Google Cloud Certified Professional Cloud Architect Study Guide, 2nd Edition
- Length: 368 pages
- Edition: 2
- Language: English
- Publisher: Sybex
- Publication Date: 2022-04-12
- ISBN-10: 1119871050
- ISBN-13: 9781119871057
- Sales Rank: #1153837 (See Top 100 Books)
An indispensable guide to the newest version of the Google Certified Professional Cloud Architect certification
The newly revised Second Edition of the Google Cloud Certified Professional Cloud Architect Study Guide delivers a proven and effective roadmap to success on the latest Professional Cloud Architect accreditation exam from Google. You’ll learn the skills you need to excel on the test and in the field, with coverage of every exam objective and competency, including focus areas of the latest exam such as Kubernetes, Anthos, and multi-cloud architectures. The book explores the design, analysis, development, operations, and migration components of the job, with intuitively organized lessons that align with the real-world job responsibilities of a Google Cloud professional and with the PCA exam topics. Architects need more than the ability to recall facts about cloud services, they need to be able to reason about design decisions. This study guide is unique in how it helps you learn to think like an architect: understand requirements, assess constraints, choose appropriate architecture patterns, and consider the operational characteristics of the systems you design. Review questions and practice exams use scenario-based questions like those on the certification exam to build the test taking skills you will need.
In addition to comprehensive material on compute resources, storage systems, networks, security, legal and regulatory compliance, reliability design, technical and business processes, and more, you’ll get:
- The chance to begin or advance your career as an in-demand Google Cloud IT professional
- Invaluable opportunities to develop and practice the skills you’ll need as a Google Cloud Architect
- Access to the Sybex online learning center, with chapter review questions, full-length practice exams, hundreds of electronic flashcards, and a glossary of key terms
The ideal resource for anyone preparing for the Professional Cloud Architect certification from Google, Google Cloud Certified Professional Cloud Architect Study Guide, 2nd Edition is also a must-read resource for
Preface Who This Book Is For Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments 1. Making Better Decisions Based on Data Many Similar Decisions The Role of Data Scientists Scrappy Environment Full Stack Cloud Data Scientists Collaboration Best Practices Simple to Complex Solutions Cloud Computing Serverless A Probabilistic Decision Probabilistic Approach Probability Density Function Cumulative Distribution Function Choices Made Choosing Cloud Not a Reference Book Getting Started with the Code Agile Architecture for Data Science on Google Cloud What Is Agile Architecture? No-Code, Low-Code Use Managed Services Summary Suggested Resources 2. Ingesting Data into the Cloud Airline On-Time Performance Data Knowability Causality Training–Serving Skew Downloading Data Hub-and-Spoke Architecture Dataset Fields Separation of Compute and Storage Scaling Up Scaling Out with Sharded Data Scaling Out with Data-in-Place Ingesting Data Reverse Engineering a Web Form Dataset Download Exploration and Cleanup Uploading Data to Google Cloud Storage Loading Data into Google BigQuery Advantages of a Serverless Columnar Database Staging on Cloud Storage Access Control Ingesting CSV Files Partitioning Scheduling Monthly Downloads Ingesting in Python Cloud Run Securing Cloud Run Deploying and Invoking Cloud Run Scheduling Cloud Run Summary Code Break Suggested Resources 3. Creating Compelling Dashboards Explain Your Model with Dashboards Why Build a Dashboard First? Accuracy, Honesty, and Good Design Loading Data into Cloud SQL Create a Google Cloud SQL Instance Create Table of Data Interacting with the Database Querying Using BigQuery Schema Exploration Using Preview Using Table Explorer Creating BigQuery View Building Our First Model Contingency Table Threshold Optimization Building a Dashboard Getting Started with Data Studio Creating Charts Adding End-User Controls Showing Proportions with a Pie Chart Explaining a Contingency Table Modern Business Intelligence Digitization Natural Language Queries Connected Sheets Summary Suggested Resources 4. Streaming Data: Publication and Ingest with Pub/Sub and Dataflow Designing the Event Feed Transformations Needed Architecture Getting Airport Information Sharing Data Time Correction Apache Beam/Cloud