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Databricks Certified Machine Learning Professional Practice Test

77 preguntas disponibles

The Databricks Certified Machine Learning Professional certification validates advanced expertise in building, deploying, and managing machine learning solutions at scale on the Databricks Lakehouse Platform. This credential demonstrates a professional's ability to implement the full ML lifecycle-from data preparation and experimentation to model deployment and monitoring-using Databricks' integrated tooling, including MLflow, AutoML, and Delta Lake. It signifies proficiency in production-grade ML engineering, encompassing feature engineering, model registry management, CI/CD for ML, and monitoring for drift and performance degradation. Earning this certification positions you as a practitioner capable of designing robust, scalable ML systems that deliver reliable business value, bridging the gap between experimental data science and operationalized machine learning. It is recognized by leading organizations as a benchmark for senior ML engineers and architects working within the modern data ecosystem.

Examen de certificación
60 Preguntas del examen
2 horas Límite de Tiempo
Professional Nivel
Oportunidades profesionales y salario
Nivel inicial – Data Analyst $84,000 - $128,000
Nivel medio – Data Engineer / ML Engineer $119,000 - $181,000
Nivel senior – Principal Data Engineer $154,000 - $235,000
Data AnalystData Engineer / ML EngineerPrincipal Data Engineergrowing mercado
Por qué esta certificación abre puertas

In today's competitive landscape, the ability to operationalize machine learning is a critical differentiator for organizations. This certification provides tangible, industry-recognized proof of your advanced skills in production ML on one of the most prominent enterprise platforms. It directly impacts career advancement by qualifying you for senior and lead roles in ML engineering, MLOps, and AI solutions architecture. Employers actively seek certified professionals to ensure their ML initiatives are built on a foundation of best practices for scalability, reproducibility, and maintainability, making this credential a powerful asset for commanding higher compensation and leading strategic projects.

Plan de Estudio

Cada dominio está ponderado para coincidir con el examen de certificación real, por lo que una simulación de práctica completa predice tu resultado.

01Deep Learning
20%
02Supervised Machine Learning
20%
03Machine Learning Model Selection and Evaluation
18%
04Computer Vision
14%
05Introduction to Machine Learning Concepts
14%
06Natural Language Processing
14%
Detalles del Examen A00-240 | $250 USD | 2 horas
Código del Examen A00-240
Proveedor SAS Institute
Costo del Examen $250 USD
Puntaje Mínimo 725
Límite de Tiempo 2 horas
Preguntas del examen 60
Tipos de Preguntas Multiple Choice, Multiple Select, Short Answer
Política de Repetición 30-day waiting period after a failed attempt. Full exam fee required for each retake.
Formato del Examen Linear
Supervisión en Línea Disponible
Disponible En
English
Recursos de Estudio
Databricks Academy
DatabricksGratis
Official Databricks training courses aligned to certification exams
Ver
Databricks Certification Practice Exams
DatabricksGratis
Free practice questions available on each certification page
Ver
Preguntas Frecuentes

What are the prerequisites for attempting this exam?

Databricks strongly recommends significant hands-on experience (typically 2+ years) in data science and machine learning engineering, including practical work with the Databricks platform, PySpark, MLflow, and Python ML libraries (e.g., scikit-learn, TensorFlow/PyTorch). Prior completion of the Databricks Certified Associate Developer for Apache Spark certification is beneficial but not mandatory. Real-world experience in building and deploying ML models is essential.

How does this certification differ from the Databricks Certified Data Scientist credential?

While there is overlap, the Certified Machine Learning Professional focuses more intensely on the engineering and operationalization (MLOps) aspects of the ML lifecycle. It delves deeper into production systems, model deployment patterns (batch/streaming), the MLflow Model Registry, CI/CD pipelines, and systematic monitoring. The Data Scientist certification may place greater emphasis on the statistical modeling, experimentation, and business analysis phases.

What is the exam format and how is it delivered?

The exam is typically proctored online and consists of multiple-choice and multiple-answer questions. It is performance-based, meaning questions often present realistic scenarios and require you to select the correct solution or series of actions. The exact number of questions and time limit are set by Databricks and should be verified on the official certification page at the time of registration.

What is the recertification policy?

Databricks certifications are valid for two years. To maintain your certified status, you must pass the current version of the exam before your certification expires. This ensures that professionals maintain up-to-date knowledge with the evolving platform features and industry best practices.

What are the best resources for preparation?

Official preparation should include: 1) The official exam guide and blueprint from Databricks, 2) Instructor-led or self-paced training courses from Databricks Academy, 3) Extensive hands-on practice in a Databricks workspace, 4) Documentation for MLflow, Feature Store, AutoML, and the Databricks runtime for ML, and 5) Reviewing real-world project scenarios covering the full ML lifecycle.

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