Cloudera Generative AI Engineer Exam
Audience
The exam tests the skills and knowledge required by machine learning engineers, data scientists, and AI specialists to successfully design, build, and operationalize generative AI systems.
Unlike generalist credentials, this role-based exam is highly specialized for professionals building production-grade intelligence workflows:
Machine Learning Engineers
AI Solutions Architects
MLOps Engineers
Generative AI Specialists
This exam allows candidates to demonstrate their proficient mastery of advanced large language model frameworks, retrieval systems, and end-to-end model infrastructure within the Cloudera ecosystem.
Exam Details
- Number of questions: 60
- Duration: 90 minutes
- Pass Score: 65%
- Delivery: Online, proctored. Please review the system requirements to enable online proctored testing through QuestionMark.
- Allowed resources: None. You may not use reference materials, white papers, user guides, or any other resources during your exam.
- Support: If you need help, please email us.
Cloudera Skills & Knowledge Measured
This exam measures the skills and knowledge topics listed in Table 1 below. The weighting of each topic is also listed.
| Topic | WEIGHT (% of exam) |
|---|---|
AI and Machine Learning Foundations |
10% |
Cloudera AI (CAI) Platform Architecture |
12% |
Machine Learning Operations (MLOps) Lifecycle |
13% |
Generative AI and LLM Fundamentals |
15% |
Retrieval-Augmented Generation (RAG) Architectures |
15% |
Agentic AI Systems and Workflows |
15% |
Enterprise Governance and Security |
10% |
Production Deployment and Model Serving at Scale |
10% |
Table 1: Exam topics and weighting
Detailed Exam Objectives & Component Breakdown
10% — AI and Machine Learning Foundations
Core principles of supervised and unsupervised machine learning models.
Evaluation metrics for classic regression, classification, and clustering workloads.
Feature engineering and data preprocessing requirements for model readiness.
12% — Cloudera AI (CAI) Platform Architecture
Navigating the Cloudera AI interface, architecture, and workspace provisioning.
Managing project environments, engine profiles, session runtimes, and compute resources.
Configuring local container filesystems, persistent mounts, and external network proxies.
13% — Machine Learning Operations (MLOps) Lifecycle
Tracking machine learning experiments, hyperparameter logs, and metric runs.
Managing model registries, artifact packaging, deployment versions, and lineage.
Setting up continuous integration and continuous deployment (CI/CD) pipelines for ML.
15% — Generative AI and LLM Fundamentals
Core concepts behind Transformer architectures, tokenization, and embedding dimensions.
Strategies for prompt engineering, context window boundaries, and inference parameters.
Evaluating trade-offs between parameter-efficient fine-tuning (PEFT/LoRA) and foundation models.
15% — Retrieval-Augmented Generation (RAG) Architectures
Designing production-grade RAG systems to ground LLMs in proprietary enterprise data.
Managing vector databases, embedding pipelines, semantic indexing, and search parameters.
Optimizing retrieval metrics, text chunking strategy, and handling multi-document orchestration.
15% — Agentic AI Systems and Workflows
Building autonomous AI agents capable of multi-step execution, tool usage, and loop planning.
Developing stateful multi-agent collaboration frameworks and workflow trees.
Integrating external APIs, SQL relational engines, and knowledge bases into active reasoning steps.
10% — Enterprise Governance and Security
Enforcing fine-grained access control (FGAC) and masking using Apache Ranger.
Ensuring model lineage tracking, metadata auditing, and asset mapping using Apache Atlas.
Securing incoming API client transactions using Apache Knox and platform model API tokens.
10% — Production Deployment and Model Serving at Scale
Deploying traditional models, open LLMs, and TensorRT (TRT-LLMs) through the Cloudera AI Inference Service.
Configuring horizontal autoscaling policies, container replicas, and low-latency infrastructure.
Setting up telemetry metrics tracking using embedded Prometheus targets and Grafana dashboard visualization interfaces.
Suggested Training
- Implementing Enterprise Generative AI Solutions on Cloudera
- MLOps and Production Model Serving Management
Helpful Documentation
- Cloudera AI Inference Service Foundations
- Model Registry Standalone API Management
- Shared Data Experience (SDX) Security and Auditing
- Managing Applications, Workbench Sessions, and Workspace Models