GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER RELIABLE EXAM BRAINDUMPS - PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM OBJECTIVES PDF

Google Professional-Machine-Learning-Engineer Reliable Exam Braindumps - Professional-Machine-Learning-Engineer Exam Objectives Pdf

Google Professional-Machine-Learning-Engineer Reliable Exam Braindumps - Professional-Machine-Learning-Engineer Exam Objectives Pdf

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The Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice test software keeps track of each previous attempt and highlights the improvements with each attempt. The Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) mock exam setup can be configured to a particular style and arrive at unique questions. ActualtestPDF Google Professional-Machine-Learning-Engineer practice exam software went through real-world testing with feedback from more than 90,000 global professionals before reaching its latest form. The Google Professional-Machine-Learning-Engineer Exam Dumps are similar to real exam questions. Our Google Professional-Machine-Learning-Engineer practice test software is suitable for computer users with a Windows operating system.

Achieving the Google Professional Machine Learning Engineer Certification is a significant accomplishment for professionals in the field of machine learning. It demonstrates a high level of expertise in designing, implementing, and deploying machine learning models using Google Cloud Platform. Google Professional Machine Learning Engineer certification also provides opportunities for career advancement and recognition as a leader in the field of machine learning.

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Originating the Professional-Machine-Learning-Engineer exam questions of our company from tenets of offering the most reliable backup for customers, and outstanding results have captured exam candidates’ heart for their functions. Our Professional-Machine-Learning-Engineer practice materials can be subdivided into three versions. All those versions of usage has been well-accepted by them. They are the PDF, Software and APP online versions of our Professional-Machine-Learning-Engineer Study Guide.

Topics of Professional Machine Learning Engineer - Google

Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:

  • ML Solution Architecture
  • ML Model Development
  • ML Solution Monitoring, Optimization, and Maintenance
  • Data Preparation and Processing
  • ML Pipeline Automation & Orchestration

Google Professional Machine Learning Engineer Certification Exam is an important credential for professionals who want to demonstrate their expertise in machine learning. Professional-Machine-Learning-Engineer Exam covers a range of topics and requires significant preparation, but it is a valuable asset for professionals who want to advance their career in machine learning. With this certification, candidates can showcase their skills and knowledge to potential employers and demonstrate their ability to design and develop machine learning models on the Google Cloud Platform.

Google Professional Machine Learning Engineer Sample Questions (Q126-Q131):

NEW QUESTION # 126
You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?

  • A. Write a query that preprocesses the data by using BigQuery and creates a new table Create a Vertex Al managed dataset with the new table as the data source.
  • B. Write a query that preprocesses the data by using BigQuery Export the query results as CSV files and use those files to create a Vertex Al managed dataset.
  • C. Use a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library Export the data as CSV files, and use those files to create a Vertex Al managed dataset.
  • D. Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket.

Answer: A

Explanation:
The simplest and most efficient approach for preparing the data for AutoML is to use BigQuery and Vertex AI. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery can preprocess the data by using SQL functions such as filtering, aggregating, joining, transforming, and creating new features. The preprocessed data can be stored in a new table in BigQuery, which can be used as the data source for Vertex AI. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a BigQuery table, which can be used to train an AutoML model. Vertex AI can also evaluate, deploy, and monitor the AutoML model, and provide online or batch predictions. By using BigQuery and Vertex AI, users can leverage the power and simplicity of Google Cloud to train an AutoML model to predict house prices.
The other options are not as simple or efficient as option A, for the following reasons:
* Option B: Using Dataflow to preprocess the data and write the output in TFRecord format to a Cloud Storage bucket would require more steps and resources than using BigQuery and Vertex AI. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows. TFRecord is a binary file format that can store sequential data efficiently. However, using Dataflow and TFRecord would require writing code, setting up a pipeline, choosing a runner, and managing the output files. Moreover, TFRecord is not a supported format for Vertex AI managed datasets, so the data would need to be converted to CSV or JSONL files before creating a Vertex AI managed dataset.
* Option C: Writing a query that preprocesses the data by using BigQuery and exporting the query results as CSV files would require more steps and storage than using BigQuery and Vertex AI. CSV is a text file format that can store tabular data in a comma-separated format. Exporting the query results as CSV files would require choosing a destination Cloud Storage bucket, specifying a file name or a wildcard, and setting the export options. Moreover, CSV files can have limitations such as size, schema, and encoding, which can affect the quality and validity of the data. Exporting the data as CSV files would also incur additional storage costs and reduce the performance of the queries.
* Option D: Using a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library and exporting the data as CSV files would require more steps and skills than using BigQuery and Vertex AI. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. Pandas is a popular Python library that can manipulate and analyze data in a tabular format.
However, using Vertex AI Workbench and pandas would require creating a notebook instance, writing Python code, installing and importing pandas, connecting to BigQuery, loading and preprocessing the data, and exporting the data as CSV files. Moreover, pandas can have limitations such as memory usage, scalability, and compatibility, which can affect the efficiency and reliability of the data processing.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 1: Introduction to Data Engineering for ML
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.3 Training models by using AutoML
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Low- code ML Solutions, Section 4.3: AutoML
* BigQuery
* Vertex AI
* Dataflow
* TFRecord
* CSV
* Vertex AI Workbench
* Pandas


NEW QUESTION # 127
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?

