Professional-Machine-Learning-Engineer復習問題集、Professional-Machine-Learning-Engineer受験体験

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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

SectionObjectives
Topic 1: Serving and scaling models- Batch prediction
- Model optimization (Quantization, Distillation)
- Hardware accelerators (GPU/TPU) in serving
- Online prediction (Vertex AI Prediction)
Topic 2: Scaling prototypes into ML models- Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
- Training at scale (Distributed training, TPUs)
- Hyperparameter tuning
Topic 3: Automating and orchestrating ML pipelines- Vertex AI Pipelines (Kubeflow Pipelines)
- Triggering and scheduling pipelines
- CI/CD for ML systems
Topic 4: Architecting low-code ML solutions- AutoML capabilities and implementation
- Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI)
- Implementing BigQuery ML for basic models
Topic 5: Monitoring ML solutions- Performance monitoring and drift detection
- Logging and alerting (Cloud Monitoring)
- Model retraining strategies
Topic 6: Collaborating within and across teams to manage data and models- Collaboration between Data Scientists, Data Engineers, and ML Engineers
- Version control and reproducibility (e.g., DVC, MLOps)
- Data management and governance

>> Professional-Machine-Learning-Engineer復習問題集 <<

高品質Professional-Machine-Learning-Engineer復習問題集 | 素晴らしい合格率のProfessional-Machine-Learning-Engineer Exam | パススルーのProfessional-Machine-Learning-Engineer: Google Professional Machine Learning Engineer

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Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q58-Q63):

質問 # 58
You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100,000 categorical features. You notice that as the data increases, the model training time increases. You plan to move the models to Google Cloud. You want to use the most scalable approach that also minimizes training time. What should you do?

正解：A


質問 # 59
You work for a pharmaceutical company based in Canada. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?

正解：B

解説：
To configure a model retraining policy that minimizes cost, you should follow these steps:
* Download the weather data each week, and download the flu data each month. This way, you can keep your data up to date with the latest information available, without downloading unnecessary or redundant data.
* Deploy the model to a Vertex AI endpoint with feature drift monitoring. This feature allows you to detect when the distribution of the input data changes significantly from the training data, which could affect the model performance 1 .
* Retrain the model if a monitoring alert is detected. This way, you can update your model only when needed, instead of retraining it on a fixed schedule, which could incur more cost and time.
:
1 : Monitor models for feature drift | Vertex AI | Google Cloud


質問 # 60
Your team is training a large number of ML models that use different algorithms, parameters, and datasets. Some models are trained in Vertex AI Pipelines, and some are trained on Vertex AI Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics. What should you do?

正解：C


質問 # 61
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an AI Platform notebook.
What should you do?

正解：A

解説：
https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas


質問 # 62
You are building a predictive maintenance model to preemptively detect part defects in bridges.
You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

正解：D


質問 # 63
......

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