Dataflow Parsing Airports Data Adding Time Zone Information Converting Times to UTC Correcting Dates Creating Events Reading and Writing to the Cloud Running the Pipeline in the Cloud Publishing an Event Stream to Cloud Pub/Sub Speed-Up Factor Get Records to Publish How Many Topics? Iterating Through Records Building a Batch of Events Publishing a Batch of Events Real-Time Stream Processing Streaming in Dataflow Windowing a Pipeline Streaming Aggregation Using Event Timestamps Executing the Stream Processing Analyzing Streaming Data in BigQuery Real-Time Dashboard Summary Suggested Resources 5. Interactive Data Exploration with Vertex AI Workbench Exploratory Data Analysis Exploration with SQL Reading a Query Explanation Exploratory Data Analysis in Vertex AI Workbench Jupyter Notebooks Creating a Notebook Jupyter Commands Installing Packages Jupyter Magic for Google Cloud Exploring Arrival Delays Basic Statistics Plotting Distributions Quality Control Arrival Delay Conditioned on Departure Delay Evaluating the Model Random Shuffling Splitting by Date Training and Testing Summary Suggested Resources 6. Bayesian Classifier with Apache Spark on Cloud Dataproc MapReduce and the Hadoop Ecosystem How MapReduce Works Apache Hadoop Google Cloud Dataproc Need for Higher-Level Tools Jobs, Not Clusters Preinstalling Software Quantization Using Spark SQL JupyterLab on Cloud Dataproc Independence Check Using BigQuery Spark SQL in JupyterLab Histogram Equalization Bayesian Classification Bayes in Each Bin Evaluating the Model Dynamically Resizing Clusters Comparing to Single Threshold Model Orchestration Submitting a Spark Job Workflow Template Cloud Composer Autoscaling Serverless Spark Summary Suggested Resources 7. Logistic Regression Using Spark ML Logistic Regression How Logistic Regression Works Spark ML Library Getting Started with Spark Machine Learning Spark Logistic Regression Creating a Training Dataset Training the Model Predicting Using the Model Evaluating a Model Feature Engineering Experimental Framework Feature Selection Feature Transformations Feature Creation Categorical Variables Repeatable, Real Time Summary Suggested Resources 8. Machine Learning with BigQuery ML Logistic Regression Presplit Data Interrogating the Model Evaluating the Model Scale and Simplicity Nonlinear Machine Learning XGBoost Hyperparameter Tuning Vertex AI AutoML Tables Time Window Features Taxi-Out Time Compounding Delays Causality Time Features Departure Hour Transform Clause Categorical Variable Feature Cross Summary Suggested Resources 9. Machine Learning with TensorFlow in Vertex AI Toward More Complex Models Preparing BigQuery Data for TensorFlow Reading Data into TensorFlow Training and Evaluation in Keras Model Function Features Inputs Training the Keras Model Saving and Exporting Deep Neural Network Wide-and-Deep Model in Keras Representing Air Traffic Corridors Bucketing Feature Crossing Wide-and-Deep Classifier Deploying a Trained TensorFlow Model to Vertex AI Concepts Uploading Model Creating Endpoint Deploying Model to Endpoint Invoking the Deployed Model Summary Suggested Resources 10. Getting Ready for MLOps with Vertex AI Developing and Deploying Using Python Writing model.py Writing the Training Pipeline Predefined Split AutoML Hyperparameter Tuning Parameterize Model Shorten Training Run Metrics During Training Hyperparameter Tuning Pipeline Best Trial to Completion Explaining the Model Configuring Explanations Metadata Creating and Deploying Model Obtaining Explanations Summary Suggested Resources 11. Time-Windowed Features for Real-Time Machine Learning Time Averages Apache Beam and Cloud Dataflow Reading and Writing Time Windowing Machine Learning Training Machine Learning Dataset Training the Model Streaming Predictions Reuse Transforms Input and Output Invoking Model Reusing Endpoint Batching Predictions Streaming Pipeline Writing to BigQuery Executing Streaming Pipeline Late and Out-of-Order Records Possible Streaming Sinks Summary Suggested Resources 12. The Full Dataset Four Years of Data Creating Dataset Training Model Evaluation Summary Suggested Resources Conclusion A. Considerations for Sensitive Data Within Machine Learning Datasets Handling Sensitive Information Sensitive Data in Columns Sensitive Data in Natural Language Datasets Sensitive Data in Free-Form Unstructured Data Sensitive Data in a Combination of Fields Sensitive Data in Unstructured Content Protecting Sensitive Data Removing Sensitive Data Masking Sensitive Data Coarsening Sensitive Data Establishing a Governance Policy Index About the Author
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