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| Section | Objectives |
|---|---|
| Topic 1: Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Batch prediction - Online prediction (Vertex AI Prediction) |
| Topic 2: Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation - Implementing BigQuery ML for basic models |
| Topic 3: Automating and orchestrating ML pipelines | - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems |
| Topic 4: Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection - Model retraining strategies |
| Topic 5: Collaborating within and across teams to manage data and models | - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Topic 6: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
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NEW QUESTION # 239
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?
Answer: B
Explanation:
The class imbalance problem is a common challenge in machine learning, especially in classification tasks. It occurs when the distribution of the target classes is highly skewed, such that one class (the majority class) has much more examples than the other class (the minority class). The minority class is often the more interesting or important class, such as failure incidents, fraud cases, or rare diseases. However, most machine learning algorithms are designed to optimize the overall accuracy, which can be biased towards the majority class and ignore the minority class. This can result in poor predictive performance, especially for the minority class.
There are different techniques to deal with the class imbalance problem, such as data-level methods, algorithm-level methods, and evaluation-level methods1. Data-level methods involve resampling the original dataset to create a more balanced class distribution. There are two main types of data-level methods: oversampling and undersampling. Oversampling methods increase the number of examples in the minority class, either by duplicating existing examples or by generating synthetic examples. Undersampling methods reduce the number of examples in the majority class, either by randomly removing examples or by using clustering or other criteria to select representative examples. Both oversampling and undersampling methods can be combined with upweighting or downweighting, which assign different weights to the examples according to their class frequency, to further balance the dataset.
For the use case of investigating failures of a production line component based on sensor readings, the best option is to downsample the data with upweighting to create a sample with 10% positive examples. This option involves randomly removing some of the negative examples (the majority class) until the ratio of positive to negative examples is 1:9, and then assigning higher weights to the positive examples to compensate for their low frequency. This option can create a more balanced dataset that can improve the performance of the classification models, while preserving the diversity and representativeness of the original data. This option can also reduce the computation time and memory usage, as the size of the dataset is reduced. Therefore, downsampling the data with upweighting to create a sample with 10% positive examples is the best option for this use case.
Reference:
A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks
NEW QUESTION # 240
You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?
Answer: D
NEW QUESTION # 241
You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?
Answer: A
Explanation:
A is not correct because K-fold cross validation offers no explanation on the predictions made by the model.
B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.
C is not correct because PCA simplifies higher dimensional datasets but offers no added benefit to the scenario.
D is not correct because clustering images does not provide any insight into why the classification model made the predictions that it did.
https://cloud.google.com/ml-engine/docs/ai-explanations/overview
https://storage.googleapis.com/cloud-ai-whitepapers/AI%20Explainability%20Whitepaper.pdf
NEW QUESTION # 242
You recently deployed a pipeline in Vertex Al Pipelines that trains and pushes a model to a Vertex Al endpoint to serve real-time traffic. You need to continue experimenting and iterating on your pipeline to improve model performance. You plan to use Cloud Build for CI/CD You want to quickly and easily deploy new pipelines into production and you want to minimize the chance that the new pipeline implementations will break in production. What should you do?
Answer: C
Explanation:
The best option for continuing experimenting and iterating on your pipeline to improve model performance, using Cloud Build for CI/CD, and deploying new pipelines into production quickly and easily, is to set up a CI
/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, deploy the pipeline to production. This option allows you to leverage the power and simplicity of Cloud Build to automate, monitor, and manage your pipeline development and deployment workflow. Cloud Build is a service that can create and run continuous integration and continuous delivery (CI/CD) pipelines on Google Cloud. Cloud Build can build your source code, run unit tests, and deploy built artifacts to various Google Cloud services, such as Vertex AI Pipelines, Vertex AI Endpoints, and Artifact Registry. A CI/CD pipeline is a workflow that can automate the process of building, testing, and deploying software. A CI/CD pipeline can help you improve the quality and reliability of your software, accelerate the development and delivery cycle, and reduce the manual effort and errors. A pre-production environment is an environment that can simulate the production environment, but is isolated from the real users and data. A pre-production environment can help you test and validate your software before deploying it to production, and catch any bugs or issues that may affect the user experience or the system performance. By setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, you can ensure that your pipeline code is consistent and error-free, and that your pipeline artifacts are compatible and functional. After a successful pipeline run in the pre-production environment, you can deploy the pipeline to production, which is the environment where your software is accessible and usable by the real users and data. By deploying the pipeline to production after a successful pipeline run in the pre-production environment, you can minimize the chance that the new pipeline implementations will break in production, and ensure that your software meets the user expectations and requirements 1 .
