IBM watsonx Generative AI Engineer - Associate - C1000-185 Exam Practice Test

A. Configuring LlamaIndex to perform real-time indexing of incoming data streams to ensure the most up-to-date documents are always retrieved.

B. Adjusting LlamaIndex to prioritize documents that contain certain predefined keywords, optimizing retrieval for specific business terms.

C. Setting LlamaIndex to index only a small subset of the documents to minimize the time it takes to retrieve any relevant document.

D. Ensuring LlamaIndex generates unique embedding vectors for each document and storing them in an optimized vector database for quick lookups.

A. Greedy decoding is highly effective when multiple equally probable tokens are available at each step

B. Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence

C. Greedy decoding guarantees the highest overall probability for the output sequence

D. Greedy decoding works best when combined with temperature scaling to increase randomness

A. Use a private cloud with role-based access controls (RBAC) and ensure model activity is logged for auditing purposes.

B. Deploy the model using a serverless architecture to minimize operational overhead and maintain compliance.

C. Deploy the model on a hybrid cloud, with inference done on the client's on-premise servers and training done in the public cloud.

D. Deploy the model on a public cloud with built-in encryption and use APIs to connect to the client's private data.

A. It allows you to manually edit the output tokens to ensure correctness.

B. It provides automated fine-tuning of the model's hyperparameters to improve performance on domain-specific tasks.

C. It automates the process of cleaning and preprocessing the input data before model training.

D. It enables the creation of new datasets by generating synthetic data based on prompts.

A. Temperature = 0.2

B. Temperature = 0.0

C. Temperature = 2.0

D. Temperature = 0.7

A. Post-training quantization is more resource-efficient than quantization-aware training, as it applies quantization after the model has been fully trained, eliminating the need for additional fine-tuning.

B. Quantizing a model to 8-bit precision always results in a significant loss in performance, especially when working with language models or large generative AI architectures.

C. Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.

D. Quantization reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.

E. Quantization can increase the inference time of a model since it adds computational complexity when converting from higher to lower precision formats during runtime.

A. They enable the AI to better predict the intent of the customer query, reducing the need for explicit customer input.

B. They ensure that the AI generates responses with consistent tone and personality across all prompts, regardless of product category.

C. They automatically improve the accuracy of the AI's responses by allowing the system to learn from each generated prompt.

D. They allow for dynamic input fields in prompts, making it easier to tailor responses for different product categories without rewriting the entire prompt.

A. Bag-of-Words (BoW)

B. Latent Dirichlet Allocation (LDA)

C. One-Hot Encoding

D. Word2Vec

A. "Guide the user through troubleshooting software installation issues, starting from common problems like insufficient disk space and incompatible OS."

B. "Please list all the possible errors that occur during software installation."

C. "Tell the user what software installation is and how it works."

D. "Can you tell the user about software installation?"

A. Top-p sampling selects the next token only from a fixed number of top tokens, ensuring the most predictable outcome.

B. Top-p sampling prioritizes tokens based on their frequency in the training dataset, ignoring their conditional probabilities.

C. Top-p sampling adjusts the probability threshold dynamically, allowing the model to sample tokens until the cumulative probability reaches p.

D. Top-p sampling ensures that only the top 1% most probable tokens are chosen, regardless of context.

A. Employing low learning rates to prevent the model from adapting too quickly to new prompts.

B. Ensuring that the prompt length does not exceed the model's input token limit.

C. Matching the training data prompts as closely as possible to real-world deployment scenarios.

D. Using a diverse set of unrelated tasks in the tuning dataset to increase model generalization.