VALID PROFESSIONAL-MACHINE-LEARNING-ENGINEER DUMPS | LATEST PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM TESTKING

Valid Professional-Machine-Learning-Engineer Dumps | Latest Professional-Machine-Learning-Engineer Exam Testking

Valid Professional-Machine-Learning-Engineer Dumps | Latest Professional-Machine-Learning-Engineer Exam Testking

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These Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice test questions are customizable and give real Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) exam experience. Windows computers support desktop software. The web-based Professional-Machine-Learning-Engineer Practice Exam is supported by all browsers and operating systems.

To be eligible for the Google Professional Machine Learning Engineer Certification Exam, you must have a strong background in software engineering, data modeling, and statistics. You must also have hands-on experience working with machine learning frameworks such as TensorFlow or PyTorch, and be familiar with cloud computing platforms such as Google Cloud Platform.

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Passing an exam requires diligent practice, and using the right study Google Certification Exams material is crucial for optimal performance. With this in mind, RealExamFree has introduced a range of innovative Professional-Machine-Learning-Engineer practice test formats to help candidates prepare for their Professional-Machine-Learning-Engineer. The platform offers three distinct formats, including a desktop-based Google Professional-Machine-Learning-Engineer practice test software, a web-based practice test, and a convenient PDF format.

What is the duration, language, and format of Professional Machine Learning Engineer - Google

  • No negative marking for wrong answers
  • Language of Exam: English, Japanese, Korean
  • Duration of Exam: 120 minutes
  • Type of Questions: Multiple choice (MCQs), multiple answers

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

NEW QUESTION # 82
You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?

  • A. Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline.
  • B. Use the func_to_container_op function to create custom components from the Python code.
  • C. Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function.
  • D. Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there.

Answer: B

Explanation:
The easiest way to integrate custom Python code into the Kubeflow Pipelines SDK is to use the func_to_container_op function, which converts a Python function into a pipeline component. This function automatically builds a Docker image that executes the Python function, and returns a factory function that can be used to create kfp.dsl.ContainerOp instances for the pipeline. This option has the following benefits:
It allows the data science team to reuse their existing Python code without rewriting it or packaging it into containers manually.
It simplifies the component specification and implementation, as the function signature defines the component interface and the function body defines the component logic.
It supports various types of inputs and outputs, such as primitive types, files, directories, and dictionaries.
The other options are less optimal for the following reasons:
Option B: Using the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there, introduces additional complexity and cost. This option requires creating and managing Dataproc clusters, which are ephemeral and scalable clusters of Compute Engine instances that run Apache Spark and Apache Hadoop. Moreover, this option requires writing the custom code in PySpark or Hadoop MapReduce, which may not be compatible with the existing Python code.
Option C: Packaging the custom Python code into Docker containers, and using the load_component_from_file function to import the containers into the pipeline, introduces additional steps and overhead. This option requires creating and maintaining Dockerfiles, building and pushing Docker images, and writing component specifications in YAML files. Moreover, this option requires managing the dependencies and versions of the Python code and the Docker images.
Option D: Deploying the custom Python code to Cloud Functions, and using Kubeflow Pipelines to trigger the Cloud Function, introduces additional latency and limitations. This option requires creating and deploying Cloud Functions, which are serverless functions that execute in response to events. Moreover, this option requires invoking the Cloud Functions from the Kubeflow Pipelines using HTTP requests, which can incur network overhead and latency. Additionally, this option is subject to the quotas and limits of Cloud Functions, such as the maximum execution time and memory usage.
Reference:
Building Python function-based components | Kubeflow
Building Python Function-based Components | Kubeflow


NEW QUESTION # 83
Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

  • A. Raise the threshold for comments to be considered toxic or harmful
  • B. Remove the model and replace it with human moderation.
  • C. Add synthetic training data where those phrases are used in non-toxic ways
  • D. Replace your model with a different text classifier.

Answer: C

Explanation:
This approach would help to improve the performance of the classifier by providing it with more examples of the religious phrases being used in non-toxic ways. This would allow the classifier to better differentiate between toxic and non-toxic comments that reference these religious groups. Additionally, synthetic data is a cost-effective way to improve the performance of an existing model without requiring a significant investment in human resources.


NEW QUESTION # 84
You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

  • A. Measure the effect of each feature as the weight of the feature multiplied by the feature value.
  • B. Configure sampled Shapley explanations on Vertex Explainable AI.
  • C. Train local surrogate models to explain individual predictions.
  • D. Configure integrated gradients explanations on Vertex Explainable AI.

Answer: B

Explanation:
Option A is incorrect because training local surrogate models to explain individual predictions is not a feature of Vertex Explainable AI, but rather a general technique for interpreting black-box models. Local surrogate models are simpler models that approximate the behavior of the original model around a specific input1.
Option B is correct because configuring sampled Shapley explanations on Vertex Explainable AI is a way to explain the difference between the actual prediction and the average prediction for a given input. Sampled Shapley explanations are based on the Shapley value, which is a game-theoretic concept that measures how much each feature contributes to the prediction2. Vertex Explainable AI supports sampled Shapley explanations for tabular data, such as customer churn3.
Option C is incorrect because configuring integrated gradients explanations on Vertex Explainable AI is not suitable for explaining the difference between the actual prediction and the average prediction for a given input. Integrated gradients explanations are based on the idea of computing the gradients of the prediction with respect to the input features along a path from a baseline input to the actual input4. Vertex Explainable AI supports integrated gradients explanations for image and text data, but not for tabular data3.
Option D is incorrect because measuring the effect of each feature as the weight of the feature multiplied by the feature value is not a valid way to explain the difference between the actual prediction and the average prediction for a given input. This method assumes that the model is linear and additive, which is not the case for an ensemble of trees and neural networks. Moreover, this method does not account for the interactions between features or the non-linearity of the model5.
Reference:
Local surrogate models
Shapley value
Vertex Explainable AI overview
Integrated gradients
Feature importance


NEW QUESTION # 85
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

  • A. Use L1 regularization to reduce the coefficients of uninformative features to 0.
  • B. Use Principal Component Analysis to eliminate the least informative features.
  • C. After building your model, use Shapley values to determine which features are the most informative.
  • D. Use an iterative dropout technique to identify which features do not degrade the model when removed.

Answer: C


NEW QUESTION # 86
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 CodeStar
  • B. Amazon S3
  • C. Amazon ECS
  • D. AWS Secrets Manager
  • E. Amazon ECR

Answer: B,E


NEW QUESTION # 87
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