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| Section | Weight | Objectives |
|---|---|---|
| MLOps and Monitoring | 28% | - Model lineage and reproducibility - Security and access management for ML - Incident response and remediation - CI/CD pipelines for ML - Model monitoring and drift detection - Cost optimization for ML workloads |
| Data Processing | 22% | - Data validation and quality assessment - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data ingestion and transformation - Data preprocessing and feature engineering |
| Model Deployment and Inference | 20% | - Model versioning and rollback - SageMaker endpoints configuration - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment - Inference optimization (latency, throughput) |
| Model Development | 30% | - ML frameworks (SageMaker, built-in algorithms) - Algorithm selection and model architecture - Hyperparameter optimization - Distributed training - Transfer learning and fine-tuning - Training and validation strategies |
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NEW QUESTION # 91
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?
Answer: B
NEW QUESTION # 92
A company wants to provide services to help other businesses label images. The company wants its labeling specialists to complete human labeling tasks on AWS. How should the company register the labeling specialists to receive tasks on AWS?
Answer: D
Explanation:
To enable labeling specialists within the company to perform tasks, the correct solution is to create and use an internal workforce in Amazon SageMaker Ground Truth. This allows the company to securely register and manage its own labeling team to receive and complete human labeling tasks.
NEW QUESTION # 93
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: C
Explanation:
AWS Lake Formation provides fine-grained access control and simplifies data governance for data lakes. By configuring Lake Formation tags to map ML engineers to their specific campaigns, you can restrict access to both structured and unstructured data in the data lake. This method is operationally efficient, as it centralizes access control management within Lake Formation and ensures consistency across Amazon Athena and S3 bucket access without requiring manual updates to policies or DynamoDB-based custom logic.
NEW QUESTION # 94
A company has deployed a model to predict the churn rate for its games by using Amazon SageMaker Studio.
After the model is deployed, the company must monitor the model performance for data drift and inspect the report. Select and order the correct steps from the following list to model monitor actions. Select each step one time. (Select and order THREE.) .
Check the analysis results on the SageMaker Studio console. .
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Schedule an hourly model explainability monitor.
Answer:
Explanation:
Explanation:
Step 1:
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Step 2:
Schedule an hourly model explainability monitor.
Step 3:
Check the analysis results on the SageMaker Studio console.
When monitoring a deployed model for data drift and explainability, AWS prescribes a specific workflow using SageMaker Clarify and SageMaker Model Monitor:
Create a SHAP baseline (Step 1)Before any monitoring can occur, SageMaker Clarify must establish a baseline explainability configuration. This baseline captures the reference SHAP values for feature importance using training or baseline data. Model Monitor uses this baseline to compare future inferences and detect drift in feature attributions.
Schedule the model explainability monitor (Step 2)After the baseline is created, an explainability monitoring schedule must be configured (hourly in this case). The monitor periodically analyzes inference data, compares it against the SHAP baseline, and generates reports that highlight drift or anomalies in feature contributions.
Inspect results in SageMaker Studio (Step 3)Once monitoring jobs run, SageMaker stores the analysis results in Amazon S3 and surfaces them in the SageMaker Studio console, where engineers can review metrics, violations, and visual reports.
This sequence is mandatory because:
A monitor cannot run without a baseline
Results cannot be reviewed until the monitor executes
NEW QUESTION # 95
A company has an Amazon S3 bucket that contains 1 TB of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet.
An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data.
The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 96
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