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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.

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Determination of when a model is deemed unsuccessful
• Assessing data readiness
• Define business success criteria
• Assessing ML solution readiness
• Defining output use
• Define ML problem
• Success metrics
• Identifying data sources
• Defining outcome of model predictions
• Aligning with Google AI principles and practices (e.g. different biases)
• Identifying nonML solutions
• Defining problem type (classification, regression, clustering, etc.)
• Identify risks to feasibility and implementation of ML solution. Considerations include:
• Key results
• Managing incorrect results

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Career Bonuses

The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.

Google Professional Machine Learning Engineer Sample Questions (Q82-Q87):

NEW QUESTION # 82

Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

• A. Kubeflow Pipelines and Al Platform Prediction
• B. Cloud Composer, BigQuery ML , and Al Platform Prediction
• C. Cloud Composer, Al Platform Training with custom containers , and App Engine
• D. Kubeflow Pipelines and App Engine

Answer: A

NEW QUESTION # 83

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:



You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

A)



B)



C)



D)

• A. Option A
• B. Option D
• C. Option B
• D. Option C

Answer: C

Explanation:

If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. https://developers.google.com/machine-learning/crash-course/18th-century-literature For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use

NEW QUESTION # 84

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

• A. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
• B. Rewrite your input function to resize and reshape the input images.
• C. Rewrite your input function using parallel reads, parallel processing, and prefetch.
• D. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.

Answer: D

NEW QUESTION # 85

You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.

The proposed architecture has the following flow:



Which endpoints should the Enrichment Cloud Functions call?

• A. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API
• B. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision
• C. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API
• D. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language

Answer: C

Explanation:

https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow:

A user writes a ticket to Firebase, which triggers a Cloud Function.

-The Cloud Function calls 3 different endpoints to enrich the ticket:

-An AI Platform endpoint, where the function can predict the priority.

-An AI Platform endpoint, where the function can predict the resolution time.

-The Natural Language API to do sentiment analysis and word salience.

-For each reply, the Cloud Function updates the Firebase real-time database.

-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

NEW QUESTION # 86

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:



A)



B)



C)



D)

• A. Option D
• B. Option A
• C. Option C
• D. Option B

Answer: A

NEW QUESTION # 87

......

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