Test Professional-Machine-Learning-Engineer Book | Study Professional-Machine-Learning-Engineer Plan

Test Professional-Machine-Learning-Engineer Book | Study Professional-Machine-Learning-Engineer Plan

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The Google Professional Machine Learning Engineer Exam consists of multiple-choice questions and a practical exam that requires candidates to solve real-world problems using Google Cloud’s machine learning technologies. Passing this exam demonstrates that the candidate has the knowledge and skills required to design, build, and deploy production-grade ML models on Google Cloud, and can be trusted to lead machine learning projects in a professional setting.

The 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. This 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.

Google Professional Machine Learning Engineer Sample Questions (Q59-Q64):

An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Choose two.)

  • A. The Latent Dirichlet Allocation (LDA) algorithm
  • B. The k-means algorithm
  • C. The factorization machines (FM) algorithm
  • D. The principal component analysis (PCA) algorithm
  • E. The Random Cut Forest (RCF) algorithm

Answer: B,D

The PCA and K-means algorithms are useful in collection of data using census form.

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

Answer: D


You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

  • A. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
  • B. 1 = Dataflow, 2 – Al Platform, 3 = BigQuery
  • C. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
  • D. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

Answer: B

You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

  • A. Three individual features binned latitude, binned longitude, and one-hot encoded car type
  • B. One feature obtained as an element-wise product between latitude, longitude, and car type
  • C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
  • D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

Answer: C


You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

  • A. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
  • B. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets
  • C. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
  • D. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets

Answer: C



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