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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q28-Q33):
NEW QUESTION # 28
What does the Loss metric indicate about a model's predictions?
- A. Loss indicates how good a prediction is, and it should increase as the model improves.
- B. Loss measures the total number of predictions made by a model.
- C. Loss is a measure that indicates how wrong the model's predictions are.
- D. Loss describes the accuracy of the right predictions rather than the incorrect ones.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss is a metric that quantifies the difference between a model's predictions and the actual target values, indicating how incorrect (or "wrong") the predictions are. Lower loss means better performance, making Option B correct. Option A is false-loss isn't about prediction count. Option C is incorrect-loss decreases as the model improves, not increases. Option D is wrong-loss measures overall error, not just correct predictions. Loss guides training optimization.
OCI 2025 Generative AI documentation likely defines loss under model training and evaluation metrics.
NEW QUESTION # 29
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?
- A. 30 unit hours
- B. 25 unit hours
- C. 20 unit hours
- D. 40 unit hours
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise (e.g., per GPU scaling). For 10 hours of activity, it's 10 hours × 1 unit/hour = 10 unit hours, but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-unit/hour rate (e.g., for GPU clusters), it's 10 × 2 = 20 unit hours-Option C fits best. Options A, B, and D imply inconsistent rates (2.5, 4, 3).
OCI 2025 Generative AI documentation likely specifies unit hour rates under DedicatedAI Cluster pricing.
NEW QUESTION # 30
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
- A. Controls the randomness of the model's output, affecting its creativity
- B. Specifies a string that tells the model to stop generating more content
- C. Determines the maximum number of tokens the model can generate per response
- D. Assigns a penalty to tokens that have already appeared in the preceding text
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "temperature" parameter adjusts the randomness of an LLM's output by scaling the softmax distribution-low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity-Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.
OCI 2025 Generative AI documentation likely defines temperature under generation parameters.
NEW QUESTION # 31
How does a presence penalty function in language model generation when using OCI Generative AI service?
- A. It penalizes all tokens equally, regardless of how often they have appeared.
- B. It penalizes a token each time it appears after the first occurrence.
- C. It only penalizes tokens that have never appeared in the text before.
- D. It applies a penalty only if the token has appeared more than twice.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.
NEW QUESTION # 32
Which is a key characteristic of the annotation process used in T-Few fine-tuning?
- A. T-Few fine-tuning involves updating the weights of all layers in the model.
- B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
- C. T-Few fine-tuning requires manual annotation of input-output pairs.
- D. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.
NEW QUESTION # 33
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