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

SectionObjectives
Architecting low-code ML solutions- Implementing BigQuery ML for basic models
- Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI)
- AutoML capabilities and implementation
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
Serving and scaling models- Online prediction (Vertex AI Prediction)
- Hardware accelerators (GPU/TPU) in serving
- Model optimization (Quantization, Distillation)
- Batch prediction
Automating and orchestrating ML pipelines- Triggering and scheduling pipelines
- Vertex AI Pipelines (Kubeflow Pipelines)
- CI/CD for ML systems
Scaling prototypes into ML models- Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
- Training at scale (Distributed training, TPUs)
- Hyperparameter tuning
Monitoring ML solutions- Model retraining strategies
- Performance monitoring and drift detection
- Logging and alerting (Cloud Monitoring)

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Google Professional Machine Learning Engineer Sample Questions (Q184-Q189):

NEW QUESTION # 184
You recently created a new Google Cloud Project After testing that you can submit a Vertex Al Pipeline job from the Cloud Shell, you want to use a Vertex Al 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?

Answer: D


NEW QUESTION # 185
You work for a retail company. You have created a Vertex Al forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

Answer: A


NEW QUESTION # 186
You are developing ML models with AI Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code.
What should you do?

Answer: C

Explanation:
At the heart of this architecture is Cloud Build, infrastructure. Cloud Build can import source from Cloud Source Repositories, GitHub, or Bitbucket, and then execute a build to your specifications, and produce artifacts such as Docker containers or Python tar files.
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture


NEW QUESTION # 187
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

Answer: D

Explanation:
* Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data. F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class1. Precision is the fraction of true positives among all predicted positives, while recall is the fraction of true positives among all actual positives1. When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company's logo, which are rare but important to detect. By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included2.
* Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data. RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values3. RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete4.
* Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data. F1 score is a special case of F-score where precision and recall are equally weighted1. F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent5. However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 score may not reflect the performance of the model well2.
* Option D is incorrect because using F-score where precision is weighed more than recall is not a good metric for binary classification with imbalanced data. By weighing precision more than recall, we can emphasize the importance of minimizing the false positives, even if some true positives are missed2. However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well2.
References:
* Precision, recall, and F-measure
* F-score for imbalanced data
* RMSE
* Regression vs classification
* F1 score
* [Imbalanced classification]
* [Binary classification]


NEW QUESTION # 188
You work for a retail company. You have created a Vertex Al forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

Answer: A

Explanation:
According to the official exam guide 1 , one of the skills assessed in the exam is to "explain the predictions of a trained model". Vertex AI provides feature attributions using Shapley Values, a cooperative game theory algorithm that assigns credit to each feature in a model for a particular outcome 2 . Feature attributions can help you understand how the model calculates the predictions and debug or optimize the model accordingly. You can use Forecasting with AutoML or Tabular Workflow for Forecasting to generate and query local feature attributions 2 . The other options are not relevant or optimal for this scenario. References :
* Professional ML Engineer Exam Guide
* Feature attributions for forecasting
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


NEW QUESTION # 189
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