Free Practice Questions for Cloudera Generative AI Engineer Certification
- Guide checked for updates:
- 18 Sep 2026
- Question bank created:
- 11 Aug 2026
- Question bank last updated:
- 11 Aug 2026
Study with 360 exam-style practice questions designed to help you prepare for the Cloudera Generative AI Engineer.
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Key information about Cloudera Generative AI Engineer
- Multiple choice
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: AI and Machine Learning Foundations
1.1: Core principles of supervised and unsupervised machine learning models.
Core principles of supervised and unsupervised machine learning models.
1.2: Evaluation metrics for classic regression, classification, and clustering workloads.
Evaluation metrics for classic regression, classification, and clustering workloads.
1.3: Feature engineering and data preprocessing requirements for model readiness.
Feature engineering and data preprocessing requirements for model readiness.
2: Cloudera AI (CAI) Platform Architecture
2.1: Navigating the Cloudera AI interface, architecture, and workspace provisioning.
Navigating the Cloudera AI interface, architecture, and workspace provisioning.
2.2: Managing project environments, engine profiles, session runtimes, and compute resources.
Managing project environments, engine profiles, session runtimes, and compute resources.
2.3: Configuring local container filesystems, persistent mounts, and external network proxies.
Configuring local container filesystems, persistent mounts, and external network proxies.
3: Machine Learning Operations (MLOps) Lifecycle
3.1: Tracking machine learning experiments, hyperparameter logs, and metric runs.
Tracking machine learning experiments, hyperparameter logs, and metric runs.
3.2: Managing model registries, artifact packaging, deployment versions, and lineage.
Managing model registries, artifact packaging, deployment versions, and lineage.
3.3: Setting up continuous integration and continuous deployment (CI/CD) pipelines for ML.
Setting up continuous integration and continuous deployment (CI/CD) pipelines for ML.
4: Generative AI and LLM Fundamentals
4.1: Core concepts behind Transformer architectures, tokenization, and embedding dimensions.
Core concepts behind Transformer architectures, tokenization, and embedding dimensions.
4.2: Strategies for prompt engineering, context window boundaries, and inference parameters.
Strategies for prompt engineering, context window boundaries, and inference parameters.
4.3: Evaluating trade-offs between parameter-efficient fine-tuning (PEFT/LoRA) and foundation models.
Evaluating trade-offs between parameter-efficient fine-tuning (PEFT/LoRA) and foundation models.
5: Retrieval-Augmented Generation (RAG) Architectures
5.1: Designing production-grade RAG systems to ground LLMs in proprietary enterprise data.
Designing production-grade RAG systems to ground LLMs in proprietary enterprise data.
5.2: Managing vector databases, embedding pipelines, semantic indexing, and search parameters.
Managing vector databases, embedding pipelines, semantic indexing, and search parameters.
5.3: Optimizing retrieval metrics, text chunking strategy, and handling multi-document orchestration.
Optimizing retrieval metrics, text chunking strategy, and handling multi-document orchestration.
6: Agentic AI Systems and Workflows
6.1: Building autonomous AI agents capable of multi-step execution, tool usage, and loop planning.
Building autonomous AI agents capable of multi-step execution, tool usage, and loop planning.
6.2: Developing stateful multi-agent collaboration frameworks and workflow trees.
Developing stateful multi-agent collaboration frameworks and workflow trees.
6.3: Integrating external APIs, SQL relational engines, and knowledge bases into active reasoning steps.
Integrating external APIs, SQL relational engines, and knowledge bases into active reasoning steps.
7: Enterprise Governance and Security
7.1: Enforcing fine-grained access control (FGAC) and masking using Apache Ranger.
Enforcing fine-grained access control (FGAC) and masking using Apache Ranger.
7.2: Ensuring model lineage tracking, metadata auditing, and asset mapping using Apache Atlas.
Ensuring model lineage tracking, metadata auditing, and asset mapping using Apache Atlas.
7.3: Securing incoming API client transactions using Apache Knox and platform model API tokens.
Securing incoming API client transactions using Apache Knox and platform model API tokens.
8: Production Deployment and Model Serving at Scale
8.1: Deploying traditional models, open LLMs, and TensorRT (TRT-LLMs) through the Cloudera AI Inference Service .
Deploying traditional models, open LLMs, and TensorRT (TRT-LLMs) through the Cloudera AI Inference Service.
8.2: Configuring horizontal autoscaling policies, container replicas, and low-latency infrastructure.
Configuring horizontal autoscaling policies, container replicas, and low-latency infrastructure.
8.3: Setting up telemetry metrics tracking using embedded Prometheus targets and Grafana dashboard visualization interfaces.
Setting up telemetry metrics tracking using embedded Prometheus targets and Grafana dashboard visualization interfaces.
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