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Google Professional-Machine-Learning-Engineer (Google Professional Machine Learning Engineer) Certification Exam is a professional-level certification exam offered by Google that tests your proficiency in building and deploying machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer exam is designed for individuals who have a solid understanding of machine learning concepts and have experience in building and deploying machine learning models on Google Cloud Platform.
Google Professional Machine Learning Engineer exam is an excellent way for experienced machine learning professionals to demonstrate their expertise in this field and to enhance their career prospects. Whether you are looking to advance your career or to simply stay up-to-date with the latest trends and technologies in machine learning, this certification program is a valuable investment in your professional development.
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To be eligible for the Google Professional Machine Learning Engineer certification exam, candidates must have a minimum of three years of experience in the field of machine learning. Candidates should also have experience in designing and implementing machine learning solutions using Google Cloud technologies such as Google Cloud ML Engine, BigQuery, and TensorFlow. In addition to these requirements, candidates should have a strong understanding of machine learning algorithms and data structures.
NEW QUESTION # 331
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?
Answer: C
Explanation:
When building a model to predict daily temperatures, it is important to split the training and test data based on time rather than a random split. This is because temperature data is likely to have temporal dependencies and patterns, such as seasonality, trends, and cycles. If the data is split randomly, there is a risk of data leakage, which occurs when information from the future is used to train or validate the model. Data leakage can lead to overfitting and unrealistic performance estimates, as the model may learn from data that it should not have access to. By splitting the data based on time, such as using the most recent data as the test set and the older data as the training set, the model can be evaluated on how well it can forecast future temperatures based on past data, which is the realistic scenario in production. Therefore, splitting the data based on time rather than a random split is the best way to make the production model more accurate.
NEW QUESTION # 332
You are responsible for managing and monitoring a Vertex AI model that is deployed in production. You want to automatically retrain the model when its performance deteriorates. What should you do?
Answer: D
Explanation:
In a production MLOps pipeline, the goal is to move from manual retraining to Continuous Training (CT) triggered by real-world performance.
* Vertex AI Model Monitoring: This service is specifically designed to monitor models in production for Prediction Drift and Feature Skew . It compares incoming production data to the training baseline.
By integrating this with Cloud Pub/Sub or Vertex Pipelines, you can automate the retraining trigger when model performance (like accuracy or precision, if ground truth is available) or data distributions drift beyond a set threshold.
* Why other options are incorrect:
* Option A: Explainable AI is for understanding why a model made a prediction; while shifts in attribution can indicate drift, it is a proxy, not a direct measure of performance deterioration.
* Option B: User feedback is subjective, often delayed, and difficult to programmatically use as a reliable trigger for automated retraining.
* Option D: A static dataset does not represent the changing nature of production data. If the world changes (drift), the model might still perform well on the old static data but fail in the real world.
NEW QUESTION # 333
You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?
Answer: C
Explanation:
Sampled Shapley is a fast and scalable approximation of the Shapley value, which is a game-theoretic concept that measures the contribution of each feature to the model prediction. Sampled Shapley is suitable for online prediction requests, as it can return feature attributions with minimal latency. The path count parameter controls the number of samples used to estimate the Shapley value, and a lower value means faster computation. Integrated Gradients is another explanation method that computes the average gradient along the path from a baseline input to the actual input. Integrated Gradients is more accurate than Sampled Shapley, but also more computationally intensive. Therefore, it is not recommended for online prediction requests, especially with a high path count. Prediction drift is the change in the distribution of feature values or labels over time. It can affect the performance and accuracy of the model, and may require retraining or redeploying the model. Vertex AI Model Monitoring allows you to monitor prediction drift on your deployed models and endpoints, and set up alerts and notifications when the drift exceeds a certain threshold. You can specify an email address to receive the notifications, and use the information to retrigger the training pipeline and deploy an updated version of your model. This is the most direct and convenient way to achieve your goal.
Training-serving skew is the difference between the data used for training the model and the data used for serving the model. It can also affect the performance and accuracy of the model, and may indicate data quality issues or model staleness. Vertex AI Model Monitoring allows you to monitor training-serving skew on your deployed models and endpoints, and set up alerts and notifications when the skew exceeds a certain threshold.
However, this is not relevant to the question, as the question is about the feature attributions of the model, not the data distribution. References:
* Vertex AI: Explanation methods
* Vertex AI: Configuring explanations
* Vertex AI: Monitoring prediction drift
* Vertex AI: Monitoring training-serving skew
NEW QUESTION # 334
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?
Answer: A
Explanation:
According to the web search results, Vertex AI Pipelines is a serverless orchestrator for running ML pipelines, using either the KFP SDK or TFX1. Vertex AI Pipelines provides a set of prebuilt components that can be used to perform common ML tasks, such as training, evaluation, deployment, and more2. Vertex AI ModelEvaluationOp and ModelDeployOp are two such components that can be used to evaluate and deploy a model to an endpoint for online inference3. However, Vertex AI Pipelines does not provide a prebuilt component for hyperparameter tuning. Therefore, to have control over how the model parameters are tuned, you need to use a custom component that calls the Vertex AI HyperparameterTuningJob service4. Therefore, option A is the best way to decide which Google Cloud pipeline components to use for the given use case, as it includes a custom component for hyperparameter tuning, and prebuilt components for model evaluation and deployment. The other options are not relevant or optimal for this scenario. References:
* Vertex AI Pipelines
* Google Cloud Pipeline Components
* Vertex AI ModelEvaluationOp and ModelDeployOp
* Vertex AI HyperparameterTuningJob
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 335
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?
Answer: B
NEW QUESTION # 336
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