Скачать или смотреть AWS Machine Learning & AI Specialty Certification Exam Question and Answer Example Bedrock Sagemaker

The AWS Certified Machine Learning – Specialty exam is a coveted certification for professionals who want to prove their expertise in building, training, tuning, and deploying machine learning (ML) models on AWS. Unlike entry-level exams, this certification assumes strong foundational knowledge in data science, ML algorithms, and AWS-native services like SageMaker, Kinesis, Glue, and S3.

🎯 Why This Certification Is Valuable
Proves real-world ML expertise on AWS
It’s not just about ML theory — you’re tested on how to use AWS tools like SageMaker, Feature Store, and pipelines to build end-to-end ML systems in the cloud.

Highly regarded by employers
This cert differentiates you from general data scientists. It shows you can deliver production-ready ML solutions at scale, not just build models in notebooks.

Covers broad and deep topics
Topics include data engineering, feature engineering, model tuning, security, monitoring, and deployment pipelines — all from a cloud-native perspective.

Bridges ML and DevOps
It includes MLOps concepts: continuous training, deployment, experiment tracking, and pipeline automation — all skills in high demand for ML engineers.

🧠 About This Sample Question
Scenario Recap:
You’re forecasting score differences using numerical features like historical team data. You’ve found multicollinearity — meaning your predictor variables are highly correlated. You need to reduce redundancy and improve model performance.

✅ Correct Answer:
Utilize Principal Component Analysis (PCA) to reduce the dimensionality of your data, then eliminate the resulting components that have very low variance.

🔍 Why This Is Correct:
Multicollinearity can hurt regression-based models (like linear regression), making coefficients unstable and reducing interpretability.

PCA is a technique that transforms correlated features into a smaller number of uncorrelated principal components.

These components are ordered by variance — so components with very low variance carry little information and can be safely removed.

PCA improves model performance and helps combat overfitting.

❌ Why the Other Options Are Incorrect:
Linear Discriminant Analysis (LDA) is a supervised technique primarily used for classification, not regression or multicollinearity mitigation.

LDA optimizes for class separation, not for decorrelating predictors.

Discarding components with high variance in PCA defeats its purpose — the high-variance components are the most informative.

📘 How This Reflects the Exam Format
This question reflects a common ML Specialty exam pattern:

A real-world business scenario (sports betting)

A clear machine learning challenge (multicollinearity)

Multiple reasonable-sounding answers, but only one based on ML best practices

To succeed on this exam, you must:

Recognize subtle differences between techniques (PCA vs. LDA)

Know when and how to apply them

Align your choices with AWS services and workflows where relevant (e.g., PCA via SageMaker Processing or Pandas in notebooks)

🧩 Final Thought
The AWS Machine Learning – Specialty exam is not just about knowing ML algorithms — it’s about knowing how to make intelligent architectural and algorithmic decisions in complex, cloud-based environments.

Questions like this test whether you understand the trade-offs, can apply appropriate techniques, and can help your organization make data-driven, scalable, and efficient decisions in production ML systems. That’s why this certification is so coveted by both employers and seasoned professionals alike.