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