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Google Professional Machine Learning Engineer Certification Exam is a highly prestigious certification offered by Google. It is designed for individuals who wish to showcase their expertise in the field of machine learning and demonstrate their ability to design, build, and deploy highly scalable and reliable machine learning models. Google Professional Machine Learning Engineer certification exam tests candidates on a variety of topics related to machine learning, including data preprocessing, feature engineering, model selection and evaluation, and deployment and monitoring of machine learning models.

Google Professional Machine Learning Engineer certification is highly respected in the industry and is recognized as a benchmark for excellence in machine learning. Achieving this certification demonstrates to employers and peers that a candidate has the skills and knowledge required to design, build, and deploy machine learning models on Google Cloud Platform. Google Professional Machine Learning Engineer certification is ideal for data scientists, machine learning engineers, software engineers, and other professionals who are looking to enhance their skills in machine learning and advance their career in this field.

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Google Professional Machine Learning Engineer Sample Questions (Q26-Q31):

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

Answer: C


Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:





  • A. Option D
  • B. Option B
  • C. Option C
  • D. Option A

Answer: B

If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set.

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

  • A. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
  • B. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
  • C. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
  • D. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

Answer: D

You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

  • A. Too few layers in the model for capturing information
  • B. Incorrect data split ratio during model training, evaluation, validation, and test
  • C. Lack of model retraining
  • D. Poor data quality

Answer: B

You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

  • A. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
  • B. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
  • C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
  • D. Significantly increase the max_batch_size TensorFlow Serving parameter

Answer: B



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