Top Chief AI Officer (CAIO) Interview Questions and Answers

Author by: Pooja Rawat

Artificial intelligence is no longer a niche experiment; it is a core business driver. As a result, the once-rare Chief AI Officer (CAIO) role is quickly becoming a cornerstone of modern C-suites. In fact, an IBM survey found 26% of global enterprises had a Chief AI Officer in 2025, up from just 11% two years prior. Another study by PwC noted that 68% of U.S. enterprises now have a chief AI, data, or analytics executive in the C-suite (a huge leap from 22% in 2019). This surge reflects how deeply AI strategy is now embedded in corporate goals. With generative AI and machine learning transforming industries, companies are racing to appoint AI leaders who can harness these technologies for a competitive advantage.

If you are preparing for a CAIO interview, you will need to demonstrate strategic insight, leadership experience, and up-to-the-minute knowledge of AI trends. Drawing on the latest trends (like the boom in generative AI) and best practices (from governance frameworks to talent development), we have compiled 15 Top CAIO Interview Questions you should be ready for.

Top 15 Chief AI Officer (CAIO) Interview Questions and Answers

1. Tell me about your experience leading AI strategy and implementation. What were your most significant achievements?
You should concisely highlight your AI leadership track record, focusing on scope and impact. Emphasize the scale of AI initiatives you led, the technologies involved, and the tangible business results achieved (e.g., revenue growth, cost savings, and process improvements). For example, mention if you led a cross-functional team to deploy a machine learning model that improved customer retention by X% or reduced operational costs by Y%. Frame your achievements in terms of business value delivered. Also note any challenges you overcame (like data quality issues or stakeholder buy-in) and how you addressed them, demonstrating resilience and problem-solving. If possible, quantify success with metrics (e.g., “increased forecasting accuracy by 30%, leading to a 10% inventory reduction”). This shows you do not just drive AI projects, you drive results aligned to business goals.

2. How do you approach developing an AI strategy for an organization? Walk me through your process.

Assess Readiness: Begin with a thorough assessment of the organization’s current state; data maturity, existing AI projects, infrastructure, and business objectives. Understanding business priorities and pain points is critical.
Identify High-Value Opportunities: Next, work with stakeholders to identify and prioritize high-impact AI use cases that align with strategic goals. You might conduct workshops to surface ideas and then evaluate them based on potential ROI, feasibility, and risk.
Framework & Roadmap: Develop a clear AI strategy framework outlining how AI will support key business objectives. This includes a phased roadmap (short-term wins and long-term initiatives) and defined success metrics. Ensure you address governance and ethical considerations in the strategy from the start (e.g., compliance, fairness guidelines).
Stakeholder Buy-In: Secure buy-in from leadership early by communicating how the AI strategy creates value. You should describe how you engage executives in planning and use their input to refine the roadmap. For example, aligning AI initiatives with each business unit’s KPIs helps create shared ownership.
Execution Plan: Finally, outline the execution plan, resources needed, talent acquisition or training, technology selection, and change management. Emphasize an iterative approach (pilot, learn, scale) and mention how you balance quick wins with long-term transformation.

3. Describe your experience with different AI technologies (ML, NLP, computer vision, generative AI, etc.). Where do you see each providing the most business value?
Demonstrate a broad understanding of major AI technologies and tie them to use cases. For example:

Machine Learning (ML): You have likely used traditional ML for predictive analytics (forecasting demand, risk scoring) and process optimizations. Emphasize your ability to choose the right algorithms for structured data problems to drive efficiency or cost reduction.
Natural Language Processing (NLP): Mention any NLP projects (like chatbots or text analytics) and note that NLP excels in automating text-heavy processes (customer service, sentiment analysis, document processing).
Computer Vision: Highlight experience applying vision AI (object detection, image classification) in contexts like quality control in manufacturing or medical imaging, anywhere visual pattern recognition creates value.
Generative AI: Acknowledge the recent surge in generative AI and its potential (e.g., content creation, code generation, designing marketing materials). You might say it is promising for enhancing creativity and productivity when used with proper oversight.

