IBM C1000-185 real exam prep : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with developing a system that uses a vector database to store embeddings generated from a large corpus of documents. The system should be able to perform fast and efficient nearest neighbor search while balancing accuracy and speed. Given the large volume of data and the need for scalability, you are considering different indexing strategies offered by vector databases.
Which of the following indexing techniques is the most appropriate for balancing search accuracy and speed in high-dimensional vector space, and why?

A) Approximate nearest neighbor (ANN) search using HNSW (Hierarchical Navigable Small World) graphs.
B) Rely on full-text indexing of documents and avoid vector search altogether.
C) Exact nearest neighbor (ENN) search using KD-trees.
D) Linear search over the entire vector dataset.

2. You have fine-tuned a model and notice several issues in the output, including repeated phrases, incomplete sentences, and factual inaccuracies.
Which of the following methods would best help you detect and resolve these data quality problems?

A) Use a higher learning rate to improve model convergence
B) Analyze token distribution patterns in the generated outputs
C) Apply model quantization to reduce the complexity of the output
D) Increase the number of training epochs to further refine the model

3. You are tasked with fine-tuning a large language model (LLM) to perform sentiment analysis on product reviews. The dataset contains customer reviews, but some reviews are very short, and others contain irrelevant data like product specifications or spam. You want to prepare the dataset for fine-tuning by ensuring the data is clean, relevant, and representative of the task at hand.
Which of the following steps is most critical to ensure the dataset is suitable for fine-tuning?

A) Increase the learning rate to adjust for inconsistencies in the dataset
B) Randomly remove 10% of the dataset to reduce noise
C) Increase the number of epochs to handle the noisy data
D) Filter the dataset to remove irrelevant or outlier reviews

4. You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

A) Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
B) Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
C) Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.
D) Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.

5. You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

A) Lower the model's perplexity score to improve both completeness and factual accuracy.
B) Increase the length of the input prompt to ensure that responses are more complete.
C) Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.
D) Fine-tune the model on domain-specific data to improve factual accuracy and relevance.

Solutions: