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