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Google Professional Machine Learning Engineer Certification Exam is a certification program designed to validate the skills and knowledge of professionals in the field of machine learning engineering. Google Professional Machine Learning Engineer certification is offered by Google Cloud and is intended for individuals who have experience in building and deploying machine learning models using Google Cloud technologies. Google Professional Machine Learning Engineer certification exam assesses the candidate's ability to design, build, and operate machine learning solutions that meet business objectives.

Google Professional Machine Learning Engineer exam is an advanced-level certification offered by Google Cloud. Google Professional Machine Learning Engineer certification is aimed at individuals who have extensive experience in machine learning and are seeking to become experts in this field. Professional-Machine-Learning-Engineer Exam is designed to test the candidate's proficiency in designing, building, and deploying machine learning models using Google Cloud technologies.

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Who should take the Professional Machine Learning Engineer - Google

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.

The Google Professional-Machine-Learning-Engineer Exam is for entry-level IT specialists and organization professionals with standard knowledge of the Google platform. The Google CCP certification validates the potential client's understanding of these topics and their skills; standard building principles, key services and also their use cases, security, and protection, as well as compliance with the Google model, paid versions, and prices. Google Professional-Machine-Learning-Engineer exam is the appropriate starting point for Google certification and is also an excellent resource for those interested in non-technical projects.

Google Professional Machine Learning Engineer Sample Questions (Q324-Q329):

NEW QUESTION # 324
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

A. Import your user events and then your product catalog to make sure you have the highest quality event stream.
B. Use the "Frequently Bought Together" recommendation type to increase the shopping cart size for each order.
C. Use the "Other Products You May Like" recommendation type to increase the click-through rate.
D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Answer: B

Explanation:
https://cloud.google.com/recommendations-ai/docs/placements#rps

NEW QUESTION # 325
You need to deploy a scikit-learn classification model to production. The model must be able to serve requests 24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?

A. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1
B. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 100.
C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 1.
D. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100

Answer: C

NEW QUESTION # 326
You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)

A. 501*256+257*128+2 = 161154
B. 501*256+257*128+128*2=161408
C. 500*256+256*128+128*2 = 161024
D. 500*256*0 25+256*128*0 25+128*2 = 40448

Answer: C

Explanation:
The number of trainable weights in a DNN regression model with Keras APIs can be calculated by multiplying the number of input units by the number of output units for each layer, and adding the number of bias units for each layer. The bias units are usually equal to the number of output units, except for the last layer, which does not have bias units if the activation function is softmax1. In this code, the model has three layers: a dense layer with 256 units and relu activation, a dropout layer with 0.25 rate, and a dense layer with 2 units and softmax activation. The input shape is 500. Therefore, the number of trainable weights is:
* For the first layer: 500 input units * 256 output units + 256 bias units = 128256
* For the second layer: The dropout layer does not have any trainable weights, as it only randomly sets some of the input units to zero to prevent overfitting2.
* For the third layer: 256 input units * 2 output units + 0 bias units = 512 The total number of trainable weights is 128256 + 512 = 161024. Therefore, the correct answer is B.
References:
* How to calculate the number of parameters for a Convolutional Neural Network?
* Dropout (keras.io)

NEW QUESTION # 327
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

A. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets
B. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
C. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
D. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets

Answer: B

NEW QUESTION # 328
You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

A. Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.
B. Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
C. Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
D. Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.

Answer: A

Explanation:
According to the official exam guide 1 , one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". TensorFlow Transform 2 is a library for preprocessing data with TensorFlow. TensorFlow Transform enables you to define and execute distributed pre-processing or feature engineering functions on large data sets, and then export the same functions as a TensorFlow graph for re-use during training or serving. TensorFlow Transform can handle both instance-level and full-pass data transformations. Apache Beam 3 is an open source framework for building scalable and portable data pipelines. Apache Beam supports both batch and streaming data processing. Dataflow 4 is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Therefore, option D is the best way to configure the preprocessing routine for the given use case, as it allows you to use the same preprocessing logic during model training and serving, and leverage the scalability and performance of Dataflow. The other options are not relevant or optimal for this scenario. References :
* Professional ML Engineer Exam Guide
* TensorFlow Transform
* Apache Beam
* Dataflow
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 329
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