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

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

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Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q153-Q158):

質問 # 153
You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

A. Rewrite your input function using parallel reads, parallel processing, and prefetch.
B. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
C. Rewrite your input function to resize and reshape the input images.
D. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.

正解：A

解説：
The trace in the question shows that the training time is taking longer than expected. This is likely due to the input function not being optimized. To decrease training time in a cost-efficient way, the best option is to rewrite the input function using parallel reads, parallel processing, and prefetch. This will allow the model to process the data more efficiently and decrease training time. References:
* [Cloud TPU Performance Guide]
* [Data input pipeline performance guide]

質問 # 154
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

A. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
B. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
C. Initialize the model with random weights in all layers and replace the last fully connected layer.
D. Initialize the model with random weights in all layers including the last fully connected layer.

正解：A

解説：
Explanation/Reference:

質問 # 155
You recently created a new Google Cloud project. After testing that you can submit a Vertex AI Pipeline job from the Cloud Shell, you want to use a Vertex AI Workbench user-managed notebook instance to run your code from that instance. You created the instance and ran the code but this time the job fails with an insufficient permissions error. What should you do?

A. Ensure that the Vertex AI Workbench instance is on the same subnetwork of the Vertex AI Pipeline resources that you will use.
B. Ensure that the Vertex AI Workbench instance is assigned the Identity and Access Management (IAM) Notebooks Runner role.
C. Ensure that the Workbench instance that you created is in the same region of the Vertex AI Pipelines resources you will use.
D. Ensure that the Vertex AI Workbench instance is assigned the Identity and Access Management (IAM) Vertex AI User role.

正解：D

質問 # 156
You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the dat a. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?

A. 1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.
2 Write a Python script that creates sharded TFRecord files in a directory inside the instance
3. Reference tf. da-a.TFRecrrdDataset in the training script.
4. Train the model by using the Workbench instance.
B. 1 Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.
2 Reference tf .data.TFRecordDataset in the training script.
3. Train the model by using Vertex Al Training with a V100 GPU.
C. 1 Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.
2 Reference tfds.fclder_da-asst.imageFclder in the training script.
3. Train the model by using Vertex AI Training with a V100 GPU.
D. 1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.
2 Write a Python scnpt that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.
3 Reference tf ds. f older_dataset. imageFolder in the training script.
4. Train the model by using the Workbench instance.

正解：B

質問 # 157
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

A. Use Vertex AI Workbench user-managed notebooks to generate the report.
B. Use the output from TensorFlow Data Validation on Dataflow to generate the report.
C. Use Dataprep to create the report.
D. Use the Google Data Studio to create the report.

正解：A

解説：
* Option A is correct because using Vertex AI Workbench user-managed notebooks to generate the report is the best way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. You can use Vertex AI Workbench to connect to your BigQuery table, query and analyze the data using SQL or Python, and create interactive charts and plots using libraries such as pandas, matplotlib, or seaborn.
You can also use Vertex AI Workbench to perform more advanced data analysis, such as outlier detection, feature engineering, or hypothesis testing, using libraries such as TensorFlow Data Validation, TensorFlow Transform, or SciPy. You can export your notebook as a PDF or HTML file, and share it with your team. Vertex AI Workbench provides maximum flexibility to create your report, as you can use any code or library that you want, and customize the report as you wish.
* Option B is incorrect because using Google Data Studio to create the report is not the most flexible way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributionsand more sophisticated statistical analyses to share with other ML engineers on your team. Google Data Studio is a service that allows you to create and share interactive dashboards and reports using data from various sources, such as BigQuery, Google Sheets, or Google Analytics. You can use Google Data Studio to connect to your BigQuery table, explore and visualize the data using charts, tables, or maps, and apply filters, calculations, or aggregations to the data. However, Google Data Studio does not support more
* sophisticated statistical analyses, such as outlier detection, feature engineering, or hypothesis testing, which may be useful for model development. Moreover, Google Data Studio is more suitable for creating recurring reports that need to be updated frequently, rather than one-time reports that are static.
* Option C is incorrect because using the output from TensorFlow Data Validation on Dataflow to generate the report is not the most efficient way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team.
TensorFlow Data Validation is a library that allows you to explore, validate, and monitor the quality of your data for ML. You can use TensorFlow Data Validation to compute descriptive statistics, detect anomalies, infer schemas, and generate data visualizations for your data. Dataflow is a service that allows you to create and run scalable data processing pipelines using Apache Beam. You can use Dataflow to run TensorFlow Data Validation on large datasets, such as those stored in BigQuery.
However, this option is not very efficient, as it involves moving the data from BigQuery to Dataflow, creating and running the pipeline, and exporting the results. Moreover, this option does not provide maximum flexibility to create your report, as you are limited by the functionalities of TensorFlow Data Validation, and you may not be able to customize the report as you wish.
* Option D is incorrect because using Dataprep to create the report is not the most flexible way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. Dataprep is a service that allows you to explore, clean, and transform your data for analysis or ML. You can use Dataprep to connect to your BigQuery table, inspect and profile the data using histograms, charts, or summary statistics, and apply transformations, such as filtering, joining, splitting, or aggregating, to the data. However, Dataprep does not support more sophisticated statistical analyses, such as outlier detection, feature engineering, or hypothesis testing, which may be useful for model development. Moreover, Dataprep is more suitable for creating data preparation workflows that need to be executed repeatedly, rather than one-time reports that are static.
References:
* Vertex AI Workbench documentation
* Google Data Studio documentation
* TensorFlow Data Validation documentation
* Dataflow documentation
* Dataprep documentation
* [BigQuery documentation]
* [pandas documentation]
* [matplotlib documentation]
* [seaborn documentation]
* [TensorFlow Transform documentation]
* [SciPy documentation]
* [Apache Beam documentation]

質問 # 158
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