4. How have you handled data governance, privacy, and ethical considerations in your AI implementations?
Convey that responsible AI is a priority in your leadership. Start by describing any AI governance frameworks or policies you have established to ensure compliance and ethics. For example, mention if you set up an AI governance committee or implemented guidelines for model development and deployment. Key points to cover:

Data Privacy: Explain how you ensure AI systems comply with data protection regulations (like GDPR) and respect user privacy. Perhaps you anonymize or encrypt sensitive data and perform privacy impact assessments.
Bias & Fairness: Discuss methods you use to identify and mitigate bias in AI models. For example, you might use bias detection toolkits and include diverse datasets to improve fairness. Emphasize proactive bias audits and tuning models to prevent discriminatory outcomes.
Ethical Frameworks: Note any ethical AI frameworks or principles (e.g., transparency, accountability) that guide your projects. You could say, “I apply frameworks like ethical AI checklists at each stage, from design to deployment, to ensure fairness and transparency.”
Governance in Practice: Provide an example, such as implementing a review process for high-risk AI applications (e.g., an ethics review board or requiring human-in-the-loop for critical decisions). Also mention how you educate and train teams on these practices (maybe conducting Responsible AI training workshops).

5. Describe how you have built and developed AI teams. What do you look for when hiring AI talent?
Outline your approach to team building and talent management in AI. Key points:

Hiring Criteria: Explain the qualities you value in AI talent. For example, you look for strong technical skills and business acumen, people who not only build models but also understand business impact. You might mention seeking diversity in skill sets (Data Scientists, ML Engineers, Product Managers) and in backgrounds to foster creative solutions.
Building Teams: Highlight any experience growing a team from scratch or scaling an existing one. You should mention strategies like pairing senior experts with junior talent for mentoring, or how you have attracted talent in a competitive market (e.g., offering exciting projects, a learning culture).
Talent Development & Retention: Discuss how you invest in upskilling and retaining your team. For example, “You ensure continuous learning via training programs, conferences, and internal knowledge sharing.” Emphasize creating a career path for data professionals, so they stay engaged.
Collaboration Culture: Note that you foster a collaborative environment, perhaps through agile cross-functional teams, where Data Scientists, Engineers, and domain experts work closely together. This breaks silos and keeps projects aligned with business needs.
Handling Performance Issues: If asked, explain your approach to underperformance: you would coach and mentor individuals, set clear expectations, and if needed, make tough decisions to maintain a high-performing team. (This shows you can handle leadership challenges constructively.)

6. As CAIO, how would you work with other C-suite leaders to drive AI adoption and transformation across the organization?
You should show that you are a collaborative executive who can influence and partner at the C-level. Key strategies to mention:

Build Relationships: Emphasize your approach to establishing trust and common ground with peers like the CIO, CTO, CDO, CMO, CISO, etc. For example, you had to schedule regular touchpoints with each leader to understand their goals and pain points, ensuring AI initiatives support their objectives. You might say, “I view the CAIO as a strategic partner to every other executive, aligning AI efforts with each department’s needs.”
Shared Vision: Explain how you create a shared AI vision across the leadership team. This could involve workshops or strategy sessions to get input from all sides and forge agreement on AI priorities. By framing AI projects as solutions to other leaders’ challenges (like improving marketing personalization for the CMO or automating compliance checks for the CISO), you get buy-in. You effectively create shared ownership of AI initiatives.
Influence and Communication: Highlight your ability to communicate complex AI concepts in business terms. For example, you provide straightforward updates to the executive team, focusing on metrics like ROI, risk, and strategic impact rather than technical jargon. This helps other C-suite members feel informed and confident about AI projects.
Navigating Priorities: Mention how you handle competing priorities or skepticism. You could say, “If an executive is hesitant about an AI project, I listen to their concerns and provide data or pilot results to demonstrate value.” Being responsive to feedback and willing to adjust plans shows flexibility. If conflicts arise (e.g., resource competition), you have to work through compromise, backed by a clear justification of AI’s benefits.

