Free C1000-185 Questions for IBM C1000-185 Exam

Question #1

You are tuning a generative AI model to control the length of the generated responses.
Which of the following parameter configurations will ensure that the model generates responses that are at least 50 tokens long but no longer than 150 tokens?

A. Setting no minimum token value but configuring the maximum tokens to 150

B. Setting the minimum tokens to 50 and maximum tokens to 150

C. Setting the maximum tokens to 50 and minimum tokens to 150

D. Setting the minimum tokens to 150 and maximum tokens to 50

Discussion 0

Correct Answer: B Vote an answer

Question #2

When working with IBM Watsonx Generative AI models, it's important to configure proper stopping criteria to control when the model should terminate the text generation process. You are developing a chatbot where responses should stay within a manageable length without losing coherence.
Which configuration best represents an effective stopping criterion to ensure coherent responses without abrupt truncation?

A. Greedy decoding with maximum tokens set to 20 and a stop sequence of "END".

B. Greedy decoding with no stop sequence and maximum tokens set to 200.

C. Beam search decoding with a stop sequence of "END" and a maximum tokens limit of 50.

D. Greedy decoding with temperature set to 2.0 and no stop sequence.

Discussion 0

Correct Answer: C Vote an answer

Question #3

A business wants to deploy a customer service chatbot using IBM watsonx, integrated with multiple back-end systems including ERP, CRM, and a payment gateway. To manage this complex integration, the chatbot should dynamically switch between these systems based on the customer's intent.
Which architecture best supports this requirement while ensuring scalability and minimal orchestration overhead?

A. Set up IBM watsonx to queue customer interactions and process them sequentially using a single system at a time.

B. Use synchronous REST APIs for all back-end systems, relying on watsonx's built-in intent recognition to manage API routing.

C. Utilize IBM watsonx's API orchestration layer to dynamically route requests to the correct system based on intent analysis.

D. Implement a monolithic architecture where all system interactions are handled within a single service.

Discussion 0

Correct Answer: C Vote an answer

Question #4

You are tasked with reconstructing a prompt used in an AI-based customer support chatbot. The current prompt generates lengthy, detailed answers that are often overly verbose and unnecessary for the customer's inquiries. Your objective is to optimize this prompt to reduce model usage costs without compromising the quality of the responses.
Which of the following strategies is the most effective in reducing the cost of using a Generative AI model while maintaining response relevance and clarity?

A. Splitting the prompt into multiple sub-prompts to generate responses for different sections separately.

B. Reducing the token limit in the model configuration to restrict the length of responses.

C. Revising the prompt to make it more specific by narrowing the scope of expected responses.

D. Using temperature scaling to increase randomness and reduce token usage.

Discussion 0

Correct Answer: C Vote an answer

Question #5

You are generating product descriptions for an online marketplace using a generative AI model. The output is coherent but tends to repeat the same phrases and words excessively. You decide to apply a repetition penalty to reduce this repetition while keeping the temperature set to a value that maintains creativity in the text generation.
Which of the following adjustments would best achieve this goal?

A. Set repetition penalty to 1.5 and maintain temperature at 0.8

B. Set repetition penalty to 0.0

C. Set repetition penalty to 1.0 and decrease temperature from 0.8 to 0.3

D. Set repetition penalty to 2.0 and increase temperature from 0.7 to 1.2

Discussion 0

Correct Answer: A Vote an answer

Question #6

Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.
What steps should you take to mitigate both the risks of data bias and poisoning?

A. Fine-tune the model using only the training data from trusted sources, without expanding the dataset.

B. Apply prompt engineering techniques to avoid triggering known biases in the model.

C. Implement a data validation pipeline to detect anomalies and potential poisoning in the training data.

D. Use adversarial training techniques to make the model more robust against bias and poisoning.

Discussion 0

Correct Answer: C Vote an answer

Question #7

When conducting prompt engineering to reduce model risks related to hate speech and abusive content, which of the following strategies is least likely to be effective?

A. Increasing the temperature during text generation to encourage more creative outputs

B. Applying ethical guidelines during the fine-tuning of the model to prioritize inclusive language

C. Creating a reward model during Reinforcement Learning to penalize hate speech outputs

D. Modifying the prompt to include explicit instructions for civil language

Discussion 0

Correct Answer: A Vote an answer

Question #8

When using IBM Watsonx Tuning Studio, what is the recommended approach to determining the number of training data examples required for effective model fine-tuning?

A. Use at least 10,000 examples for each unique task to ensure the model retains its general knowledge and effectively adapts to the new task.

B. Use at least 50% of the original training data to ensure the fine-tuned model generalizes well across both new and existing tasks.

C. Use no more than 100 examples per task to avoid overwhelming the model's general capabilities with task-specific data.

D. Use a minimum of 1,000 to 5,000 examples for each task, but focus on the quality and relevance of examples rather than quantity.

Discussion 0

Correct Answer: D Vote an answer

Question #9

You are implementing a few-shot prompting strategy with IBM Watsonx to improve the model's performance in generating customer service responses. The goal is to ensure the model understands the tone and format required for polite and concise replies.
Which of the following strategies best illustrates the correct way to use few-shot prompting?

A. Provide one or two well-structured examples that demonstrate the expected tone and format of the customer service responses within the prompt.

B. Use only negative examples in the prompt to show the model what not to generate in terms of tone and format.

C. Provide example prompts with multiple different output styles to give the model a range of responses to choose from.

D. Include a large number of examples, typically over 10, in the input prompt to ensure the model learns from diverse cases.

Discussion 0

Correct Answer: A Vote an answer

Question #10

You are designing a Retrieval-Augmented Generation (RAG) model within IBM watsonx to assist in generating responses for a customer service chatbot. The model needs to leverage a knowledge base (KB) of articles to enhance the accuracy of responses.
Which of the following correctly describes how the RAG model can be implemented to achieve this goal?

A. The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.

B. The RAG model first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.

C. The RAG model first generates an intermediate query based on the input question, which is then passed through a retrieval system to generate the final response.

D. The RAG model retrieves relevant knowledge from the knowledge base using a search or retrieval mechanism before generating a response, which is then influenced by this retrieved information.

Discussion 0

Correct Answer: D Vote an answer

Question #11

You are tasked with preparing a dataset for training a machine learning model using IBM Watsonx. The dataset contains over 1 million rows, and you notice a significant imbalance in the distribution of class labels.
To optimize model performance and minimize bias, what would be the best next step in addressing this imbalance?

A. Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic data for the minority class.

B. Randomly shuffle the data to improve the model's exposure to different instances during training.

C. Remove the majority class instances to balance the dataset.

D. Increase the learning rate of the model to improve its ability to learn from the imbalanced data.

Discussion 0

Correct Answer: A Vote an answer

Free C1000-185 Questions for IBM watsonx Generative AI Engineer - Associate C1000-185 Exam as PDF & Practice Test Engine

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