Courseiva
Free · No account needed · No credit card

Microsoft Certified: Azure AI Cloud Developer Associate (AI-200) (AI-200) Practice Test

418 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

Instant feedback after each answer
Full explanations included
Domain score breakdown
Real exam: 100 min
Pass mark: 700 (scaled 1–1,000)

Sample questions with explanations

This is exactly what you see during practice — question, options, and a full explanation after you answer.

Q1Containerized AI Workloadsmedium
Full explanation →

You have an AI model container hosted in Azure Container Registry. You want to ensure that every container image pushed to the registry is automatically scanned for known vulnerabilities before deployment. Which ACR feature should you enable?

AACR Tasks auto-purge
BContent Trust
CACR geo-replication
Microsoft Defender for Cloud vulnerability scanningCorrect

Microsoft Defender for Cloud vulnerability scanning is correct because it provides integrated image vulnerability assessment for Azure Container Registry, automatically scanning images on push and continuously re-scanning for newly disclosed CVEs. Enabling the Defender for Contai…Read full explanation

Q2Containerized AI Workloadseasy
Full explanation →

You need to store custom AI container images in a private registry hosted within Azure. Which Azure service provides a managed Docker registry service?

AAzure Blob Storage
BAzure Managed Disks
CAzure Artifacts
Azure Container RegistryCorrect

Azure Container Registry (ACR) is a managed, private Docker registry service that allows you to store and manage container images and artifacts. It supports Docker and OCI images, and integrates with Azure services like AKS, App Service, and Azure Machine Learning. For AI workloa…Read full explanation

Q3Containerized AI Workloadshard
Full explanation →

You are optimizing a large language model container inference deployment on AKS using GPU-enabled nodes (NC-series). The inference server experiences frequent out-of-memory errors on the GPU device itself during high context lengths. Which Kubernetes configuration metric should you monitor and alert on?

Acontainer_memory_working_set_bytes via kubelet
Bcontainer_cpu_cfs_throttled_periods_total
Cnode_net_bytes_total
container_gpu_memory_used_bytes via NVIDIA DCGM ExporterCorrect

GPU out-of-memory errors on the device itself must be detected by monitoring GPU memory utilization, which is exposed by the NVIDIA DCGM Exporter as container_gpu_memory_used_bytes (and related DCGM metrics like DCGM_FI_DEV_FB_USED). This metric reflects actual framebuffer memory…Read full explanation

What's covered

Questions are tagged by exam domain. Practice a specific domain or mix them all.

Untimed Practice

Answer at your own pace. Explanation and domain tag shown immediately after each answer.

Timed Practice

Countdown timer starts immediately. Results and domain scores shown at the end — just like the real exam.

Why practice here?

Full explanations on every question

Not just the right answer — you get exactly why each wrong option is wrong, so you learn the concept, not the answer.

Domain score breakdown

After each session see your score by exam domain so you know exactly where to focus study time.

100% free, forever

No subscription, no trial, no email wall. Start a session in under 10 seconds.

Exam-style questions

Scenario-based, precise wording, realistic distractors — written to match what you actually see on exam day.

← All AI-200 questionsAI-200 exam guideStudy guidePractice by domain