7. How do you measure the success or ROI of AI initiatives?
Measuring AI success involves defining clear Key Performance Indicators (KPIs) tied to business outcomes before starting a project. Key points:

Business-aligned Metrics: Emphasize picking metrics that reflect the AI project’s intended business value. For example, “You measure success in terms of improvement in relevant metrics. If an AI system is for customer service, look at customer satisfaction scores or average handling time; for a sales recommendation engine, measure conversion rates or revenue uplift.” Always link AI performance to a business KPI (cost saved, revenue generated, time reduced, quality improved).
Baseline and Uplift: Note that you establish a baseline (what was the metric before AI) and then track the delta after implementation. E.g., “We had a baseline of 70% accuracy in demand forecasts; after deploying the AI model, it improved to 85%, resulting in 15% less inventory holding costs.”
ROI Calculation: You can mention calculating ROI in financial terms: quantifying benefits (like annual savings or extra sales) versus the cost of development and deployment. For example, if AI process automation saved 2,000 work hours annually, translate that into cost savings.
Operational Metrics: Besides high-level outcomes, you might track technical metrics that contribute to business value. E.g., model precision/recall (for accuracy of predictions), system uptime, or throughput improvements, but only mention these alongside explaining how they matter to the business (e.g., higher model accuracy leads to fewer false positives in fraud detection, improving trust and saving money).
Continuous Monitoring: Explain that you do not treat success as one-time; you set up dashboards or periodic reviews to continuously monitor these KPIs over time, ensuring the AI continues to deliver value or to catch model drift.

8. What are the key regulatory frameworks and guidelines that govern AI, and how would you ensure our AI systems comply with them?
Start by naming the major AI-related regulations and standards relevant to the company’s region/industry. For example:

General AI Regulations: Mention the upcoming EU AI Act (if applicable) and how it classifies AI systems by risk with different obligations. If in healthcare/finance, cite sector-specific rules (FDA’s AI/ML guidelines for medical devices, or OCC guidelines for banking models). Also mention general data protection laws like GDPR and how they relate to AI (especially regarding personal data and automated decision-making).
Frameworks/Standards: Bring up standards like ISO/IEC 42001 (AI Management System) or the NIST AI Risk Management Framework, which provide best practices for AI governance and compliance. These show you are aware of structured approaches to trustworthy AI.
Ensuring Compliance: Then, outline how you would ensure compliance in practice:

➔ Conduct an AI compliance audit or assessment to map our AI use cases against these regulations. For example, identify if any use case falls under “high-risk” in the EU AI Act and thus requires strict controls (like transparency or human oversight).
➔ Develop a compliance matrix mapping each regulatory requirement to internal controls or processes. E.g., for GDPR: ensure we have data subject consent or opt-outs for AI-driven decisions, and for the AI Act: implement risk assessment documentation for high-risk systems.
➔ Policy Implementation: You would create or enforce policies covering areas like data privacy, model documentation, bias monitoring, and record-keeping of AI decisions. For example, maintain thorough documentation (model cards, data lineage, test results) to be audit-ready.
➔ Cross-Functional Collaboration: Highlight working closely with legal, compliance, and security teams to stay updated on regulatory changes and interpret requirements correctly. You might say, “I ensure our AI governance committee includes our Privacy Officer or compliance manager so we build controls in from design to deployment.”
➔ Training & Awareness: Ensure all AI development teams are trained on these compliance requirements so they incorporate them (like fairness metrics or explanation facilities) during model development.

9. How do you evaluate and manage risks when using third-party AI vendors or pre-built models?
You should show that you approach third-party AI solutions with due diligence and governance:

Vendor Risk Assessment: Describe how you perform upfront evaluations of AI vendors. “You should conduct vendor risk assessments, reviewing the vendor’s security practices, compliance with regulations, and reliability.” For example, verify if the vendor meets standards (ISO 27001 for security or has model transparency documentation). Check their track record, any history of breaches or ethical issues?
Quality and Bias Evaluation: Before integrating a third-party model, independently test it on your data. Ensure it meets your accuracy needs and check for biases or errors in outputs. For example, run a pilot or proof-of-concept to validate performance and fairness. If the model is closed-source, insist on information about its training data and known limitations.
Contractual Safeguards: Highlight that you negotiate contracts to include clear terms on data privacy, ownership, and performance. E.g., ensure the contract covers how the vendor will use (or not use) your data, clauses for model updates, service level agreements for uptime, and audit rights. Also include an exit strategy in case the vendor service does not work out (data portability, etc.).
Integration and Monitoring: Explain how you plan to integrate third-party models into your systems securely, likely through well-defined APIs or middleware that can be monitored. Implement monitoring on outputs from third-party AI for anomalies or drifts. If the vendor updates their model, re-evaluate to ensure it still functions as expected with your use case.
Compliance and IP: Ensure the third-party solution does not introduce compliance issues, e.g., if they use your data to improve their model, is that GDPR-compliant and agreed upon? Also, verify intellectual property and licensing, that you have the right to use the model outputs commercially, etc.
Example: You can add, “For example, when evaluating an AI SaaS vendor for NLP, I performed a thorough review and required a security audit. We discovered they did not encrypt data at rest, so we required that fix before signing. We also ran the model on a sample of our own texts to ensure its sentiment analysis was accurate for our domain.”

