Latest Test Google Professional-Machine-Learning-Engineer Simulations | Professional-Machine-Learning-Engineer Authorized Exam Dumps

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Google Professional Machine Learning Engineer Exam is a certification exam offered by Google Cloud for professionals who demonstrate mastery in designing, building, and deploying scalable machine learning models. Professional-Machine-Learning-Engineer Exam is designed to assess the candidate's ability to use Google Cloud's machine learning technologies to develop and deploy production-grade ML models, as well as to optimize and maintain them to ensure their reliability, accuracy, and scalability.

Google Professional Machine Learning Engineer Sample Questions (Q12-Q17):

NEW QUESTION # 12
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

A. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
B. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
C. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
D. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.

Answer: B

Explanation:
Data Catalog is a fully managed and scalable metadata management service that allows you to quickly discover, manage, and understand your data in Google Cloud. You can use Data Catalog to search the BigQuery datasets by using keywords in the table description, as well as other metadata attributes such as table name, column name, labels, tags, and more. Data Catalog also provides a rich browsing experience that lets you explore the schema, preview the data, and access the BigQuery console directly from the Data Catalog UI. Data Catalog helps you find the data that you need for your model building on AI Platform without writing any code or queries.
References:
* [Data Catalog documentation]
* [Data Catalog overview]
* [Searching for data assets]

NEW QUESTION # 13
You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company's products. The workflow logic is shown in the diagram Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

A. Create a custom container endpoint for the workflow that loads each models individual files Track the versions of each individual model in BigQuery.
B. Expose each individual model as an endpoint in Vertex Al Endpoints. Create a custom container endpoint to orchestrate the workflow.
C. Load each model's individual files into Cloud Run Use Cloud Run to orchestrate the workflow Track the versions of each individual model in BigQuery.
D. Expose each individual model as an endpoint in Vertex Al Endpoints. Use Cloud Run to orchestrate the workflow.

Answer: D

Explanation:
The option C is the most efficient and scalable solution for deploying a machine learning workflow with multiple models while ensuring version control and minimizing compute resource utilization. By exposing each model as an endpoint in Vertex AI Endpoints, it allows for easy versioning and management of individual models. Using Cloud Run to orchestrate the workflow ensures that the application can scale down to zero, thus minimizing resource utilization when not in use. Cloud Run is a service that allows you to run stateless containers on a fully managed environment or on Google Kubernetes Engine. You can use Cloud Run to invoke the endpoints of each model in the workflow and pass the data between them. You can also use Cloud Run to handle the input and output of the workflow and provide an HTTP interface for the application.
References:
* Vertex AI Endpoints documentation
* Cloud Run documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 14
You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?

A. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
B. Normalize the data by scaling it to have values between 0 and 1.
C. Standardize the data by transforming it with a logarithmic function.
D. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.

Answer: B

Explanation:
The best option to ensure that the features with the largest magnitude don't overfit the model is to normalize the data by scaling it to have values between 0 and 1. This is also known as min-max scaling or feature scaling, and it can reduce the variance and skewness of the data, as well as improve the numerical stability and convergence of the model. Normalizing the data can also make the model less sensitive to the scale of the features, and more focused on the relative importance of each feature. Normalizing the data can be done using various methods, such as dividing each value by the maximum value, subtracting the minimum value and dividing by the range, or using the sklearn.preprocessing.MinMaxScaler function in Python.
The other options are not optimal for the following reasons:
A . Standardizing the data by transforming it with a logarithmic function is not a good option, as it can distort the distribution and relationship of the data, and introduce bias and errors. Moreover, the logarithmic function is not defined for negative or zero values, which can limit its applicability and cause problems for the model.
B . Applying a principal component analysis (PCA) to minimize the effect of any particular feature is not a good option, as it can reduce the interpretability and explainability of the data and the model. PCA is a dimensionality reduction technique that transforms the data into a new set of orthogonal features that capture the most variance in the data. However, these new features are not directly related to the original features, and can lose some information and meaning in the process. Moreover, PCA can be computationally expensive and complex, and may not be necessary for the problem at hand.
C . Using a binning strategy to replace the magnitude of each feature with the appropriate bin number is not a good option, as it can lose the granularity and precision of the data, and introduce noise and outliers. Binning is a discretization technique that groups the continuous values of a feature into a finite number of bins or categories. However, this can reduce the variability and diversity of the data, and create artificial boundaries and gaps that may not reflect the true nature of the data. Moreover, binning can be arbitrary and subjective, and depend on the choice of the bin size and number.
Reference:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization sklearn.preprocessing.MinMaxScaler documentation Principal Component Analysis Explained Visually Binning Data in Python

NEW QUESTION # 15
You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

Answer: D

Explanation:
To log the metrics of a machine learning model in TensorFlow using the Vertex AI Python SDK, you should utilize the aiplatform.log_metrics function to log the F1 score and aiplatform.log_classification_metrics function to log the confusion matrix. These functions allow users to manually record and store evaluation metrics for each model, facilitating an efficient comparison based on specific performance indicators like F1 scores and confusion matrices. References: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI and TensorFlow.
* Vertex AI Python SDK reference | Google Cloud
* Logging custom metrics | Vertex AI
* Migrating from scikit-learn to TensorFlow | TensorFlow

NEW QUESTION # 16
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

A. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
B. Initialize the model with random weights in all layers including the last fully connected layer.
C. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
D. Initialize the model with random weights in all layers and replace the last fully connected layer.

Answer: C

Explanation:
Explanation/Reference:

NEW QUESTION # 17
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LATEST TEST GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER SIMULATIONS | PROFESSIONAL-MACHINE-LEARNING-ENGINEER AUTHORIZED EXAM DUMPS

Latest Test Google Professional-Machine-Learning-Engineer Simulations | Professional-Machine-Learning-Engineer Authorized Exam Dumps