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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Professional Machine Learning Engineer |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Available Languages: | Japanese, English |
| Certificate Validity Period: | 2 years |
| Passing Score: | Not publicly disclosed (Pass/Fail) |
| Related Certifications: | Google Cloud Certified - Professional Data Engineer |
| Real Exam Qty: | 50-60 |
| Exam Price: | $200 USD |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple choice, Multiple select |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online (proctored) or Test center (Kryterion) |
| Pre Condition: | Recommended 3+ years of industry experience with ML models and 1+ year of experience using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
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The Google Professional Machine Learning Engineer certification exam is intended for machine learning engineers, data scientists, and software engineers who are interested in designing and building scalable and efficient machine learning models on the Google Cloud Platform. Candidates who pass the certification exam will be able to demonstrate their proficiency in machine learning and will be recognized as a Google Professional Machine Learning Engineer.
Google Professional Machine Learning Engineer Exam is a certification exam designed to validate an individual's expertise in machine learning engineering. Professional-Machine-Learning-Engineer exam aims to assess the candidate's ability to create and deploy highly scalable, robust, and maintainable machine learning models using Google Cloud Platform technologies. Professional-Machine-Learning-Engineer Exam also tests the candidate's proficiency in designing and implementing machine learning architectures, solving business problems using machine learning, and optimizing machine learning workflows.
Individuals who pass the Google Professional Machine Learning Engineer Certification Exam will receive a certificate that validates their expertise in the field of machine learning. The certificate is recognized by Google Cloud Platform and is a valuable asset for individuals who are seeking career advancement opportunities in the field of machine learning. Google Professional Machine Learning Engineer certification exam is a challenging but rewarding experience that can help individuals take their career to the next level.
NEW QUESTION # 92
Your company needs to generate product summaries for vendors. You evaluated a foundation model from Model Garden for text summarization but found that the summaries do not align with your company's brand voice. How should you improve this LLM-based summarization model to better meet your business objectives?
Answer: A
Explanation:
Fine-tuning the model with a company-specific dataset aligns the model outputs with the brand voice, making it better suited for the company's objectives. Adjusting the temperature (Option A) affects randomness rather than content style, and changing token limits (Option C) does not impact tone. Replacing the model (Option D) is inefficient without guarantees of better alignment.
NEW QUESTION # 93
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?
Answer: A
Explanation:
Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters. You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups. You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.
For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.
Reference:
Kubeflow Pipelines documentation
Experiment | Kubeflow
NEW QUESTION # 94
You have developed an ML model to detect the sentiment of users' posts on your company's social media page to identify outages or bugs. You are using Dataflow to provide real-time predictions on data ingested from Pub/Sub. You plan to have multiple training iterations for your model and keep the latest two versions live after every run. You want to split the traffic between the versions in an 80:20 ratio, with the newest model getting the majority of the traffic. You want to keep the pipeline as simple as possible, with minimal management required. What should you do?
Answer: B
Explanation:
The recommended approach to achieve the desired outcome would be to deploy the ML models to a Vertex AI endpoint and configure the traffic splitting using the traffic-split parameter. The traffic-split parameter enables you to split traffic between multiple versions of a model based on a percentage split. In this case, the newest model should receive the majority of the traffic, which can be achieved by setting the traffic-split parameter to 0=80. The previous version of the model should receive the remaining 20% of the traffic, which can be achieved by setting the PREVIOUS_MODEL_ID parameter to 20.
NEW QUESTION # 95
You are a lead ML architect at a small company that is migrating from on-premises to Google Cloud. Your company has limited resources and expertise in cloud infrastructure. You want to serve your models from Google Cloud as soon as possible. You want to use a scalable, reliable, and cost-effective solution that requires no additional resources. What should you do?
Answer: D
Explanation:
Deploying models on Vertex AI endpoints provides a fully managed, scalable, and reliable serving solution without requiring you to manage infrastructure or cluster resources. This allows rapid deployment with minimal operational overhead, ideal for organizations with limited cloud expertise and resources.
NEW QUESTION # 96
While conducting an exploratory analysis of a dataset, you discover that categorical feature A has substantial predictive power, but it is sometimes missing. What should you do?
Answer: B
NEW QUESTION # 97
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