1. What are the Gemini 1.5 models in Vertex AI?
2. How can generative AI be brought to real-world experiences using Google Cloud?
3. What benefits do new customers get when starting their AI journey with Google Cloud?
4. What tools does Google Cloud offer to make it easier for developers to build with generative AI?
5. How can developers interact with, customize, and embed foundation models into their applications using Vertex AI?
6. What is the Vertex AI Agent Builder, and how can it help developers?
7. What updates have been made to Gemini, Imagen, Gemma, and MLOps on Vertex AI?
8. How can developers customize and deploy Gemini models to production in Vertex AI?
9. What capabilities does the Gemini multimodal model from Google DeepMind have?
10. How can developers prompt and test Gemini in Vertex AI using different types of input?
11. What is the Vertex AI Gemini API Quickstart?
12. How can developers kickstart their AI journey with Google Cloud's 10-step plan?
13. What is Gemini Code Assist, and how does it help developers?
14. What additional offerings does Gemini for Google Cloud provide to assist users?
15. How can businesses explore Google Cloud's open and innovative AI partner ecosystem?
16. How is generative AI transforming businesses and industries according to Google Cloud?
17. What are some customer success stories involving generative AI on Google Cloud?
18. How can generative AI transform customer service, enhance employee productivity, and automate business processes?
19. What are some business use cases for generative AI provided by Google Cloud?
20. What resources are available for developers to learn about generative AI on Google Cloud?
21. How can industry leaders leverage generative AI to transform their businesses?
22. What consulting services does Google Cloud offer for creating with generative AI?
23. How can businesses start their AI journey with Google Cloud AI and machine learning products?
24. How can developers join the Google Cloud technical community to build their AI skills?
25. What are the key benefits of using Google Cloud for digital transformation?
26. What industry solutions does Google Cloud offer for various sectors like retail, financial services, and healthcare?
27. How can businesses modernize their applications and software delivery with Google Cloud?
28. What AI and machine learning products does Google Cloud offer?
29. How can businesses use Google Cloud's APIs and applications to speed up innovation?
30. What data analytics solutions does Google Cloud provide for generating insights from data?
31. How can businesses migrate and manage their enterprise data with Google Cloud's database services?
32. What infrastructure modernization solutions does Google Cloud offer for SAP, VMware, and other workloads?
33. How can businesses enhance productivity and collaboration with Google Cloud's tools?
34. What security solutions does Google Cloud provide to protect businesses from online threats?
35. How can startups and SMBs accelerate their growth with Google Cloud's tailored solutions and programs?
36. What are some featured products offered by Google Cloud for AI, machine learning, and other needs?
37. How can businesses save money with Google Cloud's transparent pricing approach?
38. What learning and building resources does Google Cloud offer for developers and businesses?
39. How can businesses contact Google Cloud sales or find a partner for their needs?
40. What are the key benefits of choosing Google Cloud for digital transformation?

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2. What do I do when I finish this course?
3. What badges can I earn?
4. Interested in taking this course with one of our authorized on-demand partners?
5. Prefer learning with an instructor?
6. Can I take this course for free?

1. How does generative AI work?
2. What is generative AI?
3. What generative AI offerings does Google Cloud have?
4. How businesses use generative AI?
5. How can generative AI improve customer interactions?
6. How can generative AI assist with repetitive tasks?
7. How can generative AI support customer service agents?
8. How can generative AI improve developer efficiency?
9. How can generative AI help marketers?
10. How can generative AI transform website and mobile app experiences?
11. What are some common generative AI applications?
12. How can new customers get started with Google Cloud AI products?
13. What is Vertex AI?
14. What is the Vertex AI Agent Builder?
15. What is Gemini for Google Cloud?
16. How can businesses implement generative AI solutions with Google Cloud?
17. What is Google’s approach to responsible AI?
18. How can generative AI enhance content and improve navigation on websites?
19. How can generative AI drive developer productivity with code assistance?
20. How can generative AI supercharge creativity, productivity, and impact at scale for marketing?
21. How can generative AI unlock business value across the consumer journey?
22. How can generative AI improve live customer service experiences?
23. What are the benefits of using Google Cloud's generative AI for customer service?
24. How can generative AI help with summarizing large documents?
25. How can generative AI assist in creating new product concepts and designs?
26. How can generative AI help in localizing and translating content?
27. How can generative AI be used to auto-generate FAQ responses?
28. How can generative AI enhance search experiences on websites?
29. How can generative AI help in synthesizing data to understand customer profiles?
30. How can generative AI assist in generating content to reach target customers?

1. What is Generative AI?
2. How is Generative AI used?
3. How does Generative AI differ from traditional machine learning methods?
4. What Google tools can help you develop your own Generative AI?
5. What are large language models (LLM)?
6. In what use cases can large language models be utilized?
7. How can you use prompt tuning to enhance LLM performance?
8. What is responsible AI?
9. Why is responsible AI important?
10. How does Google implement responsible AI in their products?
11. What are Google's 7 AI principles?
12. What skills are demonstrated by completing the Prompt Design in Vertex AI skill badge?
13. How can you craft effective prompts and guide generative AI output in Vertex AI?
14. What is the course "Responsible AI: Applying AI Principles with Google Cloud - Locales" intended for?
15. Where can you enroll in the English version of the course "Responsible AI: Applying AI Principles with Google Cloud"?

