AI/ML Screening TestBy Vikas Sharma / 19/08/2025 AI/ML Screening Test 1 / 49 What is the purpose of a model registry in MLOps? To manage Kubernetes clusters To track CI/CD pipeline executions To store cloud infrastructure templates To store, version, and manage trained ML models 2 / 49 What is a common challenge in automating ML pipelines? Data versioning and reproducibility Automating UI testing Cloud billing alerts Writing HTML code 3 / 49 Which type of data is MOST commonly analyzed by AIOps platforms? Customer satisfaction surveys Structured business data Unstructured IT operations data like logs, metrics, and traces Video and image datasets 4 / 49 What is the role of Kubernetes in MLOps pipelines Hyperparameter tuning only Model evaluation Scaling and orchestrating ML workloads in production Data preprocessing 5 / 49 In MLOps, what is 'model lineage? Measuring network latency Monitoring server uptime Versioning HTML files Tracking datasets, code, and parameters that produced a model 6 / 49 Which cloud service provides a fully managed ML pipeline solution? VMware vSphere Photoshop Cloud Kubernetes without ML AWS SageMaker Pipelines 7 / 49 Which of the following describes Continuous Training (CT) in MLOps? Deploying models continuously without validation Scaling infrastructure on demand Running unit tests for ML code Re-training models regularly with new data 8 / 49 Which of the following best describes the goal of AIOps? Automating CI/CD pipelines without monitoring Applying AI/ML techniques to IT operations for proactive issue detection Replacing DevOps entirely Automating infrastructure scaling only 9 / 49 What is the main purpose of MLOps? To automate cloud billing processes To build web applications To integrate ML models into production through CI/CD pipelines To replace software engineering practices 10 / 49 Which challenge does AIOps primarily address? Inability to run unit tests Lack of cloud cost optimization Limited access to GitHub repositories Manual analysis of large-scale operational data 11 / 49 Which of the following tools is commonly associated with AIOps? Moogsoft Kubernetes Terraform Apache Spark 12 / 49 Which orchestrator is commonly used for ML pipelines in Kubernetes? Kubeflow Pipelines Splunk Nagios Airflow only 13 / 49 Which of the following is a common model deployment pattern? Git Rebase Deployment Static Scaling Blue-Green Deployment Round-Robin Compilation 14 / 49 . Which tool is commonly used for workflow orchestration in ML pipelines? Apache Airflow Excel Jenkins only Nagios 15 / 49 What is 'model rollback' in CI/CD pipelines Re-training from scratch Resetting hyperparameters Reverting to a previous stable model when the new one fails Restarting the server 16 / 49 In MLOps, what is 'model drift'? When model performance degrades due to changes in data patterns When models crash during deployment When hyperparameters remain constant When the model is moved between servers 17 / 49 Which CI/CD tool is widely integrated with MLOps pipelines? MS Word Jenkins Photoshop Final Cut Pro 18 / 49 How does AIOps reduce 'alert fatigue? By automating deployments only By correlating events and suppressing noise By generating more alerts By disabling monitoring tools 19 / 49 Which monitoring metric is MOST relevant in MLOps? Number of Git commits Website traffic CPU utilization only Model accuracy and drift detection 20 / 49 Which AI technique is commonly used in AIOps for anomaly detection? Clustering algorithms Linear regression only Rule-based filtering Manual log parsing 21 / 49 Which stage in MLOps involves hyperparameter tuning? Model training & optimization Deployment Monitoring Incident management 22 / 49 What is blue-green deployment in ML pipelines? Maintaining two identical environments (blue and green) to switch traffic safely during updates Splitting training datasets randomly Using two ML algorithms simultaneously Running models in GPUs only 23 / 49 . What does a feature store provide in MLOps? A CI/CD orchestrator A monitoring dashboard A code versioning platform A centralized repository for storing and sharing ML features 24 / 49 What is Canary Deployment in MLOps? Gradually rolling out a model to a subset of users before full release Deploying multiple models in parallel permanently Deploying models without validation Deploying models only in staging 25 / 49 Which tool is widely used for managing ML pipelines? Terraform Jenkins Nagios Kubeflow 26 / 49 What does CI/CD integration with model registry achieve? Tracks GitHub issues only Automates promotion of validated models to