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