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Guide for IBM watsonx Generative AI Engineer - Associate

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Quiz

1/10
In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that
needs to classify customer support tickets into different categories. You decide to experiment with both
zero-shot and few-shot prompting techniques. Which of the following best explains the key difference
between zero-shot and few-shot prompting?
Select the answer
1 correct answer
A.
Zero-shot prompting does not use any examples in the input prompt, while few-shot prompting includes a few examples to guide the model.
B.
Zero-shot prompting provides the model with a few example tasks to help it understand the problem, while few-shot prompting provides no examples at all.
C.
In zero-shot prompting, the model learns from a large number of examples during the inference stage, while in few-shot prompting, only a single example is used.
D.
Few-shot prompting is used only for training the model, while zero-shot prompting is used only for inference tasks.

Quiz

2/10
In prompt engineering, prompt variables are used to make your prompts more dynamic and reusable.
Which of the following statements best describes a key benefit of using prompt variables in IBM Watsonx
Generative AI?
Select the answer
1 correct answer
A.
Prompt variables eliminate the need to change model parameters every time you generate a new response.
B.
Prompt variables automatically improve the accuracy of responses by reducing model variance.
C.
Prompt variables ensure that the AI's response format will always be consistent, regardless of the input data.
D.
Prompt variables allow a single prompt template to handle multiple data points or scenarios by inserting different values.

Quiz

3/10
You are working on a project where the AI model needs to generate personalized customer support
responses based on various input fields like customer name, issue type, and product details. To make
the system scalable and flexible, you decide to use prompt variables in your implementation. Which of
the following statements accurately describe the benefits of using prompt variables in this scenario?
(Select two)
Select the answer
2 correct answers
A.
Prompt variables improve the model's performance by optimizing its internal architecture, reducing computation time for each request.
B.
Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability.
C.
Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task-specific prompts.
D.
Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow on- the-fly customization of responses.
E.
Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time-consuming.

Quiz

4/10
You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to
use either a zero-shot or a few-shot prompting technique with an IBM Watsonx model. Which of the
following statements best describes the key difference between zero-shot and few-shot prompting?
Select the answer
1 correct answer
A.
Zero-shot prompting provides the model with examples, while few-shot prompting does not.
B.
Zero-shot prompting requires no examples in the prompt, while few-shot prompting provides the model with one or more examples to clarify the task.
C.
Few-shot prompting is used when the model is trained on supervised learning, while zero-shot prompting works only with unsupervised models.
D.
Zero-shot prompting requires retraining the model with additional data, while few-shot prompting uses a pre-trained model without retraining.

Quiz

5/10
You are building a chatbot using a generative AI model for a medical advice platform. During testing, you
notice that the model occasionally generates medical information that contradicts established guidelines.
This is an example of a model hallucination. Which prompt engineering technique would best mitigate
the risk of hallucination in this scenario?
Select the answer
1 correct answer
A.
Implementing zero-shot learning techniques
B.
Providing a list of credible sources in the prompt
C.
Using more open-ended prompts
D.
Increasing the model's temperature parameter

Quiz

6/10
Your team has developed an AI model that generates automated legal documents based on user inputs.
The client, a large law firm, wants to deploy this model but has stringent security, compliance, and
auditability requirements due to the sensitive nature of the data. What is the most appropriate
deployment strategy to meet these specific requirements?
Select the answer
1 correct answer
A.
Deploy the model on a hybrid cloud, with inference done on the client’s on-premise servers and training done in the public cloud.
B.
Deploy the model on a public cloud with built-in encryption and use APIs to connect to the client’s private data.
C.
Deploy the model using a serverless architecture to minimize operational overhead and maintain compliance.
D.
Use a private cloud with role-based access controls (RBAC) and ensure model activity is logged for auditing purposes.

Quiz

7/10
Your team is responsible for deploying a generative AI system that will interact with customers through
automated chatbots. To improve the quality and consistency of responses across different queries and
customer profiles, the team has developed several prompt templates. These templates aim to
standardize input to the model, ensuring that outputs are aligned with business objectives. However, the
team is debating whether using these prompt templates will provide tangible benefits in the deployment.
What is the primary benefit of deploying prompt templates in this AI system?
Select the answer
1 correct answer
A.
Reducing the overall inference time by streamlining the input-output process for the model, ensuring faster responses.
B.
Improving the scalability of the system by allowing the model to handle more diverse inputs without requiring additional fine-tuning.
C.
Enhancing the model’s ability to generalize across unseen data by training it specifically on the variations included in the prompt template.
D.
Enabling more predictable and consistent outputs across different inputs, aligning the model's responses more closely with the business goals.

Quiz

8/10
You have applied a set of prompt tuning parameters to a language model and collected the following
statistics: ROUGE-L score, BLEU score, and memory utilization. Based on these metrics, how would you
prioritize further optimizations to balance the model’s performance in terms of output relevance and
resource efficiency?
Select the answer
1 correct answer
A.
Maximize BLEU score and reduce memory utilization
B.
Reduce memory utilization and maintain BLEU and ROUGE-L scores
C.
Focus on improving the ROUGE-L score while increasing memory utilization
D.
Increase memory utilization to reduce BLEU and ROUGE-L scores

Quiz

9/10
You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a
generative model. The RAG model needs to leverage a document corpus to generate answers to
complex questions. Which of the following steps is critical in the RAG pipeline to ensure accurate and
relevant answer generation?
Select the answer
1 correct answer
A.
Fine-tuning the generative model on the entire document corpus without retrieval components.
B.
Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.
C.
Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
D.
Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.

Quiz

10/10
You are tasked with designing a prompt to fine-tune an IBM Watsonx model to summarize legal
documents. The summaries must include only factual information, highlight key legal terms, and exclude
any personal interpretations or subjective analysis. Which of the following is the best prompt to achieve
this goal?
Select the answer
1 correct answer
A.
"Generate a detailed and engaging summary of this legal document, adding your insights to clarify complex legal points for the reader."
B.
"Provide a summary of this legal document, focusing on factual information, including key legal terms and avoiding personal interpretation or subjective analysis."
C.
"Create a brief summary of this legal document, ensuring to exclude any legal jargon and simplifying the content for a layperson audience."
D.
"Summarize this legal document, focusing on key arguments and providing an analysis of the potential outcomes of the case."
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  • Quiz name:IBM watsonx Generative AI Engineer - Associate
  • Total number of questions:378
  • Number of questions for the test:50
  • Pass score:80%

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