production Simplifies HTML rendering Improves IDE performance 27 / 49 What is the purpose of MLflow in MLOps? Database sharding Log analysis Container orchestration Experiment tracking, model registry, and deployment 28 / 49 Which of the following ensures fairness and bias detection in ML models? Skipping validation Relying on accuracy only Using random data Responsible AI practices and monitoring 29 / 49 In a CI/CD pipeline, unit tests for ML models typically validate: User interface design Operating system drivers Network bandwidth Data preprocessing and feature transformations 30 / 49 Which of the following ensures reproducibility in ML experiments? Avoiding CI/CD Manual hyperparameter tuning only Skipping documentation Versioning code, data, and models 31 / 49 What is the role of GitOps in MLOp? Training ML models Visualizing anomalies Running hyperparameter optimization Managing ML infrastructure and deployments declaratively through Git 32 / 49 . What is shadow deployment in MLOps? Deploying without monitoring Deploying on shadow servers only Deploying only half the model Running a new model in parallel with the current one without serving predictions to users 33 / 49 What is online learning in ML deployment Updating the model incrementally with streaming data Batch scoring only Deploying only during office hours Offline retraining every month 34 / 49 . What role does Natural Language Processing (NLP) play in AIOps? Training computer vision models Creating CI/CD pipelines Provisioning infrastructure Parsing log files and correlating incidents 35 / 49 What is the role of continuous validation in MLOps Improves GPU performance Ensures deployed models remain accurate and reliable with new data Tracks Git commits Reduces network traffic 36 / 49 Which is a key output of anomaly detection in AIOps? Application code coverage CI/CD deployment reports Optimized hyperparameters Identified unusual events that may indicate system issues 37 / 49 Why is monitoring critical after model deployment? To detect performance degradation and drift To reduce hardware costs To speed up CI builds only To reduce developer workload 38 / 49 What is the purpose of data drift detection? To identify changes in input data distribution affecting model performance To optimize CI/CD runtime To version-control datasets To detect server failures 39 / 49 Which metric is best for evaluating classification models in imbalanced dataset? CPU usage Mean Squared Error Precision-Recall AUC Accuracy only 40 / 49 What is a key advantage of using AIOps in incident management? Proactive anomaly detection and root cause analysis Increased number of false alerts Manual intervention for faster resolutions Replacing monitoring tools entirely 41 / 49 Which of the following tools integrates monitoring into MLOps pipelines? PowerPoint Tableau only Prometheus & Grafana Slack 42 / 49 What is the difference between DevOps and MLOps? DevOps is only for cloud computing MLOps replaces DevOps entirely DevOps focuses on CI/CD for software, while MLOps extends it to ML models with added steps like training and monitoring MLOps is only about data collection 43 / 49 Which of the following best describes model governance Hyperparameter optimization Processes ensuring compliance, auditability, and security in ML models Visualization dashboards Anomaly detection only 44 / 49 Which algorithm is often used in AIOps for log anomaly detection? LSTM (Long Short-Term Memory) networks Static Regex Matching Decision Trees for UI Naive Bayes only 45 / 49 What is the main role of Docker in MLOps pipelines? To act as a monitoring dashboard To containerize ML models for consistent deployment To perform hyperparameter tuning To analyze log anomalies 46 / 49 Which of the following is NOT a stage in the MLOps lifecycle? Model monitoring Model training Model destruction Model deployment 47 / 49 Which of the following is an example of predictive analytics in AIOps? Forecasting disk failures before they occur Manual root cause analysis Static capacity planning Real-time log streaming 48 / 49 Why is explainability important in production ML models? To understand model decisions and build trust with stakeholders To increase data size To reduce deployment frequency To reduce CI/CD runtime 49 / 49 Which of the following is an example of CI/CD for ML models? Manual model validation Skipping version control Automating retraining, testing, and deployment of models Running experiments locally only Your score is Share this: Share on Facebook (Opens in new window) Facebook Share on X (Opens in new window) X