Oracle Cloud Generative AI Professional (1Z0-1127-24) Exam Questions 2024
Oracle Cloud Generative AI Professional (1Z0-1127-24) Exam Questions 2024
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Question 1:
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.
Here‘s a brief explanation:
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.
Question 2:
Given the following code:
Prompt Template
(Input_variable[“human_input“,“city“],template-template)
Which statement is true about Prompt Template in relation to input_variables?
A.Prompt Tempate can support only a single variable at a time.
B.Prompt Template requires a minimum of two variables to function properly.
C.Prompt Template supports any number of variables, including the possibility of having none.
D.Prompt Template is unable to use any variables.
Answer: C
Explanation:
Prompt Template supports any number of variables, including the possibility of having none.
The input_variables parameter in a Prompt Template is a list that specifies the variables to be used in the template. This list can be empty, meaning no variables are required. Additionally, it can contain any number of variables as needed for the prompt.
Therefore, the other options are incorrect:
Prompt Template can use variables.
There‘s no minimum requirement for the number of variables.
Prompt Template can handle multiple variables.
Question 3:
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 Ranker in a text generation system evaluates and prioritizes the information retrieved by the Retriever. After the Retriever sources relevant information from a large corpus or database, the Ranker assesses the retrieved information to determine its relevance, quality, and suitability for the specific task or context. The Ranker may use various criteria and algorithms to evaluate the retrieved information, such as relevance to the users query, credibility of the source, recency of the information, and other contextual factors.
Question 4:
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:
Large Language Models (LLMs) without Retrieval Augmented Generation (RAG) primarily rely on internal knowledge learned during pretraining on a large text corpus. These models are trained on vast amounts of text data, which enables them to learn complex patterns, structures, and relationships within language.
Question 5:
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:
Increasing the temperature makes the distribution flatter, which means the difference between the probabilities of the most likely word and the less likely words is decreased. This allows for more varied and sometimes more creative or unexpected word choices.
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Question 6:
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 refers to a technique where additional parameters are introduced into a model input layer in the form of embeddings, which are tuned during training. This can be particularly useful when one wants to adapt a large pretrained model to a new task without modifying the entire models weights, which is resource-intensive. When there is a significant amount of labeled, task-specific data available, traditional fine-tuning or transfer learning might be more suitable. When the model needs to be adapted to perform well in a different domain it was not originally trained on, domain adaptation techniques that may involve fine-tuning or prompt-based approaches are commonly used, but this does not specifically denote soft prompting. When the model requires continued pretraining on unlabeled data, unsupervised or self-supervised learning techniques are typically employed, not soft prompting.
Question 7:
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:
Hallucination occurs when the model generates text that seems plausible or coherent but is not grounded in factual reality or relevant to the task at hand. Hallucination can be problematic, especially in applications where generating accurate and reliable information is crucial, such as question answering, summarization, or content generation for decision-making.
Question 8:
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:
Embeddings map words or text onto a continuous vector space where similar words are located close to each other. This allows NLP models to capture semantic relationships between words, such as synonyms or related concepts. For example, in a well-trained embedding space, the vectors for “king“ and “queen“ would be closer to each other than to unrelated words like “car“ or “tree.“ Embeddings also provide a dense, low-dimensional representation of words compared to traditional one-hot encodings. This makes them more efficient and effective as input features for machine learning models, reducing the dimensionality of the input space, and improving computational efficiency.
Question 9:
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 designed specifically to handle high-dimensional data efficiently, often used in applications that involve machine learning models and similarity search. Unlike traditional relational databases that store data in a linear or tabular format optimized for transactional operations and structured query language (SQL) queries, vector databases are optimized to quickly perform operations like nearest neighbor search in high-dimensional space. They focus on distances and similarities between data points (vectors) rather than on the relationships between discrete, structured data entries.
Question 10:
What does Loss measure in the evaluation of OCI Generative AI fine tuned models? THe difference between accuracy of the model at the beginning of training and the accuracy of the deployed model
A.The level of incorrectness in the model predictions with lower values indicating better performance.
B.The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluations
C.The improvement in accuracy achieved by the model during training on the user uploaded data set
D.The difference between the accuracy of the model at the begining of training and the accuracy of the deployed model.
Answer: A
Explanation:
Loss measures the level of incorrectness in the model predictions with lower values indicating better performance.
Explanation:
Loss function: A mathematical function that quantifies the error between the model‘s predicted output and the actual target value.
Lower loss: Indicates that the model‘s predictions are closer to the correct values, implying better performance.
Higher loss: Suggests that the model‘s predictions are further from the correct values, indicating poorer performance.
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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