10. How do you identify and prioritize AI opportunities or use cases to pursue?

Outline a systematic approach for evaluating potential AI projects, showing that you focus on high-impact, feasible initiatives. Key steps:

Business Impact: First, look for use cases aligned with strategic business goals (revenue growth, cost reduction, customer experience, risk management, etc.). You might say, “I prioritize projects that move the needle on operational efficiency or customer satisfaction, rather than novelty projects without clear ROI.” For each idea, assess the potential value in quantifiable terms (e.g., “could this save $X or increase sales by Y%?”).
Feasibility: Consider the technical viability and data availability for each use case. If the data needed for a given AI idea is not available or the technology is immature, that use case might get deprioritized despite its potential value. You can mention evaluating data quality/quantity, required expertise, and whether you have or can acquire the resources to execute.
Quick Wins vs. Strategic Projects: Balance your portfolio. You should identify some “quick win” projects that are easier to implement and show immediate value (to build momentum and buy-in), versus longer-term strategic projects that may be more complex but are transformative. Explain that you create a roadmap mixing both.
Scoring Framework: It might help to mention using a scoring or matrix method, for example, scoring each use case on Impact (high/medium/low) and Effort/Risk (high/medium/low), and prioritizing high-impact, low-effort items first. This keeps the process objective.
Stakeholder Input: Involve business stakeholders in this prioritization. “You should work closely with department heads to identify their pain points and evaluate which AI solutions would be most beneficial and welcomed.” This ensures alignment and also helps gauge change management aspects (if a department is not ready to adopt, that project might struggle).
Review and Adapt: Once prioritized, you keep an ongoing backlog and regularly review it as conditions change (new data becomes available, business strategy shifts, etc.). This agility means the AI roadmap stays relevant.

11. How do you balance driving AI innovation and experimentation with the need for reliable, production-grade solutions that deliver business value?

Acknowledge this classic tension and show you can manage innovation vs. execution thoughtfully. Key points:

Dual-track Strategy: Explain that you often maintain a two-track approach: one for R&D/innovation and one for production deployment. For example, “You might allocate a portion of the team’s time to exploring emerging AI technologies or proofs-of-concept, while the rest focuses on scaling proven solutions.” This ensures you are innovating without neglecting stability.
Evaluation and Risk Management: Describe how you decide when an experimental idea is ready to graduate to production. You could mention having criteria or stage-gates, e.g., an experimental model must meet certain accuracy or performance benchmarks and pass risk reviews (bias, security checks) before moving to production. “I encourage experimentation, but I also set clear success metrics and timelines. If a prototype is not meeting goals, we either refine or shelve it rather than deploy something unreliable.”
Sandbox Environments: Highlight that you use sandbox or pilot environments to test innovative solutions on a smaller scale. This allows learning and iteration without jeopardizing core operations. Only after a pilot proves value and robustness do you integrate it into mission-critical systems.
Resource Allocation: Explain how you allocate resources between innovation and production. Perhaps maintain a small innovation lab or “center of excellence” that tries out new ideas, while delivery teams focus on scaling. Also, align innovation efforts with business needs so they are not innovation for its own sake, e.g., exploring a new AI technique because it might solve a known business problem or create a new opportunity.
Communication: Communicate the purpose of both streams to stakeholders. For example, set expectations with executives that some percentage of projects are exploratory (and might fail), but that you have a process to quickly learn from and either pivot or stop those. Meanwhile, production projects are managed with proper project discipline (timelines, quality assurance) to reliably deliver value.