The other options are not as good as option C, for the following reasons:
* Option A: Setting up a CI/CD pipeline that builds and tests your source code, and if the tests are successful, using the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines would not allow you to deploy new pipelines into production quickly and easily, and could increase the manual effort and errors. The Google Cloud console is a web-based user interface that can help you access and manage various Google Cloud services, such as Artifact Registry and Vertex AI Pipelines. Artifact Registry is a service that can store and manage your container images and other artifacts on Google Cloud. Artifact Registry can help you upload and organize your container images, and track the image versions and metadata. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. However, setting up a CI/CD pipeline that builds and tests your source code, and if the tests are successful, using the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines would not allow you to deploy new pipelines into production quickly and easily, and could increase the manual effort and errors. You would need to write code, create and run the CI/CD pipeline, use the Google Cloud console to upload the built container to Artifact Registry, and use the Google Cloud console to upload the compiled pipeline to Vertex AI Pipelines. Moreover, this option would not use a pre-production environment to test and validate your pipeline before deploying it to production, which could increase the chance that the new pipeline implemen tations will break in production 1 .
* Option B: Setting up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment, running unit tests in the pre-production environment, and if the tests are successful, deploying the pipeline to production would not allow you to test and validate your pipeline before deploying it to production, and could cause errors or poor performance. A unit test is a type of test that can verify the functionality and correctness of a small and isolated unit of code, such as a function or a class. A unit test can help you debug and improve your code quality, and catch any bugs or issues that may affect the code logic or output. However, setting up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment, running unit tests in the pre-production environment, and if the tests are successful, deploying the pipeline to production would not allow you to test and validate your pipeline before deploying it to production, and could cause errors or poor performance. You would need to write code, create and run the CI/CD pipeline, deploy the built artifacts to the pre-production environment, run the unit tests in the pre-production environment, and deploy the pipeline to production. Moreover, this option would not run the pipeline in the pre-production environme nt, which could prevent you from testing and validating the pipeline functionality and compatibility, and catching any bugs or issues that may affect the pipeline workflow or output 1 .
* Option D: Setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, after a successful pipeline run in the pre-production environment, rebuilding the source code, and deploying the artifacts to production would not allow you to deploy new pipelines into production quickly and easily, and could increase the complexity and cost of the pipeline development and deployment. Rebuilding the source code is a process that can recompile and repackage the source code into executable artifacts, such as container images and pipeline files. Rebuilding the source code can help you incorporate any changes or updates that may have occurred in the source code, and ensure that the artifacts are consistent and up-to-date. However, setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, after a successful pipeline run in the pre-production environment, rebuilding the source code, and deploying the artifacts to production would not allow you to deploy new pipelines into production quickly and easily, and could increase the complexity and cost of the pipeline development and deployment. You would need to write code, create and run the CI/CD pipeline, deploy the built artifacts to the pre-production environment, run the pipeline in the pre- production environment, rebuild the source code, and deploy the artifacts to production. Moreover, this option would increase the pipeline development and deployment time, as rebuilding the source code can be a time-consuming and resource-intensive process 1 .
References:
Preparing for Google Cloud Certification: Machine Learning Engineer , Course 3: Production ML Systems, Week 3: MLOps Google Cloud Professional Machine Learning Engineer Exam Guide , Section 3: Scaling ML models in production, 3.2 Automating ML workflows Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6: Production ML Systems, Section 6.4: Automating ML Workflows Cloud Build Vertex AI Pipelines Artifact Registry Pre-production environment
NEW QUESTION # 243
You built a deep learning-based image classification model by using on-premises data. 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?
Answer: B
Explanation:
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. References:
* Vertex AI Model Monitoring documentation
* Training-serving skew detection documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 244
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