VALID TEST PROFESSIONAL-MACHINE-LEARNING-ENGINEER BRAINDUMPS, PROFESSIONAL-MACHINE-LEARNING-ENGINEER SIMULATIONS PDF

Valid Test Professional-Machine-Learning-Engineer Braindumps, Professional-Machine-Learning-Engineer Simulations Pdf

Valid Test Professional-Machine-Learning-Engineer Braindumps, Professional-Machine-Learning-Engineer Simulations Pdf

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To be eligible for the Google Professional-Machine-Learning-Engineer Certification Exam, you need to have at least three years of experience in developing and deploying machine learning models on Google Cloud Platform or a similar platform. You should also have experience in programming languages such as Python, Java, or C++, and have a good understanding of machine learning concepts such as supervised and unsupervised learning, deep learning, and reinforcement learning.

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Google Professional Machine Learning Engineer Sample Questions (Q18-Q23):

NEW QUESTION # 18
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

  • A. Recurrent Neural Networks (RNN)
  • B. Recommender system
  • C. Reinforcement learning
  • D. Convolutional Neural Networks (CNN)

Answer: D


NEW QUESTION # 19
You have successfully deployed to production a large and complex TensorFlow model trained on tabular dat a. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?

  • A. Implement continuous retraining of the model daily using Vertex AI Pipelines.
  • B. Add a model monitoring job where 10% of incoming predictions are sampled every hour.
  • C. Add a model monitoring job where 10% of incoming predictions are sampled 24 hours.
  • D. Add a model monitoring job where 90% of incoming predictions are sampled 24 hours.

Answer: D


NEW QUESTION # 20
You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?

  • A. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
  • B. Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.
  • C. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
  • D. Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

Answer: C

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Cloud Data Loss Prevention (DLP) API
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


NEW QUESTION # 21
You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

  • A. Import the model into Vertex Al On Vertex Al Pipelines, create a pipeline that uses the DatafIowPythonJobOp and the ModelBatchPredictOp components.
  • B. Import the model into Vertex Al and deploy it to a Vertex Al endpoint Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint and then uploads predictions to a BigQuery table.
  • C. Import the model into Vertex Al and deploy it to a Vertex Al endpoint On Vertex Al Pipelines create a pipeline that uses the Dataf lowPythonJobop and the Mcdei3archPredictoc components.
  • D. Import the model into BigQuery Implement the data processing logic in a SQL query On Vertex Al Pipelines create a pipeline that uses the BigqueryQueryJobop and the EigqueryPredictModejobOp components.

Answer: C


NEW QUESTION # 22
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

  • A. AutoML Vision Edge mobile-versatile-1 model
  • B. AutoML Vision model
  • C. AutoML Vision Edge mobile-high-accuracy-1 model
  • D. AutoML Vision Edge mobile-low-latency-1 model

Answer: D

Explanation:
AutoML Vision Edge is a service that allows you to create custom image classification and object detection models that can run on edge devices, such as mobile phones, tablets, or IoT devices1. AutoML Vision Edge offers four types of models that vary in size, accuracy, and latency: mobile-versatile-1, mobile-low-latency-1, mobile-high-accuracy-1, and mobile-core-ml-low-latency-12. Each model has its own trade-offs and use cases, depending on the device specifications and the application requirements.
For the use case of building an ML model to reduce the amount of time spent by quality control inspectors checking for product defects, the best model to use is the AutoML Vision Edge mobile-low-latency-1 model. This model is optimized for fast inference on mobile devices, with a latency of less than 50 milliseconds on a Pixel 1 phone2. Faster defect detection is a priority for the toy manufacturer, and the factory does not have reliable Wi-Fi, so a low-latency model that can run on the device without internet connection is ideal. The mobile-low-latency-1 model also has a small size of less than 4 MB, which makes it easy to deploy and update2. The mobile-low-latency-1 model has a slightly lower accuracy than the mobile-high-accuracy-1 model, but it is still suitable for most image classification tasks2. Therefore, the AutoML Vision Edge mobile-low-latency-1 model is the best option for this use case.
Reference:
AutoML Vision Edge documentation
AutoML Vision Edge model types


NEW QUESTION # 23
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