12. What is your approach to identifying and mitigating bias in AI algorithms?

Communicate a proactive and methodical approach to ensuring fairness in AI systems. Key steps:

Diverse Data & Testing: Start at the data level, “You should ensure training data is as representative as possible of the populations affected by the AI.” Explain that you look for potential bias in data (e.g., underrepresentation of a group) and address it via data augmentation or resampling if needed. Also, split evaluation by subgroup: for example, testing model accuracy separately for different demographic groups to uncover disparate performance.
Bias Audits & Metrics: Mention that you employ fairness metrics and bias detection toolkits to quantify bias. For example, “After model training, I measure metrics like disparate impact or false positive/negative rates across groups. If I find that, say, an HR recruiting model scores a certain demographic lower systematically, that’s a red flag to fix.”
Techniques to Mitigate: Describe mitigation strategies: algorithmic (like reweighting data, using fairness-aware algorithms, or adding constraints in model training to equalize outcomes) and post-processing (like adjusting the decision threshold for groups to balance outcomes). The specific techniques can be mentioned if you know them, but even stating conceptually that you would adjust the model or its outputs to correct biases is good.
Human Review: State that for critical decisions, you keep a human in the loop to review or override AI outputs, especially in early stages, as an added check against biased results.
Governance & Iteration: Emphasize incorporating bias checks into the development lifecycle (not just at the end). Perhaps you have a practice of conducting an ethical review or bias test at each major model update. If biases are found, you iterate on the model. Document these findings and solutions as part of governance.

13. How do you ensure the organization’s data is ready and sufficient for AI initiatives?

Describe how you approach data strategy as a foundation for AI success:

Data Audit: Explain that you start by assessing what data is available, its quality, and how it is managed. “You should perform a data audit to inventory relevant datasets, identify gaps, and evaluate data cleanliness (completeness, accuracy, consistency).” For example, check if key data is siloed in different systems and might need integration.
Data Governance Alignment: Emphasize aligning data governance practices with AI needs. This means ensuring data policies (quality standards, metadata, lineage tracking) support AI. You might mention establishing a single source of truth for critical data and ensuring that things like customer data are labeled and accessible for model training with proper permissions.
Collaboration with CDO/Data Teams: Indicate you have worked closely with any Chief Data Officer or data engineering teams. “A CAIO must collaborate on data architecture, making sure we have the data pipelines and platforms (like a data lake or feature store) to feed AI models with the right data.” If data is insufficient, you plan for data collection or acquisition strategies (e.g., external data partnerships or data enrichment).
Data Preparation Processes: Highlight implementing processes for data preparation as part of AI projects. For example, setting up automated ETL (Extract, Transform, Load) workflows to continuously supply fresh data for model training and updates. You might also ensure data labeling efforts are in place if supervised learning is used (could involve internal labeling or outsourcing).
Quality Controls: Note specific measures: “You should enforce data quality checks, no model training on data until it meets defined quality thresholds (no excessive missing values, etc.).” Mention using tools for data validation and having data stewards for critical domains.
Scalability and Availability: Ensure the data infrastructure can handle the volume, velocity, and variety needed for AI. If planning big AI (like deep learning on large datasets), you need adequate storage and compute.
Security & Privacy as Part of Readiness: Ensuring data readiness also means data is compliant and safely accessible. E.g., implement data anonymization where required so that data can be used in AI without privacy breaches.

14. How do you assess and manage risks associated with AI projects?

Below is a structured risk management approach to AI initiatives, covering various risk dimensions:

Risk Identification: Describe how, at the outset of a project, you systematically identify potential risks. “You should evaluate risks across multiple categories: ethical (bias, fairness), regulatory compliance, data privacy, security, financial impact, and operational risks (like model failure or downtime).” For each AI use case, consider what could go wrong, e.g., could the model make an unsafe recommendation? Could data breaches happen? Could results be misinterpreted?
Assessment/Prioritization: Once identified, assess the likelihood and impact of each risk. You might mention using a scoring method (e.g., high/medium/low for each). This helps prioritize which risks need the most attention. For example, a risk of regulatory non-compliance might be high impact/high likelihood, so that’s the top priority to mitigate.
Mitigation Strategies: For each significant risk, outline mitigation controls. For example:

➔ If bias is a risk, mitigation is bias testing and model adjustments.
➔ If model error could cause financial loss, mitigation might include a human review step or setting conservative thresholds.
➔ For data privacy risk, mitigation includes data anonymization or limiting personal data usage.
➔ Security risks are mitigated by penetration testing and encryption, etc.

Essentially, map risks to actions and owners. Possibly mention creating a risk register or matrix for tracking.

