Oracle Cloud Generative AI Professional (1Z0-1127-24) Exam Questions 2025
Oracle Cloud Generative AI Professional (1Z0-1127-24) Exam Questions 2025
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Question 1:
What does the Ranker do in a text generation system?
A. It sources information from databases to use in text generation.
B. It generates the final text based on the users query.
C. It interacts with the user to understand the query better.
D. It evaluates and prioritizes the information retrieved by the Retriever.
Answer: D
Explanation:
The Role of the Ranker in Text Generation Systems
In modern text generation architectures, the Ranker serves a critical function in information evaluation and prioritization. Following the Retriever's initial data sourcing from large-scale corpora or databases, the Ranker performs sophisticated assessment of the retrieved content to determine:
Relevance to the user's specific query
Information quality and source credibility
Temporal relevance (recency)
Contextual appropriateness for the task
The Ranker employs advanced algorithms and multiple evaluation criteria to optimize content selection, ensuring the system delivers the most suitable information for generation tasks. This component is essential for maintaining output accuracy, reliability, and contextual alignment in AI-powered text generation systems.
Question 2:
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?
A. They cannot generate responses without fine-tuning.
B. They rely on internal knowledge learned during pretraining on a large text corpus.
C. They always use an external database for generating responses.
D. They use vector databases exclusively to produce answers.
Answer: B
Explanation:
Knowledge Limitations in Standalone Large Language Models
Large Language Models (LLMs) operating without Retrieval-Augmented Generation (RAG) capabilities depend exclusively on their pretrained knowledge base. These models acquire their understanding through extensive training on diverse text corpora, enabling them to:
• Develop sophisticated linguistic pattern recognition
• Internalize complex semantic relationships
• Master various language structures and syntax
While this training approach allows for impressive language generation capabilities, it inherently limits the model to information available during its training period, creating potential gaps in:
Current factual knowledge
Domain-specific updates
Emerging trends and terminology
Question 3:
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
A. Decreasing the temperature broadens the distribution, making less likely words more probable.
B. Increasing the temperature removes the impact of the most likely word.
C. Increasing the temperature flattens the distribution, allowing for more varied word choices.
D. Temperature has no effect on probability distribution; it only changes the speed of decoding.
Answer: C
Explanation:
The Impact of Temperature on LLM Output Diversity
Adjusting the temperature parameter directly influences the probability distribution of word selections. Higher temperature values produce a flatter distribution, effectively:
• Reducing the probability gap between high-likelihood and lower-likelihood tokens
• Increasing output variability and diversity
• Potentially enhancing creative or unconventional responses
This controlled randomization mechanism enables fine-tuning of model outputs along the spectrum from deterministic precision to exploratory generation.
Question 4:
In which scenario is soft prompting appropriate compared to other training styles?
A. When there is a significant amount of labeled, task-specific data available
B. When the model requires continued pretraining on unlabeled data
C. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
D. When the model needs to be adapted to perform well in a domain on which it was not originally trained
Answer: C
Explanation:
Soft Prompting: A Parameter-Efficient Adaptation Technique
Soft prompting is an advanced adaptation method that introduces trainable embedding parameters at the input layer while keeping the base model's weights frozen. This approach offers several key advantages:
Key Characteristics:
• Implements task-specific adaptation through learned embeddings rather than full model fine-tuning
• Preserves original model parameters, significantly reducing computational requirements
• Enables efficient adaptation of large pretrained models to new tasks
Comparative Use Cases:
For abundant labeled data: Traditional fine-tuning often yields superior performance
For domain adaptation: Either fine-tuning or prompt-based methods may be employed
For unlabeled data: Self-supervised pretraining remains the standard approach
Technical Note:
Soft prompting specifically refers to the optimization of continuous embedding parameters, distinct from other adaptation techniques that modify model weights or architecture.
Question 5:
What does the term “hallucination“ refer to in the context of Language Large Models (LLMs)?
A. The models ability to generate imaginative and creative content
B. The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
C. The process by which the model visualizes and describes images in detail
D. A technique used to enhance the model performance on specific tasks
Answer: B
Explanation:
Addressing Hallucination in Language Model Outputs
Hallucination refers to instances where a language model generates coherent but factually incorrect or irrelevant content that deviates from the provided context or objective truth. This phenomenon presents significant challenges in high-stakes applications, including:
Knowledge-intensive tasks (e.g., question answering, where factual accuracy is critical)
Content summarization (requiring faithful representation of source material)
Decision-support systems (demanding reliable, verifiable information)
Mitigating hallucination remains an active area of research, as it directly impacts the reliability and trustworthiness of AI-generated outputs in professional settings.
