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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Professional Machine Learning Engineer |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Passing Score: | Not publicly disclosed (Pass/Fail) |
| Related Certifications: | Google Cloud Certified - Professional Data Engineer |
| Exam Price: | $200 USD |
| Available Languages: | English, Japanese |
| Real Exam Qty: | 50-60 |
| Exam Format: | Multiple select, Multiple choice |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online (proctored) or Test center (Kryterion) |
| Pre Condition: | Recommended 3+ years of industry experience with ML models and 1+ year of experience using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
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The Google Professional Machine Learning Engineer certification exam covers various topics related to machine learning, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Professionals who pass the exam demonstrate their ability to design and develop machine learning models that meet specific business requirements. Google Professional Machine Learning Engineer certification exam also covers various machine learning techniques such as deep learning, supervised and unsupervised learning, and reinforcement learning.
NEW QUESTION # 343
You need to build classification workflows over several structured datasets currently stored in BigQuery.
Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
Answer: C
Explanation:
AutoML Tables is a service that allows you to automatically build and deploy state-of-the-art machine learning models on structured data without writing code. You can use AutoML Tables to perform the following steps for the classification task:
* Exploratory data analysis: AutoML Tables provides a graphical user interface (GUI) and a command-line interface (CLI) to explore your data, visualize statistics, and identify potential issues.
* Feature selection: AutoML Tables automatically selects the most relevant features for your model based on the data schema and the target column. You can also manually exclude or include features, or create new features from existing ones using feature engineering.
* Model building: AutoML Tables automatically builds and evaluates multiple machine learning models using different algorithms and architectures. You can also specify the optimization objective, the budget, and the evaluation metric for your model.
* Training and hyperparameter tuning: AutoML Tables automatically trains and tunes your model using the best practices and techniques from Google's research and engineering teams. You can monitor the training progress and the performance of your model on the GUI or the CLI.
* Serving: AutoML Tables automatically deploys your model to a fully managed, scalable, and secure environment. You can use the GUI or the CLI to request predictions from your model, either online
* (synchronously) or offline (asynchronously).
References:
* [AutoML Tables documentation]
* [AutoML Tables overview]
* [AutoML Tables how-to guides]
NEW QUESTION # 344
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?
Answer: D
NEW QUESTION # 345
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?
Answer: C
Explanation:
TensorFlow Data Validation (TFDV) is a library that helps you understand, validate, and monitor your data for machine learning. It can automatically detect and report schema anomalies, such as missing features, new features, or different data types, in your data. It can also generate descriptive statistics and data visualizations to help you explore and debug your data. TFDV can be integrated with your model training pipeline to ensure data quality and consistency throughout the machine learning lifecycle. References:
* TensorFlow Data Validation
* Data Validation | TensorFlow
* Data Validation | Machine Learning Crash Course | Google Developers
NEW QUESTION # 346
You have recently developed a custom model for image classification by using a neural network.
You need to automatically identify the values for learning rate, number of layers, and kernel size.
To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management.
What should you do?
Answer: D
Explanation:
https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning
NEW QUESTION # 347
You work for a telecommunications company You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery, and the predictive features that are available for model training include
- Customer_id -Age
- Salary (measured in local currency) -Sex
-Average bill value (measured in local currency)
- Number of phone calls in the last month (integer) -Average duration of phone calls (measured in minutes) You need to investigate and mitigate potential bias against disadvantaged groups while preserving model accuracy What should you do?
Answer: D
Explanation:
* A fairness metric is a way to measure how well a machine learning model treats different groups of customers, such as by sex or age. A common fairness metric is accuracy, which is the proportion of correct predictions among all predictions. Accuracy across the sensitive features means calculating the accuracy for each group separately, and then comparing them. For example, if the model has 90% accuracy for male customers and 80% accuracy for female customers, there is a 10% accuracy gap that indicates potential bias against female customers.
* To investigate and mitigate potential bias, it is important to define a fairness metric and evaluate it on a test set. A test set is a subset of the data that is not used for training the model, but only for evaluating its performance. By joining the test set predictions with the sensitive features, you can calculate the fairness metric and see if it meets your requirements. For example, you may require that the accuracy gap between any two groups is less than 5%. If the fairness metric does not meet your requirements, you may need to adjust the model or the data to reduce bias.
* Option A is not the best answer because excluding the sensitive features and any meaningfully correlated features may not eliminate bias. For example, if salary is correlated with sex, and salary is also a predictive feature for the target variable, excluding both features may reduce the model accuracy and still leave some residual bias. Moreover, excluding features based on correlation may not capture the complex interactions and dependencies among the features that may affect bias.
* Option B is not the best answer because using the global attribution values for each feature of the model may not reflect the individual-level impact of the features on the predictions. Global attribution values are calculated by averaging the attribution values across all the data points, and they indicate how important each feature is for the overall model performance. However, they do not show how each feature affects each customer's prediction, which may vary depending on the values of the other features. For example, sex may have a low global attribution value, but it may have a high impact on some customers' predictions, especially if it interacts with other features such as salary or age.
* Option C is not the best answer because discarding the model and training the model again without a feature based on a single customer's attribution value may not be a robust or scalable way to mitigate bias. Attribution values are calculated by measuring how much each feature contributes to the prediction for a given data point, and they indicate how sensitive the prediction is to the feature value.
However, they do not show how the feature affects the overall fairness metric or the model accuracy.
For example, sex may have a high attribution value for a customer, but it may not affect the accuracy gap between the groups. Moreover, discarding and retraining the model based on a single customer's attribution value may not be feasible if there are many customers with high attribution values for different features.
NEW QUESTION # 348
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