Governance & Oversight: Explain that you incorporate these risk assessments into the project’s governance. Maybe you have an AI steering committee or review board that vets projects for these risks before deployment. Or that you follow frameworks like NIST AI Risk Management to ensure completeness. Regular audits or checkpoints (like a “go/no-go” before production with a risk checklist) can be mentioned.
Monitoring & Response: Even after deployment, you continuously monitor for risk indicators (as covered in monitoring earlier). Also, have incident response plans: e.g., if an AI system causes an unexpected issue, what steps do you take (roll back model, notify stakeholders, review what went wrong, etc.).

15. How would you foster an AI-driven culture within the organization?

Explain how to cultivate a culture where AI is embraced and leveraged by all, not just the data science team:

Executive Sponsorship and Vision: State that it starts from the top. “You should work with leadership to consistently communicate a vision of becoming a data/AI-driven company.” When the CEO and C-suite visibly support AI initiatives and talk about their importance, it sets the tone. You, as CAIO, would regularly share success stories and the “why” behind AI projects to get everyone on board.
AI Literacy and Training: A major component is educating employees. Describe plans for organization-wide AI literacy programs. For example, offer workshops or e-learning for non-technical staff about AI basics and how AI can aid their work. Perhaps establish an “AI Academy” internally. Also, more in-depth training for managers on how to identify AI opportunities and collaborate with technical teams. This reduces fear and builds enthusiasm.
Cross-functional AI champions: Identify or recruit AI ambassadors in different departments, people who are interested in AI and can serve as liaisons or product owners for AI projects in their domain. This creates grassroots support.
Demonstrate Quick Wins: As part of culture change, highlight early wins and positive impacts of AI on employees’ day-to-day. “You should communicate how AI is helping remove mundane tasks (like automating reports) so employees can focus on more meaningful work.” When people see AI as a tool that benefits them, they become advocates. Celebrate teams that successfully use AI (perhaps via internal newsletters or awards) to reinforce positive attitudes.
Incorporate AI in Goals: Encourage each business unit to include AI-related objectives or KPIs. For example, customer service aiming to leverage an AI chatbot to improve response times. When part of their goals, teams will pay attention.
Collaboration and Inclusion: Emphasize a culture of collaboration between technical AI teams and domain experts. Create forums (like “AI ideas” workshops or hackathons) where anyone can pitch process problems and the AI team can help prototype solutions. This inclusion makes people feel part of the AI journey.
Address Fear and Ethics Openly: Acknowledge that some employees might fear AI (job displacement concerns, etc.). Tackle this by being transparent about AI’s role, e.g., “AI is here to augment, not replace; we will retrain staff for higher-level roles as automation takes over repetitive work.” Also, involve employees in discussions about ethical AI use, so they trust these systems.
Measure Adoption: You have to measure culture shift via surveys or tracking usage of AI tools across the organization, and adjust initiatives accordingly.

Master CAIO Readiness with AAISM Training from InfosecTrain

Preparing for a Chief AI Officer interview means more than technical know-how. You need to articulate your strategic vision, governance mindset, leadership philosophy, and ethical commitment to AI-driven transformation. Each interview question is a chance to showcase how you bridge AI with business value through risk-managed innovation, cross-functional collaboration, and responsible deployment.

But to stand out in this competitive landscape, you need more than experience; you need structured, specialized preparation.

InfosecTrain’s AAISM Certification Training Course is designed to equip aspiring CAIOs and AI leaders like you with:

In-depth knowledge of AI governance, ethics, and regulatory frameworks
Practical skills in AI risk management, data privacy, and compliance
Strategic insight into enterprise AI integration and leadership
Confidence to lead AI programs that align with business and security objectives

Whether you are preparing for your first CAIO role or stepping into broader AI leadership, AAISM helps you lead with clarity, compliance, and confidence.

Enroll in AAISM by InfosecTrain today and future-proof your AI leadership journey. With the right knowledge, mindset, and tools, you will not just ace the interview. You will lead your organization into the future of secure, ethical, and strategic AI.

TRAINING CALENDAR of Upcoming Batches For Advanced in AI Security Management (AAISM) Certification Training

Start Date	End Date	Start - End Time	Batch Type	Training Mode	Batch Status
16-May-2026	14-Jun-2026	09:00 - 12:00 IST	Weekend	Online	[ Open ]