C1000-185 시험 - IBM실제시험문제와 답 - 378문항

Question No : 1

You are building a question-answering system using a Retrieval-Augmented Generation (RAG) architecture. You are deciding whether to incorporate a vector database into the system to handle the document embeddings.
Under which of the following circumstances is the use of a vector database most appropriate?

A.When the corpus consists mainly of short, structured text like JSON records and traditional SQL indexing will suffice
B.When the data consists primarily of binary files such as images and videos, and full-text search is required
C.When real-time similarity search over high-dimensional embeddings is needed for large-scale unstructured text data
D.When the text corpus consists entirely of predefined categories that can be handled by simple keyword matching algorithms

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Question No : 2

When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?

A.Increasing the temperature parameter to 1.5
B.Decreasing the value of the temperature parameter to 0.2
C.Reducing the beam size in beam search from 5 to 1
D.Using Top-k Sampling with a k value of 100

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Question No : 3

You are working with a foundation model pre-trained on a large general-purpose dataset, and you plan to deploy it for a specialized task in healthcare-related text generation. However, before tuning the model, you want to assess whether tuning is necessary for your use case.
Which of the following is the best indicator that it is time to tune the foundation model for your task?

A.You are noticing that the model occasionally makes grammar mistakes in the generated text.
B.The model performs well on general datasets but fails to capture specific domain-related terminology and context.
C.The model's accuracy is already above 90%, but you want to achieve 95% accuracy for your task.
D.The model's inference time is longer than expected, and you need to reduce latency for real-time applications.

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Question No : 4

As a Generative AI engineer, you're tasked with optimizing the performance and cost-efficiency of a model by adjusting the model parameters.
Given that your objective is to reduce the cost of generation while maintaining acceptable quality, which of the following parameter changes is most likely to result in cost savings?

A.Set the temperature parameter to a higher value.
B.Increase the top-k sampling value.
C.Increase the max tokens parameter to allow for more complex output.
D.Decrease the max tokens parameter.

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Question No : 5

You are tasked with deploying a generative AI solution for a client who operates in the healthcare sector. Due to the sensitive nature of the data, the client requires a highly secure deployment with continuous monitoring for regulatory compliance.
Which role is primarily responsible for ensuring the AI solution is compliant with these security and regulatory requirements?

A.Security Engineer
B.Data Privacy Officer
C.AI Model Developer
D.Chief Technology Officer (CTO)

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Question No : 6

In the context of model quantization for generative AI, which of the following statements correctly describes the impact of quantization techniques on model performance and resource efficiency? (Select two)

A.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.
B.Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.
C.Quantization reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.
D.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.
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.

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Question No : 7

Which of the following techniques can be most effectively used to mitigate the generation of hate speech, abuse, and profanity in generative AI models when applying prompt engineering?

A.Applying Token Regularization to limit the diversity of generated responses
B.Fine-tuning the model with specific datasets curated to exclude offensive content
C.Restricting the model's ability to generate certain words or phrases using stop-word lists
D.Using Greedy Decoding to ensure that the model outputs the most likely sequence of tokens

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Question No : 8

In the context of analyzing prompt-tuning results, which statistical measure is most important to assess how well the tuned model generalizes to unseen data?

A.Validation loss
B.Training loss
C.Number of epochs completed
D.Accuracy on the training dataset

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Question No : 9

You are implementing a RAG system and have chosen LlamaIndex to handle the document indexing process. Your system needs to retrieve relevant documents quickly and efficiently for large datasets.
What is the most important function of LlamaIndex in managing document retrieval?

A.LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.
B.LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.
C.LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.
D.LlamaIndex compresses the documents and stores them in a traditional SQL database to improve retrieval speed.

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Question No : 10

You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.
Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

A.Fine-tune IBM InstructLab itself to generate data that closely resembles the training data format, ensuring consistent sentiment distribution.
B.Use IBM InstructLab to generate synthetic data, but only for neutral sentiment, as the model already handles positive and negative sentiment well.
C.Increase the diversity of synthetic data by focusing on outliers and rare sentiment cases that are underrepresented in the original dataset.
D.Use a generic prompt to generate a wide variety of data from IBM InstructLab, regardless of sentiment polarity.

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Question No : 11

You are tasked with optimizing a generative AI model’s output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

A.Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3
B.Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2
C.Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
D.Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6

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Question No : 12

You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A.Manually categorize each interaction and organize them into a taxonomy tree structure.
B.Perform a spelling correction on the entire dataset to remove any language inconsistencies.
C.Tokenize the dataset before curating it and mapping it to the taxonomy tree.
D.Convert all text to lowercase to ensure uniformity in the dataset.

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Question No : 13

A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.
Which of the following techniques would most effectively reduce this risk in a production environment?

A.Using human-in-the-loop (HITL) methods for real-time validation
B.Increasing the model's response diversity by adjusting top-p sampling
C.Switching to greedy decoding for more deterministic responses
D.Deploying rule-based post-processing filters to validate the output

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Question No : 14

You are tasked with deploying a versioned prompt for a customer-facing generative AI application. The prompts are iteratively improved based on feedback, and you need to ensure that each version of the prompt is tracked and accessible for rollback in case a newer version produces worse results.
Which strategy would best ensure that all prompt versions are stored and easily retrievable, while minimizing disruption to the current deployment?

A.Deploy prompts directly to production without versioning and manually track changes in a spreadsheet.
B.Leverage a cloud-based deployment pipeline with integrated versioning that automates prompt rollback and audit tracking.
C.Store each version of the prompt as a separate file in a local folder and manually deploy the desired version when needed.
D.Use a version control system like Git to track prompt changes and synchronize the latest version with the deployment environment.

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Question No : 15

Prompt Lab in IBM Watsonx Generative AI offers several advantages for AI prompt engineering.
Which of the following best describes a primary benefit of using the Prompt Lab feature?

A.It guarantees that all generated responses adhere to industry-specific regulatory standards.
B.It provides a collaborative environment where multiple users can co-author prompts in real time.
C.It allows users to design custom AI models from scratch to handle specific tasks.
D.It enables users to test different versions of prompts and receive immediate feedback on their effectiveness.

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IBM C1000-185 시험