Note (2026-05-06): Vertex AI was rebranded as Gemini Enterprise Agent Platform. Google's exam guides still use the Vertex AI naming, so this article does too. The official guides may switch to the new name at some point as you prep, but for now we're matching the language currently in the exam materials.
Once you understand where a model gets its information, the next question is how to control the way it generates a response. That is exactly the dimension the Generative AI Leader exam tests when it asks about model parameters. I want to walk through the five parameters Google expects you to know for the Generative AI Leader certification and how each one shows up in exam-style scenarios.
LLM parameters are configuration settings that control how a generative AI model behaves during inference, meaning at the moment it is producing output. The important thing to understand is that parameters do not change the model itself. They tweak its behavior without retraining it, which makes them essential tools for shaping output without any additional cost or complexity. The model is the same model. You are just adjusting the dials on how it responds.
There are five parameters worth knowing for the exam: Temperature, Top-K, Top-P, output length, and safety settings. Four of them shape how the model generates a response. The last one acts as a filter after generation. The exam will test whether you can match the right parameter to the symptom described in a scenario.
Temperature is the most common parameter you will adjust. It controls the randomness and creativity of the output. Think of it as a dial that determines how safe or how risky the model should be when choosing the next word.
Low temperature, in the range of 0.0 to 0.3, produces deterministic, focused, and conservative outputs. The model picks the most likely next word almost every time. This is what you want for factual tasks like classification, data extraction, or answering specific questions where accuracy is paramount.
Medium temperature, in the range of 0.4 to 0.7, gives you balanced creativity and consistency. You get coherent answers with a little natural variation so the model does not sound robotic.
High temperature, in the range of 0.8 to 2.0, produces more creative, diverse, and unpredictable outputs. The model takes more risks. This is the right setting for brainstorming sessions, creative writing, or generating a list of unique ideas.
For the exam, watch the keywords that describe the output problem. If the scenario says the model is being too predictable or repetitive, the answer is to increase the temperature. If the model is being too random, incoherent, or hallucinating, the answer is to decrease the temperature.
Every time a language model generates the next word, it does not pick blindly from its entire vocabulary. It first narrows down to a shortlist of the most likely tokens. Top-K is the parameter that sets exactly how long that shortlist is. Top-K limits the model to selecting from only the K most likely next tokens at each step.
With K set to 3, only the three highest-probability tokens are eligible. Everything else is cut off entirely, regardless of how close the next-ranked token might be. The result is very predictable, focused output. Low Top-K is ideal for factual question answering, data extraction, and code generation where consistency and accuracy matter more than variety.
With K set to 40, the pool expands dramatically. The model now has 40 tokens to draw from at each step, which lets it make more surprising, less obvious word choices. The output becomes varied and creative. High Top-K is the right setting for creative writing and brainstorming.
Top-P solves a similar problem to Top-K but in a more dynamic way. Top-P selects from the smallest set of tokens whose cumulative probability exceeds the threshold P. Instead of fixing the number of eligible tokens, it uses a probability threshold, so the number of eligible tokens can change at every step depending on how the probabilities are distributed.
Take the prompt "The weather is very..." with the next-token probabilities sunny at 34%, warm at 22%, hot at 16%, nice at 11%, and clear at 7%. With Top-P set to 0.4, the model accumulates probabilities starting from the most likely token until it crosses 40%. Sunny alone is 34%. Add warm at 22% and you are already past 40%. So only two tokens are eligible: sunny and warm. The output is predictable and focused.
Raise Top-P to 0.9 and the model keeps accumulating: 34% plus 22% is 56%, plus 16% is 72%, plus 11% is 83%, plus 7% brings you to 90%. Now five tokens are in the pool. The output becomes more varied and creative.
The distinction the Generative AI Leader exam will test is exactly this: Top-K fixes the number of eligible tokens regardless of how the probabilities are distributed. Top-P is dynamic and uses a probability threshold instead, so the number of eligible tokens varies at every step.
Output length sets the maximum number of tokens the model can generate in a single response. A token is roughly equivalent to a word or part of a word, so this parameter is essentially a hard cap on how long the answer can be. Once the model hits that limit, it stops, regardless of whether the response is complete.
The most common issue here is what the exam frames as cut-off responses. If users are complaining that answers are being cut off mid-sentence or that the model seems to stop right before its conclusion, the problem is almost always that output length is set too low. The fix is to increase the max_output_tokens parameter so the model has the runway it needs to finish.
There is a trade-off, though. You should not just set this to the maximum value by default. Longer outputs require more computational resources, which means they cost more and take longer to generate. If you let the model ramble for 2,000 words when the user wanted a yes or no, you are adding unnecessary latency and cost.
For the exam, if a scenario describes incomplete sentences or cut-off responses, max_output_tokens is usually the correct answer.
Safety settings are different in nature from the other four parameters. Temperature, Top-K, Top-P, and output length all shape how the model generates its response. Safety settings operate after the response is generated, acting as a filter that decides whether that response should reach the user at all.
Safety settings are responsible for controlling the filtering of potentially harmful, inappropriate, or sensitive content. Their purpose is to protect users and ensure the application complies with content policies. A common misconception is that enabling safety filters will make the model more cautious or restrictive in its general tone. That is not the case. Safety settings filter content but do not affect creativity or variety in appropriate responses. They only intervene when content crosses into harmful territory.
In Google Cloud, you configure safety settings via the Vertex AI API by adjusting thresholds for categories like hate speech, harassment, sexually explicit content, and dangerous content. Each category can be tuned independently, which gives you fine-grained control over what gets blocked based on the sensitivity requirements of your application.
For the exam, remember that safety settings are a post-generation filter. They do not change how the model thinks. They only control what it is allowed to deliver to the user.
The fastest way to handle parameter questions on the Generative AI Leader exam is to map symptom to parameter. Too predictable or too repetitive points to raising temperature, Top-K, or Top-P. Too random, incoherent, or hallucinating points to lowering them. Cut-off mid-sentence points to output length. Harmful or inappropriate content reaching users points to safety settings. The dynamic-versus-fixed-count distinction is the specific lever the exam uses to separate Top-P from Top-K.
My Generative AI Leader course covers model parameters and the keyword mappings the exam tests for alongside the rest of the foundational material you need for the exam.
