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