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