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Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q221-Q226):

質問 # 221
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

A. Remove negative examples until the numbers of positive and negative examples are equal
B. Use a convolutional neural network with max pooling and softmax activation
C. Use the class distribution to generate 10% positive examples
D. Downsample the data with upweighting to create a sample with 10% positive examples

正解：D

解説：
https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting
https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data

質問 # 222
You built a deep learning-based image classification model by using on-premises dat a. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A. Use Vertex Explainable Al for model explainability Configure example-based explanations.
B. Use Vertex Explainable Al for model explainability Configure feature-based explanations.
C. Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.
D. Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.

正解：D

解説：
Vertex AI Model Monitoring is a service that allows you to monitor the performance and quality of your ML models in production. You can use Vertex AI Model Monitoring to detect changes in the input data distribution, the prediction output distribution, or the model accuracy over time. Training-serving skew detection is a feature of Vertex AI Model Monitoring that compares the statistics of the data used for training the model and the data used for serving the model. If there is a significant difference between the two data distributions, it indicates that the model might be outdated or inaccurate. By enabling training-serving skew detection for your model, you can detect model performance changes in production and trigger retraining or redeployment of your model as needed. This way, you can ensure that your model is always up-to-date and accurate, without moving your data to the cloud. Reference:
Vertex AI Model Monitoring documentation
Training-serving skew detection documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

質問 # 223
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

A. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.
B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction
C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map
D. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

正解：D

解説：
This answer is correct because it uses a regression model to estimate the number of passengers at each shuttle station, which is a continuous variable. A tree-based regression model can handle both numerical and categorical features, such as the time of day, the location of the station, and the weather conditions. Based on the predicted number of passengers, the organization can dispatch a shuttle that has enough capacity and provide a map that shows the required stops. This way, the organization can optimize the shuttle service route and reduce the waiting time and fuel consumption. References:
* [Tree-based regression models]

質問 # 224
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 data.
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 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.
B. 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.
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 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.

正解：A

解説：
TFRecord is a binary file format that stores your data as a sequence of binary strings 1 . TFRecord files are efficient, scalable, and easy to process 1 . Sharding is a technique that splits a large file into smaller files, which can improve parallelism and perfo rmance 2 . Dataflow is a service that allows you to create and run data processing pipelines on G oogle Cloud 3 . Dataflow can create sharded TFRecord files from your images in a Cloud Storage directory 4 .
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.
:
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]

質問 # 225
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework.
Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs.
You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

A. Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.
B. Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
C. Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization.
D. Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

正解：C

解説：
Vertex AI Experiments is a service that allows you to track, compare, and optimize your ML experiments on Google Cloud. You can use Vertex AI Experiments to log metrics and parameters from your TensorFlow models, and then visualize them in Vertex AI TensorBoard. Vertex AI TensorBoard is a managed service that provides a web interface for viewing and debugging your ML experiments. You can use Vertex AI TensorBoard to compare different runs, inspect model graphs, analyze scalars, histograms, images, and more.
By using Vertex AI Experiments and Vertex AI TensorBoard, you can simplify your ML experiment tracking and visualization workflow, and avoid the overhead of setting up and maintaining your own Cloud Functions, Cloud Storage buckets, or VMs. References:
* [Vertex AI Experiments documentation]
* [Vertex AI TensorBoard documentation]
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

質問 # 226
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