Google Professional Machine Learning Engineer Practice Test Questions, Google Professional Machine Learning Engineer Exam Dumps
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To help you become a certified Professional Machine Learning Engineer, Google offers the certification path that you can pursue by passing one exam. If the test is successfully completed, a potential candidate can earn this Google Cloud certificate and show off his/her skills. Being this type of specialist means that you are able to design, build, and productionize the ML models in order to solve any business challenges using the Google Cloud technologies and knowledge of the proven ML techniques and models. To be able to understand the concepts covered in the qualifying exam, you need to have a certain level of expertise. It is recommended that you possess more than 3 years of industry experience, including more than a year of designing solutions using Google Cloud and managing them. There are no other requirements that you should fulfill to sit for the test.
There is only one qualification exam that you need to take to be eligible for the certification. It contains the questions of various types, including multiple select and multiple choice. To complete it, you will be given 120 minutes, so you need to have good test-taking and time management skills. The exam is available in the English language only, and you should choose the option that you prefer the most from the available delivery methods. The individuals can opt for the online variant and take the test from a place they are comfortable with or go for the onsite proctored exam that can be scheduled to complete at a testing center. Also, you will need to pay $200 as the registration fee.
To prepare for the Professional Machine Learning Engineer exam with great deliberation, you will need to use reliable study options. Therefore, the vendor provides all the interested candidates with the opportunity to enroll for the training courses. There are 5 courses in total, and you can take the whole learning path to gain the required skills. Thus, you will be able to learn Cloud SQL, BigQuery, Dataproc, and other ML products and take your first steps with the Google Cloud tools. Also, you will be able to learn how to build a machine learning-focused strategy as well as progress into the model training, optimization, and productionalization. There are also the training options that teach you how to build a scalable, accurate, and production-ready model and help you know best practices for deploying, monitoring, operating and evaluating the production ML systems on Google Cloud.
If you want to prepare for the exam and make sure you are studying the right topics, you need to read the official guide. It is available on the vendor’s website and covers the following domains:
- Framing ML problems
This section is all about ML problems, business success criteria, risks to feasibility of ML solutions, and translation of the business challenges into the ML use cases. That is why your skills in choosing the best solution, identifying data sources, as well as assessing and communicating the business impact will be evaluated. Also, you should have knowledge of the input (features) & predicted output format and problem type and be ready to handle with the incorrect results. It is also important to know how the model output should be used to solve the business problems.
- Architecting ML solutions
To deal with this part of the exam, the applicants should know about reliable, scalable, and highly available ML solutions, know how to choose the appropriate Google Cloud hardware components, and design architecture, which complies with the security concerns across sectors/industries. This means that you should have the ability to choose the appropriate ML services for the use case and perform various tasks that include orchestration, feature engineering, monitoring, exploration & analysis, automation, and more. Besides that, you need to know how to build the secure ML systems.
- Designing data preparation & processing systems
As for this topic, it is essential to have the abilities to explore data, build data pipelines, and create the input features. Therefore, a potential candidate needs to know about such processes as data validation, visualization, feature selection, data leakage, handling outliers & missing data, as well as organization & optimization of training datasets. You should also be able to ensure consistent data pre-processing between serving and training, encode structured data types, and establish data constraints.
- Developing ML models
For this objective, you need to have the skills in building models, training, and testing them, as well as scaling model training and serving. That is why you should know about hyperparameter tuning, transfer learning, distributed training, data augmentation, choice of framework & model, as well as retraining/redeployment evaluation. The students should also know how to ingest various file types into the training and model techniques given interpretability requirements.
- Automating & orchestrating ML pipelines
In the next domain, you are required to learn all about the design and implementation of training pipelines, tracking and auditing of metadata, as well as implementation of serving pipelines. It is vital to have the skills in identifying the triggers, components, parameters, and compute needs, hooking into model & dataset versioning, and organizing and tracking experiments & pipeline runs. You should know about the Google Cloud serving options, orchestration framework, model/dataset lineage, and system design with TFX components/Kubeflow DSL.
- Monitoring, maintaining, and optimizing ML solutions
This last subject area available in the exam covers the details of monitoring and troubleshooting ML solutions as well as tuning the performance of ML solutions for training & serving in production. This means that you have to be able to establish continuous evaluation metrics, understand the Google Cloud permissions model, and identify the appropriate retraining policy. This module also includes the information about the simplification techniques, ML model failure and resulting biases, and logging strategies.
Further Career Path
Getting the Google Professional Machine Learning Engineer certificate means that you are ready to perform the skills required for this path. This certification also gives you plenty of advantages, including a better salary and the opportunity to land a new job. You can opt for various positions, such as a MacOS GPU Performance Engineer, a Distinguished Machine Learning Engineer, a Machine Learning Software Engineer, an Applied Machine Learning Researcher, a C++ Software Engineer, or a Speech Scientist/Engineer. The potential average income for the certificate holders will be approximately $146,085 per year.
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