IBM Watsonx L1 Quix

Question: A client is struggling with delivering consistent customer care across all channels and touchpoints with their customers. What IBM offering do you recommend that can help the client provide an accurate and consistent customer experiences out of the box with no-code conversational AI features and large language model (LLM)-powered algorithms that achieve higher accuracy with less training.

Question: You have a client that is looking to modernize parts of their mainframe application stack, in particular some key COBOL business services. These services need regular updating, which is challenging given the declining number of software engineers with COBOL skills. What generative AI solution should you propose to your client to automate this modernization effort?

Question: You are working with a client that is interested in quickly driving real business results and outcomes with AI. What AI use cases can serve as ideal starting points for many companies?

Question: The watsonx platform makes it possible for enterprises to scale AI workloads using all their data with a fit-for-purpose data lakehouse service optimized for governed data and AI workloads, supported by querying, governance, and open data formats to access and share data. What is the name of this component of the watsonx AI and data platform?

Question: Clients are concerned that generative AI models will propagate established biases and hate, or unethical behavior, which could be very damaging to their company, not to mention have legal ramifications. IBM knows that AI models must be architected with governance in mind from the start, not as an afterthought. For responsible AI, the objective is to provide transparency and explainability for models and to support increasing regulatory compliance demands. What is the IBM core principle that underpins this?

Question: Your client wants to know how IBM's foundation models (like Granite) are different from platforms like ChatGPT, which have been built for general consumption and trained on huge amounts of Internet-supplied data. How do you answer?

Question: The watsonx platform has an enterprise-ready AI toolset to train, validate, tune, and deploy both machine learning AI models as well as foundation models for generative AI. These models combine best-of-breed architectures with a rigorous focus on data acquisition, provenance, and quality, to serve enterprise needs. What is the name of this component of the watsonx AI and data platform?

Question: A client wants to know how foundation models are different from the traditional AI models they have already implemented in their business workflows. What is a key difference that you should share with this client?

Question: Clients have concerns about how data is being curated for foundation models. What information can you share with your client about how IBM curates data for developing its foundation models like Granite and Sandstone?

Question: You are meeting with the Human Resources manager to discuss how AI can enable their employees to quickly offload time-consuming work such as creating a job description, pulling a report, sourcing candidates, and more. What IBM AI offering would you recommend to this client?

Question: Many clients do not have a data scientist on staff. Which IBM AI offering allows organizations to design, deploy, and manage AI-powered virtual assistants without needing data science or developement technical skills?

Question: The watsonx platform enables responsible, transparent, and explainable data and AI workflows by providing an end-to-end solution that encompasses both data and AI governance to enable responsible, transparent, and explainable AI workflows. What is the name of this component of the watsonx AI an

Question: The IBM AI Ethics Board is at the heart of the ethical decision-making that IBM applies to AI. What is the mission of the IBM AI Ethics Board?

Question: If a business needs to deliver accurate information, insights, or recommendations at scale, their systems cannot afford to contain errors, bias, misleading answers, or inaccurate results. Which one of the following answers represents IBM's "pillars of trust" for AI that can help businesses trust the AI-driven information, insights, or recommendations being given to them?

Question: IBM’s AI principles for trust and transparency are at work throughout its entire business. In fact, IBM is at the forefront of global efforts to hold AI to high ethical standards. How does IBM put these AI principles and pillars into practice?