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