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| Section | Weight | Objectives |
|---|---|---|
| Deployment and Orchestration of ML Workflows | 22% | - ML pipeline orchestration
|
| ML Model Development | 26% | - Model tuning and evaluation
|
| Data Preparation for Machine Learning (ML) | 28% | - Data ingestion and collection
|
| ML Solution Monitoring, Maintenance, and Security | 24% | - Security and governance
|
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NEW QUESTION # 76
A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs from its own standalone Docker image.
The company needs to perform a lift and shift from the on-premises Kubernetes cluster to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: B
Explanation:
A lift-and-shift migration requires minimal architectural changes. The company already uses Kubernetes and standalone Docker images, which are fully compatible with Amazon Elastic Kubernetes Service.
The lowest-overhead approach is to push the existing Docker images to Amazon Elastic Container Registry and deploy them directly to the EKS cluster using standard Kubernetes manifests or Helm charts. This approach preserves the existing ML workflows, training logic, and inference services without refactoring.
Option A requires redesigning services with Kubeflow, significantly increasing complexity. Option C changes both data storage and training methodology. Option D requires retraining models and changing operational workflows.
Therefore, uploading Docker images to Amazon ECR and deploying them to EKS is the correct lift-and-shift solution.
NEW QUESTION # 77
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 # 78
An ML engineering team is spread across multiple locations. When the lead ML engineer opens an Amazon SageMaker AI notebook, the ML engineer does not see the latest merged notebook made by other team members from a Git repository.
The lead ML engineer must see the latest SageMaker AI notebook updates.
Which solution will meet this requirement?
Answer: C
NEW QUESTION # 79
An ML model is deployed in production. The model has performed well and has met its metric thresholds for months.
An ML engineer who is monitoring the model observes a sudden degradation. The performance metrics of the model are now below the thresholds.
What could be the cause of the performance degradation?
Answer: B
Explanation:
A sudden drop in model performance after a long period of stability is a classic indicator of data drift.
According to AWS ML documentation, production data distribution drift occurs when the statistical properties of incoming data change over time compared to the training dataset.
This drift can be caused by changes in user behavior, market conditions, seasonal trends, or upstream data pipelines. When drift occurs, the model's learned patterns no longer align with real-world data, leading to degraded accuracy and other performance metrics.
Lack of training data (Option A) and overfitting (Option D) are issues that typically manifest during initial training, not after months of stable production performance. Compute constraints (Option C) may affect latency but do not usually cause sustained accuracy degradation.
AWS recommends using SageMaker Model Monitor to detect such drift and trigger retraining workflows.
Therefore, drift in the production data distribution is the most likely cause of the observed performance degradation.
NEW QUESTION # 80
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes.
Which algorithm and hyperparameter should the company use to meet this requirement?
Answer: C
Explanation:
The requirement is to detect irregularities (anomalies) in patient data using an unlabeled dataset, which clearly defines an unsupervised anomaly detection problem. According to AWS documentation, Amazon SageMaker Random Cut Forest (RCF) is purpose-built for detecting anomalies in high-dimensional, continuous datasets such as healthcare metrics and time-series-like records.
RCF works by constructing multiple random decision trees that partition the data. Observations that are isolated closer to the root of these trees are more likely to be anomalies. AWS explicitly recommends RCF for use cases such as fraud detection, system monitoring, and healthcare anomaly detection.
The num_trees hyperparameter controls the number of trees in the forest. Increasing num_trees improves anomaly detection accuracy and stability by averaging anomaly scores across more trees, which is especially important in sensitive domains like healthcare. AWS documentation notes that larger forests provide better generalization and more reliable anomaly scores.
Option A (XGBoost) is a supervised learning algorithm and requires labeled data, making it unsuitable.
Option B (k-means) performs clustering but does not explicitly detect anomalies. Option C (DeepAR) is designed for time-series forecasting, not anomaly detection in unlabeled datasets.
Therefore, using Amazon SageMaker Random Cut Forest with a higher num_trees value is the most appropriate, scalable, and AWS-recommended solution.
NEW QUESTION # 81
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