Professional-Machine-Learning-Engineer受験トレーリング、Professional-Machine-Learning-Engineer受験記

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

Section	Weight	Objectives
Topic 1: Monitor and optimize AI solutions	16%	- Monitor data quality and pipeline health - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems
Topic 2: Automate and orchestrate ML pipelines	18%	- Design end-to-end ML workflows - Automate retraining and model updates - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools
Topic 3: Train and deploy models	20%	- Use Vertex AI deployment features and infrastructure - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns - Configure training jobs and environments
Topic 4: Architect low-code AI solutions	12%	- Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs
Topic 5: Scale prototypes into AI models	18%	- Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization - Select appropriate model architectures and frameworks
Topic 6: Collaborate to manage data and models	16%	- Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data 1. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools 2. Work with structured, unstructured, and semi-structured data

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Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q129-Q134):

質問 # 129
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A. Train a regression model using AutoML Tables.
B. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
C. Develop a regression model using BigQuery ML.
D. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.

正解：C

解説：
The key is to understand the amount of data that needs to be used for training - the sensor collects tens of millions of records every day and the model needs to use all the data up to the current date.
There is a limitation for AutoML is 100M rows -> https://cloud.google.com/vertex-ai/docs/tabular- data/classification-regression/prepare-data

質問 # 130
You are creating a deep neural network classification model using a dataset with categorical input values.
Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

A. Convert each categorical value into a run-length encoded string.
B. Convert the categorical string data to one-hot hash buckets.
C. Convert each categorical value into an integer value.
D. Map the categorical variables into a vector of boolean values.

正解：B

解説：
* Option A is incorrect because converting each categorical value into an integer value is not a good way to encode categorical values with high cardinality. This method implies an ordinal relationship between the categories, which may not be true. For example, assigning the values 1, 2, and 3 to the categories
"red", "green", and "blue" does not make sense, as there is no inherent order among these colors1.
* Option B is correct because converting the categorical string data to one-hot hash buckets is a suitable way to encode categorical values with high cardinality. This method uses a hash function to map each category to a fixed-length vector of binary values, where only one element is 1 and the rest are 0. This method preserves the sparsity and independence of the categories, and reduces the dimensionality of the input space2.
* Option C is incorrect because mapping the categorical variables into a vector of boolean values is not a valid way to encode categorical values with high cardinality. This method implies that each category can be represented by a combination of true/false values, which may not be possible for a large number of categories. For example, if there are 10,000 categories, then there are 2

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