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The Google Professional-Machine-Learning-Engineer exam covers a wide range of topics related to machine learning, including data preparation, model design and implementation, model training and evaluation, and deployment and monitoring of machine learning models. Successful candidates will be able to demonstrate their ability to design and implement machine learning models using Google Cloud Platform tools and services, as well as their ability to optimize performance and ensure reliability and scalability of machine learning systems. Google Professional Machine Learning Engineer certification is recognized as a valuable credential for professionals working in the field of machine learning, and it can help to enhance career opportunities and earning potential.

The Google Professional Machine Learning Engineer certification exam covers a wide range of topics related to machine learning engineering, including data preparation and analysis, feature engineering, model selection and training, hyperparameter tuning, deployment, and monitoring. Candidates will be required to demonstrate their ability to develop and manage machine learning models using Google Cloud Platform tools and services. Successful candidates will be able to design, implement, and optimize machine learning models to solve complex business problems and improve operational efficiency. The Google Professional Machine Learning Engineer Certification Exam is an excellent way for individuals to demonstrate their expertise in the field of machine learning engineering and to advance their careers in this rapidly growing field.

The Google Professional-Machine-Learning-Engineer exam is designed to test a variety of skills and knowledge areas related to machine learning, including data analysis, model selection and evaluation, and deployment and monitoring of machine learning models. It is also designed to test candidates' ability to apply machine learning techniques to real-world problems and to demonstrate their ability to work effectively with data science teams.

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Google Professional Machine Learning Engineer Sample Questions (Q31-Q36):

NEW QUESTION # 31
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

  • A. Increase the number of epochs for model training
  • B. Increase the training data by adding variation in rotation for training images.
  • C. Increase the number of layers for the neural network.
  • D. Increase the dropout rate for the second-to-last layer.

Answer: A


NEW QUESTION # 32
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

  • A. Address the model overfitting by using a less complex algorithm.
  • B. Address data leakage by removing features highly correlated with the target value.
  • C. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
  • D. Address data leakage by applying nested cross-validation during model training.

Answer: D

Explanation:
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9


NEW QUESTION # 33
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?

  • A. Ensure that you have GPU quota in the selected region.
  • B. Ensure that you have preemptible GPU quota in the selected region.
  • C. Ensure that the required GPU is available in the selected region.
  • D. Ensure that the selected GPU has enough GPU memory for the workload.

Answer: A


NEW QUESTION # 34
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

  • A. Create an AWS Glue crawler to infer duplicate accounts in the source data.
  • B. Create a FindMatches machine learning transform in AWS Glue.
  • C. Search for duplicate accounts in the AWS Glue Data Catalog.
  • D. Execute the built-in FindDuplicates Amazon Athena query.

Answer: B

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html


NEW QUESTION # 35
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

  • A. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
  • B. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
  • C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
  • D. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.

Answer: B


NEW QUESTION # 36
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