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Question 6:
What is the purpose of embeddings in natural language processing?
A. To create numerical representations of text that capture the meaning and relationships between words or phrases
B. To compress text data into smaller files for storage
C. To increase the complexity and size of text data
D. To translate text into a different language
Answer: A
Explanation:
Vector Embeddings: Semantic Representation in NLP
Embeddings transform discrete linguistic units into continuous vector representations within a multidimensional space, enabling models to encode and process semantic relationships. This approach offers several key advantages:
Key Properties:
• Semantic Topology: Words with similar meanings or related concepts occupy proximate regions in vector space (e.g., "king" and "queen" exhibit smaller inter-vector distances than to semantically unrelated terms like "car")
• Dimensional Efficiency: Provides compact, dense representations compared to sparse one-hot encodings, typically reducing dimensionality by several orders of magnitude
• Computational Optimization: The lower-dimensional feature space decreases memory requirements and improves processing efficiency for downstream ML tasks
Technical Implementation:
Modern embedding techniques create these vector spaces through neural network training, where the relative positioning of words emerges from their contextual usage patterns in large text corpora.
Applications:
These learned representations form the foundational input layer for most contemporary NLP architectures, enabling more sophisticated language understanding than discrete symbol-based approaches.
Question 7:
How does the structure of vector databases differ from traditional relational databases?
A. It is not optimized for high-dimensional spaces.
B. It uses simple row-based data storage.
C. It is based on distances and similarities in a vector space
D. A vector database stores data in a linear or tabular format.
Answer: C
Explanation:
Vector databases are purpose-built to efficiently manage high-dimensional data, making them ideal for applications involving machine learning models and similarity search. Unlike traditional relational databases, which store data in a tabular format optimized for transactional operations and SQL queries, vector databases are specifically engineered to rapidly perform operations such as nearest neighbor search within high-dimensional spaces. Their primary focus is on computing and leveraging distances and similarities between data points (vectors), rather than on the relationships between discrete, structured data entries.
Question 8:
Which is NOT a category of pretrained foundation models available in the OCI generative AI service?
A. Embedding models.
B. Generation models.
C. Translation models.
D. Summarization models.
Answer: C
Explanation:
Available Foundation Model Categories in OCI Generative AI Service
The OCI Generative AI service offers several categories of pretrained foundation models, with translation models currently not being a supported category. The available model types include:
Embedding Models
Transform textual input into dense vector representations
Enable semantic search and similarity analysis applications
Text Generation Models
Produce diverse textual outputs including:
Creative content (poems, scripts)
Technical artifacts (code, documentation)
Business communications (emails, letters)
Musical compositions
Summarization Models
Distill source material into concise representations
Maintain core meaning and critical information
Support both extractive and abstractive approaches
Question 9:
Which is NOT a built in memory type in LangChain?
A. ConversationBufferMemory.
B. ConvesationTokenBufferMemory
C. ConversationImageMemory
D. ConversationSummaryMemory
Answer: C
Explanation:
ConversationImageMemory is not a native memory type within the LangChain framework.
LangChain's core design currently prioritizes text-based interactions, and as such, it does not include a dedicated memory component specifically engineered for direct image handling.
The following are valid, built-in memory types provided by LangChain:
ConversationBufferMemory: Retains the complete conversation history as an unsummarized buffer.
ConversationTokenBufferMemory: Stores the conversation history, managing it by the number of tokens to control memory length.
ConversationSummaryMemory: Generates and stores a concise summary of the ongoing conversation.
Question 10:
What is the primary funtion of the temperature parameter in the OCI generative AI Models.
A. Determine the maximum number of tokens the model can generate per response.
B. Specfies a string that tells the model to stop generatng more content.
C. Controls the randomness of the models output, affecting its creativitity.
D. Assigns a penalty to tokens that have already appeared in the preceding text
Answer: C
Explanation:
The primary function of the temperature parameter in OCI generative AI models is to control the randomness of the model‘s output, affecting its creativity.
Higher temperature: Leads to more diverse and potentially surprising outputs.
Lower temperature: Produces more focused and deterministic outputs.
By adjusting the temperature, you can fine-tune the balance between creativity and predictability in the generated text.
For a full set of 235 questions. Go to
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SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
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