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