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Google Professional Machine Learning Engineer certification is a highly sought-after certification in the field of machine learning. Google Professional Machine Learning Engineer certification is designed for professionals who are looking to validate their expertise in designing, building and deploying machine learning models using the Google Cloud Platform. Google Professional Machine Learning Engineer certification exam tests the candidate's ability to apply machine learning technologies to real-world scenarios.
To be eligible for the Google Professional-Machine-Learning-Engineer Certification Exam, you need to have at least three years of experience in developing and deploying machine learning models on Google Cloud Platform or a similar platform. You should also have experience in programming languages such as Python, Java, or C++, and have a good understanding of machine learning concepts such as supervised and unsupervised learning, deep learning, and reinforcement learning.
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NEW QUESTION # 248
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?
Answer: D
NEW QUESTION # 249
You were asked to investigate failures of a production line component based on sensor readings.
After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents.
You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
Answer: A
Explanation:
- less than 1% of the readings are positive
- none of them converge.
Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.
https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting
NEW QUESTION # 250
You recently deployed a model lo a Vertex Al endpoint and set up online serving in Vertex Al Feature Store. You have configured a daily batch ingestion job to update your featurestore During the batch ingestion jobs you discover that CPU utilization is high in your featurestores online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?
Answer: D
NEW QUESTION # 251
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?
Answer: D
Explanation:
According to the web search results, Vertex AI Pipelines is a serverless orchestrator for running ML pipelines, using either the KFP SDK or TFX1. Vertex AI Pipelines provides a set of prebuilt components that can be used to perform common ML tasks, such as training, evaluation, deployment, and more2. Vertex AI ModelEvaluationOp and ModelDeployOp are two such components that can be used to evaluate and deploy a model to an endpoint for online inference3. However, Vertex AI Pipelines does not provide a prebuilt component for hyperparameter tuning. Therefore, to have control over how the model parameters are tuned, you need to use a custom component that calls the Vertex AI HyperparameterTuningJob service4. Therefore, option A is the best way to decide which Google Cloud pipeline components to use for the given use case, as it includes a custom component for hyperparameter tuning, and prebuilt components for model evaluation and deployment. The other options are not relevant or optimal for this scenario. References:
* Vertex AI Pipelines
* Google Cloud Pipeline Components
* Vertex AI ModelEvaluationOp and ModelDeployOp
* Vertex AI HyperparameterTuningJob
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 252
Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?
Answer: B
Explanation:
According to the web search results, Cloud Build1 is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories2, Cloud Storage, GitHub, Bitbucket, or any publicly hosted Git repository. Cloud Build allows you to create and manage build triggers, which are automated workflows that run whenever a code change is pushed to your source repository. You can use Cloud Build triggers to automatically retrain your ML models whenever there is any modification of the code. Therefore, option B is the best way to set up the CI pipeline for the given use case, as it allows you to configure a Cloud Build trigger with the event set as "Push to a branch", which means the trigger will run whenever a new commit is pushed to a specific branch of your source repository. The other options are not relevant or optimal for this scenario. Reference:
Cloud Build
Cloud Source Repositories
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 253
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