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Professional-Machine-Learning-Engineer최신버전인기덤프, Professional-Machine-Learning-Engineer인기자격증시험덤프최신자료
PassTIP는 여러분을 성공으로 가는 길에 도움을 드리는 사이트입니다. PassTIP에서는 여러분이 안전하게 간단하게Google인증Professional-Machine-Learning-Engineer시험을 패스할 수 있는 자료들을 제공함으로 빠른 시일 내에 IT관련지식을 터득하고 한번에 시험을 패스하실 수 있습니다.
Google Professional Machine Learning Engineer 자격증 시험은 머신 러닝 분야에서 개인의 전문성을 검증할 수 있는 기회입니다. 이 자격증 시험은 머신 러닝 개념에 대한 지식과 이러한 개념을 실제 시나리오에 적용할 수 있는 능력을 개인이 검증하도록 설계되었습니다. 이는 Google Cloud Platform을 사용하여 확장 가능한 머신 러닝 모델을 설계, 구축 및 배포할 수 있는 능력을 개인이 증명해야 하는 엄격한 시험입니다.
>> Professional-Machine-Learning-Engineer최신버전 인기덤프 <<
Professional-Machine-Learning-Engineer최신버전 인기덤프 최신 시험대비 덤프공부자료
우리PassTIP에는 아주 엘리트 한 전문가들로 구성된 팀입니다 그들은 끈임 없는 연구와 자기자신만의 지식으로 많은 IT관연 덤프자료를 만들어 냄으로 여러분의 꿈을 이루어드립니다, 기존의 시험문제와 답과 시험문제분석 등입니다. PassTIP에서 제공하는Google Professional-Machine-Learning-Engineer시험자료의 문제와 답은 실제시험의 문제와 답과 아주 비슷합니다. PassTIP덤프들은 모두 보장하는 덤프들이며 여러분은 과감히 PassTIP의 덤프를 장바구니에 넣으세요. PassTIP에서 여러분의 꿈을 이루어 드립니다.
최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q140-Q145):
질문 # 140
You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
- A. Address the model overfitting by using a less complex algorithm.
- B. Address data leakage by removing features highly correlated with the target value.
- C. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
- D. Address data leakage by applying nested cross-validation during model training.
정답:D
설명:
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9
질문 # 141
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?
- A. Add more data to your test set to ensure that you have a fair distribution and sample for testing
- B. Normalize the data for the training, and test datasets as two separate steps.
- C. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
- D. Split the training and test data based on time rather than a random split to avoid leakage
정답:A
질문 # 142
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website.
Which result should you use to determine whether the model is successful?
- A. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
- B. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
- C. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.
- D. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
정답:A
질문 # 143
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?
- A. Use one-hot encoding on all categorical features.
- B. Z-normalize all the numeric features.
- C. Oversample the fraudulent transaction 10 times.
- D. Write your data in TFRecords.
정답:C
설명:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samplesin the minority class.
Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
* Random Oversampling and Undersampling for Imbalanced Classification
* Exploring Oversampling Techniques for Imbalanced Datasets
질문 # 144
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
- B. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- C. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- D. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
정답:D
질문 # 145
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