  • A. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
  • B. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
  • C. Use the Vertex AI Training to submit training jobs using any framework.
  • D. Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.

Answer: C

Explanation:
The best option for using a managed service to submit training jobs with different frameworks is to use Vertex AI Training. Vertex AI Training is a fully managed service that allows you to train custom models on Google Cloud using any framework, such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. You can also use custom containers to run your own libraries and dependencies. Vertex AI Training handles the infrastructure provisioning, scaling, and monitoring for you, so you can focus on your model development and optimization.
Vertex AI Training also integrates with other Vertex AI services, such as Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Prediction. The other options are not as suitable for using a managed service to submit training jobs with different frameworks, because:
* Configuring Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob would require more infrastructure maintenance, as Kubeflow is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as you would have to pay for the cluster resources, regardless of the training job usage. TFJob is also mainly designed for TensorFlow models, and might not support other frameworks as well as Vertex AI Training.
* Creating a library of VM images on Compute Engine, and publishing these images on a centralized repository would require more development time and effort, as you would have to create and maintain different VM images for different frameworks and libraries. You would also have to manually configure and launch the VMs for each training job, and handle the scaling and monitoring yourself. This would not leverage the benefits of a managed service, such as Vertex AI Training.
* Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure would require more configuration and administration, as Slurm is not a native Google Cloud service, and you would have to install and manage it on your own VMs or clusters. Slurm is also a general-purpose workload manager, and might not have the same level of integration and optimization for ML frameworks and libraries as Vertex AI Training. References:
* Vertex AI Training | Google Cloud
* Kubeflow on Google Cloud | Google Cloud
* TFJob for training TensorFlow models with Kubernetes | Kubeflow
* Compute Engine | Google Cloud
* Slurm Workload Manager


NEW QUESTION # 128
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

  • A. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
  • B. This is a good result because the accuracy across both groups is greater than 80%.
  • C. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
  • D. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

Answer: A


NEW QUESTION # 129
You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

  • A. Embed the augmentation functions dynamically in the tf.Data pipeline.
  • B. Use Dataflow to create all possible augmentations, and store them as TFRecords.
  • C. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.
  • D. Embed the augmentation functions dynamically as part of Keras generators.

Answer: A

Explanation:
The best option for optimizing the data processing pipeline for run time and compute resources utilization is to embed the augmentation functions dynamically in the tf.Data pipeline. This option has the following advantages:
* It allows the data augmentation to be performed on the fly, without creating or storing additional copies of the data. This saves storage space and reduces the data transfer time.
* It leverages the parallelism and performance of the tf.Data API, which can efficiently apply the augmentation functions to multiple batches of data in parallel, using multiple CPU cores or GPU
* devices. The tf.Data API also supports various optimization techniques, such as caching, prefetching, and autotuning, to improve the data processing speed and reduce the latency.
* It integrates seamlessly with the TensorFlow and Keras models, which can consume the tf.Data datasets as inputs for training and evaluation. The tf.Data API also supports various data formats, such as images, text, audio, and video, and various data sources, such as files, databases, and web services.
The other options are less optimal for the following reasons:
* Option B: Embedding the augmentation functions dynamically as part of Keras generators introduces some limitations and overhead. Keras generators are Python generators that yield batches of data for training or evaluation. However, Keras generators are not compatible with the tf.distribute API, which is used to distribute the training across multiple devices or machines. Moreover, Keras generators are not as efficient or scalable as the tf.Data API, as they run on a single Python thread and do not support parallelism or optimization techniques.
* Option C: Using Dataflow to create all possible augmentations, and store them as TFRecords introduces additional complexity and cost. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow to create all possible augmentations requires generating and storing a large number of augmented images, which can consume a lot of storage space and incur storage and network costs. Moreover, using Dataflow to create the augmentations requires writing and deploying a separate Dataflow pipeline, which can be tedious and time-consuming.
* Option D: Using Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords introduces additional complexity and latency. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow to create the augmentations dynamically per training run requires running a Dataflow pipeline every time the model is trained, which can introduce latency and delay the training process. Moreover, using Dataflow to create the augmentations requires writing and deploying a separate Dataflow pipeline, which can be tedious and time-consuming.
References:
* [tf.data: Build TensorFlow input pipelines]
* [Image augmentation | TensorFlow Core]
* [Dataflow documentation]


NEW QUESTION # 130
You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible What should you do?

  • A. Use Tabular Workflow forTabel through Vertex Al Pipelines to train attention-based models.
  • B. Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models.
  • C. Use Cloud Composer to build the training pipelines for custom deep learning-based models.
  • D. Use Tabular Workflow for Wide & Deep through Vertex Al Pipelines to jointly train wide linear models and deep neural networks.

Answer: C

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "automate and orchestrate ML pipelines using Cloud Composer". Cloud Composer2 is a fully managed workflow orchestration service that uses Apache Airflow to create, schedule, monitor, and manage workflows.Cloud Composer allows you to build custom training pipelines for deep learning-based models and integrate them with other Google Cloud services. You can also use Cloud Composer to implement model interpretability techniques, such as feature attributions, explainable AI, or model debugging3. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Cloud Composer
* Model interpretability with Cloud Composer
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


NEW QUESTION # 131
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