The Key Ansible Interview Questions that Matter the Most

1. How would you implement infrastructure testing using Ansible and Testinfra?

Answer: Combine Ansible provisioning with Testinfra validation by creating test files that verify system state after automation runs. Use pytest to execute tests and generate reports.

What an ideal candidate should discuss: Test-driven infrastructure development, continuous testing integration, and test organization strategies.

2. Write a playbook that implements smoke tests for a deployed application.

---
- name: Application Smoke Tests
  hosts: webservers
  tasks:
    - name: Check application service status
      service_facts:
      
    - name: Verify service is running
      assert:
        that: ansible_facts.services['myapp.service'].state == 'running'
        fail_msg: "Application service is not running"
    
    - name: Test application endpoint
      uri:
        url: "http://{{ ansible_default_ipv4.address }}:8080/health"
        method: GET
        return_content: yes
      register: health_check
      
    - name: Validate response content
      assert:
        that:
          - health_check.status == 200
          - "'healthy' in health_check.content"
        fail_msg: "Application health check failed"

What an ideal candidate should discuss: Test automation integration, reporting mechanisms, and failure notification strategies.

3. How do you implement contract testing for APIs using Ansible?

Answer: Create playbooks that validate API contracts by testing endpoints, response formats, and data schemas. Use uri module with JSON schema validation to ensure API compliance.

What an ideal candidate should discuss: Schema validation techniques, API versioning testing, and integration with development workflows.

4. Describe how you would implement performance testing automation with Ansible.

Answer: Integrate with performance testing tools like JMeter or Artillery, automate test execution, collect performance metrics, and generate reports comparing baseline performance.

What an ideal candidate should discuss: Performance baseline management, load generation strategies, and automated performance regression detection.

5. How would you implement security testing automation using Ansible?

Answer: Integrate security scanning tools, automate vulnerability assessments, implement compliance checking, and generate security reports as part of the deployment pipeline.

What an ideal candidate should discuss: Security scanning integration, compliance frameworks, and automated remediation workflows.

1. How would you use Ansible to manage big data cluster deployments?

Answer: Create roles for different cluster components (HDFS, YARN, Spark), manage configuration across nodes, handle service dependencies, and implement rolling updates for cluster maintenance.

What an ideal candidate should discuss: Cluster scaling procedures, data locality considerations, and integration with cluster management tools.

2. Write a playbook to deploy and configure Apache Kafka cluster.

---
- name: Deploy Kafka Cluster
  hosts: kafka_brokers
  vars:
    kafka_version: "2.8.0"
    kafka_home: "/opt/kafka"
  
  tasks:
    - name: Create kafka user
      user:
        name: kafka
        system: yes
        shell: /bin/bash
    
    - name: Download and extract Kafka
      unarchive:
        src: "https://downloads.apache.org/kafka/{{ kafka_version }}/kafka_2.13-{{ kafka_version }}.tgz"
        dest: /opt
        remote_src: yes
        owner: kafka
        group: kafka
    
    - name: Configure Kafka broker
      template:
        src: server.properties.j2
        dest: "{{ kafka_home }}/config/server.properties"
      notify: restart kafka
    
    - name: Start Kafka service
      systemd:
        name: kafka
        state: started
        enabled: yes
  
  handlers:
    - name: restart kafka
      systemd:
        name: kafka
        state: restarted

What an ideal candidate should discuss: Cluster coordination, partition management, monitoring setup, and disaster recovery procedures.

3. How would you automate data pipeline deployment using Ansible?

Answer: Create playbooks for data processing framework deployment, manage pipeline configurations, handle data source connections, and implement pipeline monitoring and alerting.

What an ideal candidate should discuss: Pipeline orchestration, data quality checks, error handling, and integration with data catalog systems.

4. Describe how you would implement data backup automation with Ansible.

Answer: Automate database dumps, implement file system backups, coordinate with storage systems, verify backup integrity, and manage retention policies across different data stores.

What an ideal candidate should discuss: Backup validation procedures, recovery testing, encryption strategies, and compliance requirements.

5. How would you use Ansible to manage data lake infrastructure?

Answer: Automate storage layer setup, manage access controls, deploy data processing engines, configure metadata management, and implement data governance policies.

What an ideal candidate should discuss: Data partitioning strategies, access pattern optimization, cost management, and integration with analytics tools.

1. How would you use Ansible to deploy machine learning models in production?

Answer: Create playbooks for model deployment, manage model versioning, implement A/B testing infrastructure, handle model scaling, and integrate with ML monitoring tools.

What an ideal candidate should discuss: Model lifecycle management, feature store integration, performance monitoring, and rollback strategies.

2. Write a playbook to set up a GPU-enabled machine learning environment.

---
- name: Setup ML Environment with GPU
  hosts: ml_servers
  become: yes
  
  tasks:
    - name: Install NVIDIA drivers
      package:
        name: nvidia-driver-470
        state: present
    
    - name: Install Docker with NVIDIA container runtime
      apt:
        name:
          - docker.io
          - nvidia-container-runtime
        state: present
    
    - name: Configure Docker for NVIDIA runtime
      copy:
        content: |
          {
            "default-runtime": "nvidia",
            "runtimes": {
              "nvidia": {
                "path": "/usr/bin/nvidia-container-runtime",
                "runtimeArgs": []
              }
            }
          }
        dest: /etc/docker/daemon.json
      notify: restart docker
    
    - name: Pull ML base images
      docker_image:
        name: "{{ item }}"
        source: pull
      loop:
        - tensorflow/tensorflow:latest-gpu
        - pytorch/pytorch:latest
  
  handlers:
    - name: restart docker
      service:
        name: docker
        state: restarted

What an ideal candidate should discuss: Resource allocation, container orchestration, model serving optimization, and monitoring GPU utilization.

3. How would you implement automated model training pipeline deployment?

Answer: Create playbooks for training infrastructure setup, manage experiment tracking, implement pipeline orchestration, handle data preprocessing, and integrate with MLOps platforms.

What an ideal candidate should discuss: Experiment management, resource scheduling, distributed training setup, and artifact management.

4. Describe how you would automate feature store deployment using Ansible.

Answer: Deploy feature store infrastructure, configure data ingestion pipelines, set up feature serving endpoints, implement access controls, and integrate with ML workflows.

What an ideal candidate should discuss: Feature versioning, data lineage tracking, serving latency optimization, and integration with training pipelines.

5. How would you use Ansible to manage Kubernetes-based ML workloads?

Answer: Deploy ML operators, manage custom resources for ML jobs, configure GPU sharing, implement autoscaling, and integrate with experiment tracking systems.

What an ideal candidate should discuss: Resource optimization, job scheduling, model serving scaling, and integration with cloud ML services.