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

Section	Weight	Objectives
Topic 1: Scale prototypes into AI models	18%	- Design and run experiments - Optimize model performance and generalization - Work with foundation models and generative AI techniques - Select appropriate model architectures and frameworks
Topic 2: Collaborate to manage data and models	16%	- 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 - Manage datasets and features in Vertex AI - Address data privacy, compliance, and governance
Topic 3: Architect low-code AI solutions	12%	- Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs
Topic 4: Train and deploy models	20%	- Configure training jobs and environments - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure
Topic 5: 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 6: 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

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最新的 Google Cloud Certified Professional-Machine-Learning-Engineer 免費考試真題 (Q42-Q47):

問題 #42
You recently trained an XGBoost model on tabular data. You plan to expose the model for internal use as an HTTP microservice. After deployment, you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?

A. Use a prebuilt XGBoost Vertex container to create a model, and deploy it to Vertex AI Endpoints.
B. Build a Flask-based app. Package the app in a custom container on Vertex AI, and deploy it to Vertex AI Endpoints.
C. Build a Flask-based app. Package the app in a Docker image, and deploy it to Google Kubernetes Engine in Autopilot mode.
D. Deploy the model to BigQuery ML by using CREATE MODEL with the
BOOSTED_TREE_REGRESSOR statement, and invoke the BigQuery API from the microservice.

答案：A

問題 #43
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

A. Reduce the value of the repeat parameter
B. Use the interleave option for reading data
C. Increase the buffer size for the shuffle option.
D. Decrease the batch size argument in your transformation
E. Set the prefetch option equal to the training batch size

答案：B,E

解題說明：
The tf.data dataset is a TensorFlow API that provides a way to create and manipulate data pipelines for machine learning. The tf.data dataset allows you to apply various transformations to the data, such as reading, shuffling, batching, prefetching, and interleaving. These transformations can affect the performance and efficiency of the model training process1 One of the common performance issues in model training is input-bound, which means that the model is waiting for the input data to be ready and is not fully utilizing the computational resources. Input-bound can be caused by slow data loading, insufficient parallelism, or large data size. Input-bound can be detected by using the Cloud TPU profiler plugin, which is a tool that helps you analyze the performance of your model on Cloud TPUs. The Cloud TPU profiler plugin can show you the percentage of time that the TPU cores are idle, which indicates input-bound2 To reduce the input-bound bottleneck and speed up the model training process, you can make some modifications to the tf.data dataset. Two of the modifications that can help are:
* Use the interleave option for reading data. The interleave option allows you to read data from multiple files in parallel and interleave their records. This can improve the data loading speed and reduce the idle time of the TPU cores. The interleave option can be applied by using the tf.data.Dataset.interleave method, which takes a function that returns a dataset for each input element, and a number of parallel calls3
* Set the prefetch option equal to the training batch size. The prefetch option allows you to prefetch the next batch of data while the current batch is being processed by the model. This can reduce the latency
* between batches and improve the throughput of the model training. The prefetch option can be applied by using the tf.data.Dataset.prefetch method, which takes a buffer size argument. The buffer size should be equal to the training batch size, which is the number of examples per batch4 The other options are not effective or counterproductive. Reducing the value of the repeat parameter will reduce the number of epochs, which is the number of times the model sees the entire dataset. This can affect the model's accuracy and convergence. Increasing the buffer size for the shuffle option will increase the randomness of the data, but also increase the memory usage and the data loading time. Decreasing the batch size argument in your transformation will reduce the number of examples per batch, which can affect the model's stability and performance.
References: 1: tf.data: Build TensorFlow input pipelines 2: Cloud TPU Tools in TensorBoard 3: tf.data.Dataset.interleave 4: tf.data.Dataset.prefetch : [Better performance with the tf.data API]

問題 #44
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 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.
B. 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.
C. 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.
D. 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.

答案：C

解題說明：
TFRecord is a binary file format that stores your data as a sequence of binary strings1. TFRecord files are efficient, scalable, and easy to process1. Sharding is a technique that splits a large file into smaller files, which can improve parallelism and performance2. Dataflow is a service that allows you to create and run data processing pipelines on Google Cloud3. Dataflow can create sharded TFRecord files from your images in a Cloud Storage directory4.
tf.data.TFRecordDataset is a class that allows you to read and parse TFRecord files in TensorFlow. You can use this class to create a tf.data.Dataset object that represents your input data for training. tf.data.Dataset is a high-level API that provides various methods to transform, batch, shuffle, and prefetch your data.
Vertex AI Training is a service that allows you to train your custom models on Google Cloud using various hardware accelerators, such as GPUs. Vertex AI Training supports TensorFlow models and can read data from Cloud Storage. You can use Vertex AI Training to train your image classification model by using a V100 GPU, which is a powerful and fast GPU for deep learning.
Reference:
TFRecord and tf.Example | TensorFlow Core
Sharding | TensorFlow Core
Dataflow | Google Cloud
Creating sharded TFRecord files | Google Cloud
[tf.data.TFRecordDataset | TensorFlow Core v2.6.0]
[tf.data: Build TensorFlow input pipelines | TensorFlow Core]
[Vertex AI Training | Google Cloud]
[NVIDIA Tesla V100 GPU | NVIDIA]

問題 #45
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. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
B. Add more data to your test set to ensure that you have a fair distribution and sample for testing.
C. Normalize the data for the training, and test datasets as two separate steps.
D. Split the training and test data based on time rather than a random split to avoid leakage.

答案：D

解題說明：
If you do time series prediction, you can't borrow information from the future to predict the future.
If you do, you are artificially increasing your accuracy.

問題 #46
You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table. The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:

You launch your Vertex Al pipeline as the following:

You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs. What should you do?

答案：B

問題 #47
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