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