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