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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.

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How to book the Professional Machine Learning Engineer - Google

To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:

• Step 1: Go to the Google Official Site
• Step 2: Read the instruction carefully
• Step 3: Follow the given steps
• Step 4: Apply for the Professional Machine Learning Engineer Exam

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 (Q103-Q108):

NEW QUESTION # 103
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 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language
• B. 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision
• C. 1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API
• D. 1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API

Answer: D

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 # 104
You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

• A. Spam posts can be flagged using far fewer keywords.
• B. New problematic phrases can be identified in spam posts.
• C. Posts can be compared to the keyword list much more quickly.
• D. A much longer keyword list can be used to flag spam posts.

Answer: C

NEW QUESTION # 105
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

• A. Manage all relational entities in the Hive Metastore.
• B. Manage your ML workflows with Vertex ML Metadata.
• C. Store your tf.logging data in BigQuery.
• D. Store all ML metadata in Google Cloud's operations suite.

Answer: D

NEW QUESTION # 106
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

• A. AWS Secrets Manager
• B. Amazon ECS
• C. Amazon S3
• D. AWS CodeStar
• E. Amazon ECR

Answer: C,E

NEW QUESTION # 107
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

• A. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
• B. An optimization objective that maximizes the Precision at a Recall value of 0.50
• C. An optimization objective that minimizes Log loss
• D. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

Answer: A

NEW QUESTION # 108
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