Professional-Machine-Learning-Engineer Exam Duration & 100% Latest Professional-Machine-Learning-Engineer Official Cert Guide Library - Google Professional Machine Learning Engineer

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Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

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Google Professional Machine Learning Engineer exam is a certification program designed to test the skills and knowledge of individuals who work in the field of machine learning. Google Professional Machine Learning Engineer certification is intended for professionals who have a strong background in machine learning and who are interested in demonstrating their expertise in this field to potential employers.

For more info read reference:

Google Web Services Website

Google Professional Machine Learning Engineer Sample Questions (Q111-Q116):

NEW QUESTION # 111
You are a data analyst on a marketing team. You have 40 GB of customer transaction data already stored in BigQuery, and you need to produce a churn-propensity model quickly to validate whether the project is worth further investment. Your team has strong SQL skills but limited Python experience. You want to minimize infrastructure setup. What should you do?

Answer: D

Explanation:
BigQuery ML lets the team train and evaluate a model with SQL where the data already resides, avoiding data movement and infrastructure provisioning. For a fast feasibility check with SQL- oriented staff, it delivers a baseline model with the least effort, and the model can later be exported or promoted to a managed service if the project proceeds.


NEW QUESTION # 112
The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

Answer: B


NEW QUESTION # 113
While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

Answer: B

Explanation:
The best option for handling missing values in a categorical feature is to replace them with a placeholder category indicating a missing value. This is a type of imputation, which is a method of estimating the missing values based on the observed data. Imputing the missing values with a placeholder category preserves the information that the data is missing, and avoids introducing bias or distortion in the feature distribution. It also allows the machine learning model to learn from the missingness pattern, and potentially use it as a predictor for the target variable. The other options are not suitable for handling missing values in a categorical feature, because:
* Removing the rows with missing values and upsampling the dataset by 5% would reduce the size of the dataset and potentially lose important information. It would also introduce sampling bias and overfitting, as the upsampling process would create duplicate or synthetic observations that do not reflect the true population.
* Replacing the missing values with the feature's mean would not make sense for a categorical feature, as the mean is a numerical measure that does not capture the mode or frequency of the categories. It would
* also create a new category that does not exist in the original data, and might confuse the machine learning model.
* Moving the rows with missing values to the validation dataset would compromise the validity and reliability of the model evaluation, as the validation dataset would not be representative of the test or production data. It would also reduce the amount of data available for training the model, and might introduce leakage or inconsistency between the training and validation datasets. References:
* Imputation of missing values
* Effective Strategies to Handle Missing Values in Data Analysis
* How to Handle Missing Values of Categorical Variables?
* Google Cloud launches machine learning engineer certification
* Google Professional Machine Learning Engineer Certification
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate


NEW QUESTION # 114
You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry. You want to perform A/B testing to identify the best-performing model using the simplest approach. What should you do?

Answer: A

Explanation:
Vertex AI Model Registry supports traffic splitting and built-in monitoring, making A/B testing seamless. This approach eliminates the need for additional monitoring tools and infrastructure overhead. Cloud Run and GKE solutions (Options A and C) add unnecessary complexity, while Looker Studio (Option B) requires additional configuration for monitoring.


NEW QUESTION # 115
You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

Answer: D

Explanation:
To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:
Use Vertex Explainable AI with the sampled Shapley method. Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction1. The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets2. The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features3.
Enable Vertex AI Model Monitoring to check for feature distribution drift. Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time4. Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.
Reference:
1: Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
2: Shapley value - Wikipedia
3: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
4: Introduction to Vertex AI Model Monitoring | Vertex AI | Google Cloud
[5]: Monitor models for data drift | Vertex AI | Google Cloud


NEW QUESTION # 116
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