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How much Professional Machine Learning Engineer - Google Cost

The cost of the Professional Machine Learning Engineer - Google is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.

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The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.

How to book the Professional Machine Learning Engineer - Google

To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:

  • Step 1: Go to the Google Official Site
  • Step 2: Read the instruction carefully
  • Step 3: Follow the given steps
  • Step 4: Apply for the Professional Machine Learning Engineer Exam

Google Professional Machine Learning Engineer Sample Questions (Q105-Q110):

You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

  • A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
  • B. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
  • C. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
  • D. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Answer: B


A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream.
As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

  • A. Decrease the retention period for the data stream.
  • B. Increase the number of shards for the data stream.
  • C. Increase the number of S3 prefixes for the delivery stream to write to.
  • D. Add more consumers using the Kinesis Client Library (KCL).

Answer: B


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?

  • A. Downsample the data with upweighting to create a sample with 10% positive examples
  • B. Use a convolutional neural network with max pooling and softmax activation
  • C. Remove negative examples until the numbers of positive and negative examples are equal
  • D. Use the class distribution to generate 10% positive examples

Answer: B

Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

  • A. Cloud Composer, Al Platform Training with custom containers , and App Engine
  • B. Cloud Composer, BigQuery ML , and Al Platform Prediction
  • C. Vertex AI Pipelines and Al Platform Prediction
  • D. Vertex AI Pipelines and App Engine

Answer: C

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

  • A. Use the Natural Language API to classify support requests
  • B. Use an established text classification model on Al Platform as-is to classify support requests
  • C. Use an established text classification model on Al Platform to perform transfer learning
  • D. Use AutoML Natural Language to build the support requests classifier

Answer: C

the model cannot work as-is as the classes to predict will likely not be the same; we need to use transfer learning to retrain the last layer and adapt it to the classes we need


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