1. What is Vertex AI used for?
2. Why use Vertex AI Platform?
3. Is Google Cloud's Vertex AI free?
4. How do I get access to Gemini models in Vertex AI?
5. What are the features of Vertex AI with Gemini 1.5 Pro and Gemini 1.5 Flash?
6. How can new customers try Vertex AI for free?
7. What are the product highlights of Vertex AI?
8. What are the capabilities of Gemini models in Vertex AI?
9. How can developers use Gemini models in Vertex AI?
10. What is the Vertex AI Gemini API quickstart?
11. What generative AI models and tools are available in Vertex AI?
12. How does Vertex AI integrate with BigQuery?
13. What tools does Vertex AI Platform provide for MLOps?
14. What is Vertex AI Agent Builder?
15. What AI solutions are built on top of Vertex AI Platform?
16. How does Vertex AI support model training and deployment?
17. How can you access Gemini models via the Gemini API in Google Cloud Vertex AI?
18. What are the best practices for multimodal design in Vertex AI?
19. How can you use generative AI for summarization, classification, and extraction in Vertex AI?
20. What are the benefits of custom ML training in Vertex AI?
21. How can you train and create ML models with minimal technical expertise using Vertex AI?
22. How can you deploy a model for production use in Vertex AI?
23. How does Vertex AI pricing work?
24. What are the pricing details for different Vertex AI features and services?
25. How can you estimate your Vertex AI costs?
26. How can you start a proof of concept with Vertex AI?
27. How can you browse, customize, and deploy machine learning models in Vertex AI?
28. What are the benefits of using Vertex AI for businesses?
29. How do other businesses use Vertex AI to accelerate the delivery of ML models and applications?
30. What are the featured benefits of Vertex AI?
31. How can you get a custom quote for Vertex AI?
32. How can you get started with notebooks for machine learning in Vertex AI?
33. What are the common uses of Vertex AI?
34. How can you learn multimodal design best practices in Vertex AI?
35. How can you get hands-on with a Vertex AI Predictions codelab?
36. How can you simplify model serving with custom prediction routines in Vertex AI?
37. How can you use prebuilt containers for prediction and explanation in Vertex AI?
38. What are the steps required to train custom models on Vertex AI?
39. How can you accelerate research and discovery with generative AI in Vertex AI?
40. How can you use Vertex AI to build generative AI applications quickly?

1. What are the pressing questions for banking institutions regarding the use of generative AI?
2. How much value could generative AI add annually across various industries, according to the McKinsey Global Institute?
3. What is the estimated annual potential value of generative AI for the banking sector?
4. How are banks currently using generative AI to improve customer service and productivity?
5. What are some specific examples of generative AI applications in banking?
6. What are the key factors that make scaling generative AI different from other technologies in banking?
7. How does generative AI complicate the operating dynamics within financial institutions?
8. Why is the pace of change with generative AI adoption faster compared to previous technologies like smartphones?
9. What unique talent-related challenges do banks face when scaling up generative AI?
10. What are the seven dimensions critical for a successful generative AI scale-up in banking?
11. How should banks approach the strategic road map for scaling generative AI?
12. What role does senior leadership play in the successful implementation of generative AI in banks?
13. How can banks address the talent gap and upskill employees for generative AI?
14. Why is a cross-functional operating model important for scaling generative AI in banks?
15. What are the considerations for banks when deciding between building, buying, or partnering for generative AI solutions?
16. How does generative AI's reliance on unstructured data add complexity to banks' data strategies?
17. What are the unique risks associated with generative AI in banking?
18. How can banks manage the risks of generative AI "hallucinations"?
19. Why is change management crucial for the adoption of generative AI in banks?
20. What are the potential benefits of generative AI for banks and financial institutions?

1. What opportunities does generative AI offer in the banking industry?
2. How can the right operating model help banks unlock the potential of generative AI?
3. What are some of the tasks that generative AI can speed up in financial institutions?
4. How much value could generative AI add to the global banking sector annually, according to the McKinsey Global Institute?
5. What are some of the risks associated with implementing generative AI in banking?
6. What are the seven dimensions required for achieving sustained value from generative AI in banking?
7. Why is an operating model important for implementing generative AI in financial institutions?
8. What are the benefits of a centrally led generative AI operating model in banking?
9. What are the potential challenges of a highly centralized generative AI operating model?
10. How can financial institutions tailor their generative AI operating model to their own structure and culture?
11. What are the four organizational archetypes for generative AI operating models in banking?
12. What are the potential benefits and challenges of a "centrally led, business unit executed" generative AI operating model?
13. What are the potential benefits and challenges of a "business unit led, centrally supported" generative AI operating model?
14. What are the potential benefits and challenges of a highly decentralized generative AI operating model?
15. Why do financial institutions with centralized generative AI operating models appear to be ahead in implementing use cases?
16. What are some of the main obstacles to implementing a centralized generative AI operating model?
17. How have financial-services companies managed the transition to generative AI?
18. What might cause the operating model for generative AI to swing toward a more federated design in the future?
19. What are some essential decisions financial institutions need to consider when choosing a generative AI operating model?
20. How should financial institutions define their generative AI strategy and vision?
21. Who should determine the enterprise domains and use cases for generative AI in financial institutions?
22. What are the different deployment models for implementing generative AI use cases?
23. How should financial institutions fund generative AI use cases?
24. What skills are needed for generative AI initiatives in financial institutions?
25. Who should define risk guardrails and mitigation strategies for generative AI in financial institutions?
26. What role does change management play in the successful adoption of generative AI across an enterprise?
27. Why is it important for financial institutions to have the right generative AI operating model in place?
28. How can financial institutions balance speed and innovation with risk when implementing generative AI?
29. What strategies can financial institutions use to align their generative AI initiatives with strategic goals for maximum impact?
30. What are some related articles that explore the value and implementation of generative AI in banking?