As a Google Professional Data Engineer, you will be required to collect, modify, and distribute data to enable data-driven decision-making. You should be proficient in designing, developing, deploying, securing, and monitoring data processing systems focusing on security and compliance, scalability and efficiency, reliability and fidelity, and flexibility and portability. As a Data Engineer, you should also have the ability to leverage, deploy, and train pre-existing machine learning models on a continuous basis.
What does the exam expect from you?
The Professional Data Engineer exam assesses your ability to do the following:
- Create data processing systems.
- Create and deploy data processing systems.
- Machine learning models must be operationalized.
- Ensure the solution’s quality.
Google Cloud Certified Professional Data Engineer is the most well-known and difficult IT certification exam. Furthermore, the Google Professional-Data-Engineer exam is an expert level certification exam that will assist you in obtaining a high-ranking position in a reputable organisation. It is one of the most prestigious and difficult IT certification exams. However, passing this exam is much more difficult. The difficult part is the breadth and depth of knowledge Google expects of you.
To begin, let us go over the specifics of the Google Cloud Certified Professional Data Engineer certification exam. A candidate has two hours to complete the Google Cloud Certified Professional Data Engineer exam. Furthermore, the exam questions are presented as multiple choice and multiple select. To pass the exam, the candidate must achieve a score of 70%. Furthermore, the exam has a two-year validity period and is available in four languages: English, Japanese, Spanish, and Portuguese. Above all, the exam will set you back $200 USD. Because different exams have different requirements, it is necessary to understand Professional Data Engineer requirements.
The following are the requirements for the specific exam:
- The ideal candidate will be scalable and efficient.
- He or she should be able to design and monitor data processing systems focusing on security.
- Above all, a data engineer should be able to leverage and train pre-existing machine learning models continuously.
To pass the Google Professional Data Engineer exam Testpreptraining has come up with an amazing online course that can help you in learning the concepts easily and pass the exam with flying colors. Let’s have a look at the online course –
This course is a comprehensive introduction to the Google Cloud Platform, with 20 hours of content and 60 demos. The Google Cloud Platform is maybe the best cloud offering for high-end machine learning applications because Google also makes TensorFlow, a popular deep learning technology.
Course Features –
- Certification material – Covers nearly all of the material you should need to pass the Google Data Engineer and Cloud Architect certification tests.
- Compute and Storage – AppEngine, Container Engine (aka Kubernetes), and Compute Engine provide compute and storage.
- Managed Hadoop and Big Data – Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub
- TensorFlow on the Cloud explains what neural networks and deep learning are, how neurons function, and how neural networks are trained.
- StackDriver logging, monitoring, and cloud deployment manager are examples of DevOps tools.
- Identity and Access Management, Identity-Aware Proxying, OAuth, API Keys, and service accounts are all examples of security features.
- Networking – Virtual Private Clouds, shared VPCs, network, transport, and HTTP load balancing; VPN, Cloud Interconnect, and CDN Interconnect
- Hadoop Foundations: A look at the open-source cousins (Hadoop, Spark, Pig, Hive, and YARN).
You will learn and understand the following concepts thoroughly in this course:
- Managed Hadoop apps can be deployed on the Google Cloud.
- TensorFlow can be used to create deep learning models in the cloud.
- Make well-informed decisions about containers, virtual machines, and AppEngine.
- Make use of big data technologies like BigTable, Dataflow, Apache Beam, and Pub/Sub.
Lets now look at the course curriculum –
- Theory, Practice, and Tests
- Why Cloud?
- Hadoop and Distributed Computing
- On-premise, Colocation, or Cloud?
- Introducing the Google Cloud Platform
- Lab: Setting Up A GCP Account
- Lab: Using The Cloud Shell
2. Compute Choices
- Compute Options
- Google Compute Engine (GCE)
- More GCE
- Lab: Creating a VM Instance
- also, Lab: Editing a VM Instance
- furthermore, Lab: Creating a VM Instance Using The Command Line
- moreover, Lab: Creating And Attaching A Persistent Disk
- Google Container Engine – Kubernetes (GKE)
- More GKE
- Lab: Creating A Kubernetes Cluster And Deploying A WordPress Container
- App Engine
- Contrasting App Engine, Compute Engine, and Container Engine
- Lab: Deploy and Run An App Engine App
- Storage Options
- Quick Take
- Cloud Storage
- also, Lab: Working With Cloud Storage Buckets
- furthermore, Lab: Bucket And Object Permissions
- moreover, Lab: Life cycle Management On Buckets
- also, Lab: Running a Program On a VM Instance And Storing Results on Cloud Storage
