than open source. Option D requires full infrastructure management.
MSK Serverless delivers near real-time streaming with minimal operational overhead while maintaining compatibility with open-source Kafka tooling.
Therefore, Option A is the correct solution.
NEW QUESTION # 152
An ML engineer is training an ML model to identify people's health risk based on 20 features and
1 target. The target class has two values:
- Likely to have health risk (positive class)
- Unlikely to have health risk (negative class)
The age range of people in the dataset is 30 years old to 60 years old. Age is one of the features.
The ML engineer analyzes the features. For the positive class, the difference in proportions of labels (DPL) value is (+0.9) for the age range of 40 to 45 compared with all other age ranges.
What should the ML engineer do to correct this data imbalance?
Answer: A
Explanation:
A DPL of +0.9 indicates that the positive class is heavily overrepresented in the 40-45 age range compared to other age ranges. To correct this imbalance, the solution is to undersample the positive class within the 40-45 range, reducing its dominance and improving fairness in the dataset.
NEW QUESTION # 153
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
NEW QUESTION # 154
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?
Answer: B
NEW QUESTION # 155
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