Google Generative-AI-Leader Q&A - in .pdf

  • Generative-AI-Leader pdf
  • Exam Code: Generative-AI-Leader
  • Exam Name: Google Cloud Certified - Generative AI Leader Exam
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  • Q & A: 365 Questions and Answers
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NEW QUESTION: 1
シングルサイト、集中マルチサイト、分散マルチサイトの3つのVoIP設計モデルを検討する場合、
以下のどんな質問はオプションの1つを削除するのに役立ちますか?
A. ユーザーはボイスメールやインタラクティブな音声応答などのアプリケーションを必要としますか?
B. スイッチはインラインパワーを供給する必要がありますか?
C. エンタープライズアクセスのみがQoS対応WAN経由であるユーザーはいますか?
D. ユーザーは企業外でオフサイトコールを行う必要がありますか?
Answer: C

NEW QUESTION: 2
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.
learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the sampling method for the Hyperdrive experiment Which two sampling methods can you use? Each correct answer is a complete solution.
NOTE: Each correct selection is worth one point.
A. Grid sampling
B. No sampling
C. Bayesian sampling
D. Random sampling
Answer: C,D
Explanation:
Explanation
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.
Bayesian sampling does not support any early termination policy
Example:
from azureml.train.hyperdrive import BayesianParameterSampling
from azureml.train.hyperdrive import uniform, choice
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D: In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

NEW QUESTION: 3
Which of the following is a key difference between MDX and SQL?
A. MDX is used for dimensional style reporting while SQL is incapable of dimensional style reporting.
B. Dimensionally Modeled Relational (DMR) retrieves data using MDX, not SQL.
C. MDX can reference metadata that SQL cannot.
D. MDX has built in functions to support relative date calculations while SQL cannot perform relative date calculations.
Answer: C

NEW QUESTION: 4
A company has recurring quality issues with one of its sole-source suppliers, a new project will incur significant penalties from the customer if deliveries are late.
What should the project manager do?
A. Escalate the issue to senior management.
B. Identify high-level risks prior to developing contingency plans.
C. Review incoming inspection reports and quality logs.
D. Negotiate with other vendors with better quality-control methods.
Answer: C

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