C1000-185 無料問題集「IBM watsonx Generative AI Engineer - Associate」

（A）Increasing the batch size to reduce the number of gradient updates during training, thus speeding up the process.

（B）Adjusting the learning rate dynamically during training based on the model's real-time loss values.

（C）Using a fixed number of training epochs to ensure consistent results across tuning iterations.

（D）Reducing the number of layers in the model to decrease computational overhead.

（A）Embeddings are used to directly generate the textual responses in the output.

（B）Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.

（C）Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.

（D）Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.

（A）Soft prompts require more human intervention during generation because they depend on predefined patterns and rules for guidance.

（B）Soft prompts offer simpler debugging processes because the learned embeddings are directly linked to specific model behaviors and outputs.

（C）Soft prompts significantly limit the flexibility of the model because they are tied to specific tasks, unlike hard prompts which can generalize to various scenarios.

（D）Soft prompts introduce more complexity during the training phase, as the model must learn embeddings that are not inherently interpretable by humans.

（A）When you need to store a relatively small dataset (under 1,000 records) and can perform brute-force search without significant performance issues.

（B）When the data consists of text, images, or other unstructured content, and the goal is to retrieve items based on semantic similarity rather than exact matches.

（C）When the data is highly structured and queries are focused on exact matches like numeric ranges or specific dates.

（D）When you need to perform frequent joins and aggregations across multiple tables of structured data.

（A）Quantization-aware training (QAT)

（B）Post-training dynamic quantization

（C）Hybrid quantization

（D）Post-training static quantization

（A）Bag-of-Words (BoW)

（B）One-Hot Encoding

（C）Word2Vec

（D）Latent Dirichlet Allocation (LDA)

（A）Zero-shot prompting provides feedback to the model during inference, while few-shot does not allow model feedback.

（B）In zero-shot prompting, the model is fine-tuned before answering, but in few-shot prompting, no fine-tuning occurs.

（C）Zero-shot prompting does not provide any examples in the prompt, while few-shot prompting includes multiple task examples.

（D）Few-shot prompting requires a model to have pre-trained examples of the task, while zero-shot does not.

（A）Lowering the top-k value forces the model to generate shorter outputs by limiting the number of tokens available for selection.

（B）The model will now consider only the top 5 most probable tokens at each step, making the output more deterministic and focused.

（C）Reducing the top-k value reduces the model's ability to predict rare or uncommon words, leading to less accurate outputs.

（D）Reducing the top-k value increases the temperature of the model, making the outputs more creative and diverse.

（A）Increasing the temperature makes the model generate more deterministic responses by always selecting the most probable token at each step.

（B）Raising the temperature encourages the model to consider less likely tokens, leading to more diverse and creative outputs.

（C）A higher temperature setting reduces the length of the generated output by limiting the number of tokens in each response.

（D）Raising the temperature makes the model more likely to repeat tokens, reducing variability in its responses.

（A）Integrate IBM watsonx Assistant to handle multi-channel inputs and orchestrate LLM responses, while using IBM Event Streams to handle real-time scalability across channels.

（B）Use IBM watsonx's Generative AI API and directly integrate it with the application via REST, ensuring the LLM receives real-time data from each channel.

（C）Employ IBM watsonx's Data Refinery tool to preprocess incoming data from each channel and orchestrate data flow through Apache Kafka for real-time processing.

（D）Directly implement IBM watsonx Machine Learning models into each communication channel to ensure low-latency interactions with the LLM.

（A）Implementing role-based access control (RBAC) to restrict who can interact with the model.

（B）Regularly retraining the model to avoid performance degradation due to data drift.

（C）Ensuring model version control to track changes and updates made to the model during the deployment process.

（D）Using AI explainability techniques to make the model's decisions transparent to regulators and customers.

（A）Prompt Lab allows users to experiment with prompt structures, such as adjusting token limits or adding contextual instructions, to improve responses.

（B）Users can apply real-time machine learning to modify the underlying model parameters within Prompt Lab.

（C）Users can disable the model's access to certain pre-trained knowledge domains within Prompt Lab to focus its output on specific areas.

（D）Users can use Prompt Lab to train the AI model on new datasets and retrain it based on prompt performance.

（A）Using temperature scaling to increase randomness and reduce token usage.

（B）Revising the prompt to make it more specific by narrowing the scope of expected responses.

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

（D）Reducing the token limit in the model configuration to restrict the length of responses.

（A）Modify the model to use IBM's proprietary transformer architecture, as third-party architectures are not supported by Watsonx.

（B）Ensure that the model's training data is in a proprietary IBM format, as only Watsonx-specific formats are supported for custom model deployments.

（C）Set up auto-scaling in the IBM Watsonx environment to handle large numbers of simultaneous model inference requests.

（D）Containerize the model using Docker or an equivalent containerization tool, ensuring that all required dependencies, such as transformers, tokenizers, and necessary packages, are included.

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

（B）It automates the process of cleaning and preprocessing the input data before model training.

（C）It enables the creation of new datasets by generating synthetic data based on prompts.

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

（A）Set the maximum token limit to 700 and the minimum token limit to 600 to ensure a well-rounded summary with all necessary sections and detailed information included.

（B）Set the maximum token limit to 300 and the minimum token limit to 0, allowing the model to generate a brief summary of the most relevant points while avoiding excessive verbosity.

（C）Set both the minimum and maximum token limits to 1,000 to ensure the entire report is captured in detail, with no important information left out.

（D）Set the maximum token limit to 500 and the minimum token limit to 250 to ensure a balanced summary of key sections with some detailed

（A）Prompt Lab enables you to train the model with new data, ensuring continuous learning and improved performance over time.

（B）Prompt Lab offers pre-trained prompts specific to industry verticals, making it unnecessary to create customized prompts.

（C）Prompt Lab provides an environment to experiment with different prompt structures and analyze their impact on model outputs in real-time, helping optimize responses.

（D）Using Prompt Lab guarantees that the model will never produce biased responses, regardless of the input data used.

（A）Set the learning rate to its maximum value to speed up the tuning process and reduce experimentation time.

（B）All hyperparameters should be fixed at the default settings to ensure consistency across different experiments.

（C）Manually adjust one hyperparameter at a time while keeping all other parameters constant to precisely identify its impact on performance.

（D）Use automated hyperparameter search techniques such as grid search or random search to explore multiple configurations efficiently.

（A）The model performs well on general datasets but fails to capture specific domain-related terminology and context.

（B）The model's accuracy is already above 90%, but you want to achieve 95% accuracy for your task.

（C）You are noticing that the model occasionally makes grammar mistakes in the generated text.

（D）The model's inference time is longer than expected, and you need to reduce latency for real-time applications.

（A）Apply a simple word-level blacklist filter to detect and remove harmful content from the model output.

（B）Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter to dynamically detect and block harmful content at inference time.

（C）Fine-tune the model with a highly curated dataset that contains labeled examples of hate speech, abuse, and profanity for the model to learn to avoid.

（D）Train the model only on data that excludes all user-generated content to prevent exposure to harmful language.