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