01. In the context of developing a Retrieval-Augmented Generation (RAG) system, what is the function of an embedding model?
a) The embedding model visualizes the retrieved documents in a multidimensional space for better comprehension.
b) The embedding model generates output text based on the retrieved documents and represents them in a multidimensional vector space.
c) The embedding model is used to translate the input text into the desired target language before the retrieval operation.
d) The embedding model converts input queries into numerical representations to facilitate similarity matching during the retrieval process.
02. What does the Top K parameter control in the sampling decoding process?
a) The number of highest probability tokens to consider for sampling.
b) The cumulative probability threshold for sampling tokens.
c) The random seed used to start the random number generator.
d) The maximum temperature value for token sampling.
03. Which statement describes an AI agent, as compared to a traditional generative AI application?
a) It ensures the AI system only works in strictly pre-programmed workflows.
b) It focuses exclusively on multimodal input (text, image, audio) and matching output.
c) It is a system where the model can plan, take actions, and interact with tools to achieve goals.
d) It distributes LLM inferencing across servers to optimize for generative AI applications on edge devices.
04. In Data Refinery, which two charts show the frequency distribution of data?
a) Bubble
b) Histogram
c) Scatter plot
d) Circle packing
e) Population pyramid
05. When using the API client to manage generative AI assets, what is the function of the following statement:
client.set.default_project(project_id)
a) It initializes the API Client with the given project ID.
b) It defines the project ID as a global variable for all functions.
c) It automatically retrieves the project ID from an environment variable.
d) It sets the specified project ID as the default project for all subsequent API calls.
06. In watsonx.ai, how does increasing the repetition penalty impact the generated text?
a) The text becomes shorter and more concise.
b) The text becomes more diverse and less repetitive.
c) The text becomes more formal and less conversational.
d) The text becomes more focused and less prone to going off-topic.
07. How are complex AI tasks handled in Agentic AI RAG workflows?
a) All tasks are executed simultaneously without segmentation
b) Workflows are restricted to predefined, non-customizable steps
c) Tasks are routed directly to external tools without intermediate coordination
d) Complex tasks are broken down into smaller, manageable steps for coordinated execution
08. Which statement is correct about the config.json file in the foundation model content folder?
a) It is required to load the model in the Text Generation Inference Server (TGIS) runtime.
b) It is required to store the model tensors safely.
c) It is required to activate the model’s Tokenizer.encode or Tokenizer.decode.
d) The config.json is not required.
09. How does model quantization reduce compute resources and increase the speed of inferences?
a) By reducing the precision of the input vectors.
b) By reducing the precision of the model weights.
c) By increasing the precision of the model weights.
d) By increasing the precision of the input vectors.
10. Which machine learning approach divides an AI model into subnetworks, each specializing in a subset of the input data to collaboratively perform a task?
a) Transformer model
b) Variational autoencoders
c) Multi-Point Crossover
d) Mixture of experts
The purpose of this Sample Question Set is to provide you with information about the IBM watsonx Generative AI Engineer - Associate exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the C1000-185 certification test. To get familiar with real exam environment, we suggest you try our Sample IBM watsonx Generative AI Engineer Associate Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual IBM Certified watsonx Generative AI Engineer - Associate certification exam.