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Preparation Process

The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.

The Professional Machine Learning Engineer exam is a performance-based assessment that evaluates the candidate's ability to solve real-world problems using machine learning techniques. Professional-Machine-Learning-Engineer Exam consists of a series of hands-on tasks that require the candidate to demonstrate their understanding of various machine learning concepts and their ability to apply them in practical scenarios. Professional-Machine-Learning-Engineer exam is conducted online and can be taken from anywhere in the world.

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To become a Google Professional Machine Learning Engineer, candidates must have a deep understanding of machine learning concepts, including supervised and unsupervised learning, deep learning, and reinforcement learning, as well as experience in using Google Cloud's machine learning tools, such as TensorFlow, Cloud ML Engine, and BigQuery. They must also have a strong understanding of software engineering principles, such as version control, testing, and deployment, and be able to apply these principles to machine learning models.

Google Professional Machine Learning Engineer Sample Questions (Q105-Q110):

NEW QUESTION # 105
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?

A. Use Al Platform Notebooks to run the classification model with pandas library
B. Use Al Platform to run the classification model job configured for hyperparameter tuning
C. Run a BigQuery ML task to perform logistic regression for the classification
D. Configure AutoML Tables to perform the classification task

Answer: D

Explanation:
https://cloud.google.com/automl-tables/docs/beginners-guide

NEW QUESTION # 106
You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline'?

Answer: B

NEW QUESTION # 107
You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process.
You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

A. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.
B. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.
C. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.
D. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.

Answer: A

Explanation:
The simplest way to deploy a logistic regression model with BigQuery ML to production while adding minimal latency is to export the model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline. This option has the following advantages:
* It allows the model prediction to be performed in real time, as part of the Dataflow streaming pipeline that processes the ticket purchase requests. This ensures that the promo code offer is based on the most recent data and customer behavior, and that the offer is delivered to the customer without delay.
* It leverages the compatibility and performance of TensorFlow and Dataflow, which are both part of the Google Cloud ecosystem. TensorFlow is a popular and powerful framework for building and deploying machine learning models, and Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. By using the tfx_bsl.public.beam.RunInference step, you can easily integrate your TensorFlow model with your Dataflow pipeline, and take advantage of the parallelism and scalability of Dataflow.
* It simplifies the model deployment and management, as the model is packaged with the Dataflow pipeline and does not require a separate service or endpoint. The model can be updated by redeploying the Dataflow pipeline with a new model version.
The other options are less optimal for the following reasons:
* Option A: Running batch inference with BigQuery ML every five minutes on each new set of tickets issued introduces additional latency and complexity. This option requires running a separate BigQuery job every five minutes, which can incur network overhead and latency. Moreover, this option requires storing and retrieving the intermediate results of the batch inference, which can consume storage space and increase the data transfer time.
* Option C: Exporting the model in TensorFlow format, deploying it on Vertex AI, and querying the prediction endpoint from the streaming pipeline introduces additional latency and cost. This option requires creating and managing a Vertex AI endpoint, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, querying the Vertex AI endpoint from the streaming pipeline requires making an HTTP request, which can incur network overhead and latency. Moreover, this option requires paying for the Vertex AI endpoint usage, which can increase the cost of the model deployment.
* Option D: Converting the model with TensorFlow Lite (TFLite), and adding it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub introduces additional challenges and risks. This option requires converting the model to a TFLite format, which is a lightweight and optimized format for running TensorFlow models on mobile and embedded devices. However, converting the model to TFLite may not preserve the accuracy or functionality of the original model, as some operations or features may not be supported by TFLite. Moreover, this option requires updating the mobile app with the TFLite model, which can be tedious and time-consuming, and may depend on the user's willingness to update the app. Additionally, this option may expose the model to potential security or privacy issues, as the model is running on the user's device and may be accessed or modified by malicious actors.
References:
* [Exporting models for prediction | BigQuery ML]
* [tfx_bsl.public.beam.run_inference | TensorFlow Extended]
* [Vertex AI documentation]
* [TensorFlow Lite documentation]

NEW QUESTION # 108
A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.
How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

A. Use CMD configin the Dockerfile to add the training program as a CMD of the image
B. Configure the training program as an ENTRYPOINTnamed train
C. Modify the bash_profile file in the container and add a bashcommand to start the training program
D. Copy the training program to directory /opt/ml/train

Answer: A

NEW QUESTION # 109
You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

A. 1. Create a new service account and grant it the Notebook Viewer role.
2 Grant the Service Account User role to each team member on the service account.
3 Grant the Vertex Al User role to each team member.
4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
B. 1 Create a new service account and grant it the Vertex Al User role.
2 Grant the Service Account User role to each team member on the service account.
3. Grant the Notebook Viewer role to each team member.
4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
C. 1. Grant the Vertex Al User role to the default Compute Engine service account.
2. Grant the Service Account User role to each team member on the default Compute Engine service account.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.
D. 1 Grant the Vertex Al User role to the primary team member.
2. Grant the Notebook Viewer role to the other team members.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.

Answer: B

Explanation:
To create a Vertex AI Workbench environment for your team and limit access to other employees in your project, you should follow these steps:
Create a new service account and grant it the Vertex AI User role. This role grants full access to all resources in Vertex AI, including creating and managing notebook instances1.
Grant the Service Account User role to each team member on the service account. This role allows the team members to impersonate the service account and use its permissions2.
Grant the Notebook Viewer role to each team member. This role allows the team members to view and connect to the notebook instance, but not to modify or delete it3.
Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account. This way, the notebook instance will run as the service account and only the team members who have the Service Account User and Notebook Viewer roles will be able to access it.
Reference:
1: Vertex AI access control with IAM | Google Cloud
2: Understanding service accounts | Cloud IAM Documentation
3: Manage access to a Vertex AI Workbench instance | Google Cloud
[4]: Create and manage Vertex AI Workbench instances | Google Cloud

NEW QUESTION # 110
......

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Professional-Machine-Learning-Engineer Valid Test Papers & Hottest Professional-Machine-Learning-Engineer Certification

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Google Professional-Machine-Learning-Engineer Exam Questions [2025] Right Preparation Material

Google Professional Machine Learning Engineer Sample Questions (Q105-Q110):

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