- Transfer Service
- Lab: Migrating Data Using the Transfer Service
4. Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
- Cloud SQL
- Lab: Creating A Cloud SQL Instance
- also, Lab: Running Commands On Cloud SQL Instance
- furthermore, Lab: Bulk Loading Data Into Cloud SQL Tables
- Cloud Spanner
- More Cloud Spanner
- Lab: Working With Cloud Spanner
5. BigTable ~ HBase = Columnar Store.
- BigTable Intro
- Columnar Store
- Column Families
- BigTable Performance
- Lab: BigTable demo
6. Datastore ~ Document Database
- Lab: Datastore demo
7. BigQuery ~ Hive ~ OLAP
- BigQuery Intro
- also, BigQuery Advanced
- furthermore, Lab: Loading CSV Data Into Big Query
- also, Lab: Running Queries On Big Query
- furthermore, Lab: Loading JSON Data With Nested Tables
- moreover, Lab: Public Datasets In Big Query
- also, Lab: Using Big Query Via The Command Line
- furthermore, Lab: Aggregations And Conditionals In Aggregations
- moreover, Lab: Subqueries And Joins
- also, Lab: Regular Expressions In Legacy SQL
- furthermore, Lab: Using The With Statement For SubQueries
8. Dataflow ~ Apache Beam
- Data Flow Intro
- Apache Beam
- Lab: Running A Python Data flow Program
- also, Lab: Running A Java Data flow Program
- furthermore, Lab: Implementing Word Count In Dataflow Java
- moreover, Lab: Executing The Word Count Dataflow
- also, Lab: Executing MapReduce In Dataflow In Python
- furthermore, Lab: Executing MapReduce In Dataflow In Java
- moreover, Lab: Dataflow With Big Query As Source And Side Inputs
- also, Lab: Dataflow With Big Query As Source And Side Inputs 2
9. Dataproc ~ Managed Hadoop
- Data Proc
- Lab: Creating And Managing A Dataproc Cluster
- also, Lab: Creating A Firewall Rule To Access Dataproc
- furthermore, Lab: Running A PySpark Job OnDataproc
- moreover, Lab: Running ThePySpark REPL Shell And Pig Scripts On Dataproc
- also, Lab: Submitting A Spark Jar ToDataproc
- furthermore, Lab: Working With Dataproc Using TheGCloud CLI
10. Pub/Sub for Streaming.
- also, Lab: Working With Pubsub On The Command Line
- furthermore, Lab: Working WithPubSub Using The Web Console
- moreover, Lab: Setting Up A Pubsub Publisher Using The Python Library
- also, Lab: Setting Up A Pubsub Subscriber Using The Python Library
- furthermore, Lab: Publishing Streaming Data IntoPubsub
- moreover, Lab: Reading Streaming Data FromPubSub And Writing To BigQuery
- also, Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
- furthermore, Lab: Pubsub Source BigQuery Sink
11. Datalab ~ Jupyter
- Data Lab
- also, Lab: Creating And Working On A Datalab Instance
- furthermore, Lab: Importing And Exporting Data Using Datalab
- moreover, Lab: Using the Charting API InDatalab
12. TensorFlow and Machine Learning
- Introducing Machine Learning
- Representation Learning
- NN Introduced
- Introducing TF
- Lab: Simple Math Operations
- Computation Graph
- Lab: Tensors
- Linear Regression Intro
- Placeholders and Variables
- Lab: Placeholders
- also, Lab: Variables
- furthermore, Lab: Linear Regression with Made-up Data
- Image Processing
- Images As Tensors
- Lab: Reading and Working with Images
- Lab: Image Transformations
- Introducing MNIST
- K-Nearest Neighbors as Unsupervised Learning
- One-hot Notation and L1 Distance
- Steps in the K-Nearest-Neighbors Implementation
- Lab: K-Nearest-Neighbors
- Learning Algorithm
- Individual Neuron
- Learning Regression
- Learning XOR
- XOR Trained
13. Regression in TensorFlow
- Lab: Access Data from Yahoo Finance
- Non-TensorFlow Regression
- Lab: Linear Regression – Setting Up a Baseline
- Gradient Descent
- Lab: Linear Regression
- Lab: Multiple Regression in TensorFlow
- Logistic Regression Introduced
- Linear Classification
- Lab: Logistic Regression – Setting Up a Baseline
- Lab: Logistic Regression
- Lab: Linear Regression using Estimators
- Lab: Logistic Regression using Estimators
14. Vision, Translate, NLP, and Speech: Trained ML APIs
- Lab: Taxicab Prediction – Setting up the dataset
- also, Lab: Taxicab Prediction – Training and Running the model
- furthermore, Lab: The Vision, Translate, NLP, and Speech API
- moreover, Lab: The Vision API for Label and Landmark Detection
- Virtual Private Clouds
- VPC and Firewalls
- XPC or Shared VPC
- Types of Load Balancing
- Proxy and Pass-through load balancing
- Internal load balancing
16. Ops and Security
- StackDriver Logging
- Cloud Deployment Manager
- Cloud Endpoints
- Security and Service Accounts
- Auth and End-user accounts
- Identity and Access Management
- Data Protection
17. Appendix: Hadoop Ecosystem
- Introducing the Hadoop Ecosystem
- also, Hadoop
- furthermore, HDFS
- moreover, MapReduce
- also, Yarn
- furthermore, Hive
- moreover, Hive vs. RDBMS
- also, HQL vs. SQL
- furthermore, OLAP in Hive
- moreover, Windowing Hive
- also, Pig
- furthermore, More Pig
- moreover, Spark
- also, More Spark
- furthermore, Streams Intro
- moreover, Microbatches
- also, Window Types
Let us now look at some additional learning resources –
Google Cloud Free Tier–
The Google Cloud Free Tier gives the candidate access to free resources for researching Google Cloud services. This is especially beneficial for candidates who are new to the platform and need to learn the fundamentals. On the other hand, if you’re an existing customer looking to try out new solutions, the Google Cloud Free Tier has you covered.
Google Cloud Essentials–
The candidate will gain hands-on experience with Google Cloud’s fundamental tools and services in this introductory-level quest. The recommended first Quest for a Google Cloud learner is Google Cloud Essentials. This gives the candidate hands-on experience that they can put to use on their first Google Cloud project. From writing Cloud Shell commands and marshaling their first virtual machine to running applications on Kubernetes Engine or with load balancing, they’ve come a long way. All of this is simple with the help of Google Cloud Essential. Because it is the primary introduction to the platform’s fundamental features.
Google Cloud Certified Professional Data Engineer Practice Exams provide candidates with confidence in their preparation. The practice test will assist candidates in identifying their weak points so that they can work on them. There are numerous practice tests available on the internet these days, so the candidate can select which one they prefer. We at Testprep training also provide practice tests, which are extremely beneficial to those who are preparing.