Google Cloud Professional Data Engineer Certification Exam - Study guide and Practice tests

I cleared the Professional Data Engineer Certification recently and the question topics are fresh in my mind, I wanted to share with you helpful tips for preparing and ace the certification exam.

Why would you want to do a Google Cloud Professional Data Engineer Certification?

You may already have the skills to use Google Cloud already but how do you demonstrate this to a future employer or client? Two ways. Through a portfolio of projects or a certification.

A certificate says to future clients and employers, ‘Hey, I’ve got the skills and I’ve put in the effort to get accredited

Who is it for ?

If you’re already a data scientist, a data engineer, data analyst, machine learning engineer or looking for a career change into the world of data, the Google Cloud Professional Data Engineer Certification is for you

How much does it cost?

To sit the certification exam costs $200 USD. If you fail, you will have to pay the fee again to resit.

What is the exam format ?

• Length: 2 hours
• Registration fee: $200 (plus tax where applicable)
• Languages: English, Japanese.
• Exam format: 50 Multiple choice and multiple select taken remotely or in person at a test center. Locate a test center near you.
• Exam Delivery Method: 
• Take the online-proctored exam from your home
• Take the onsite-proctored exam at a testing center
• Prerequisites: None
• Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.

Course Topics to Prepare:

You will need to familiar with,

• 5 database products (Bigquery, Bigtable, Cloud SQL, Cloud Spanner, Datastore)
• 3 ETL tools (Dataflow, Dataprep, Dataproc)
• 5 Misc products (Stackdriver, CloudComposer, Cloud Storage, Pub/Sub, IOT Core)
• Machine learning basics and Cloud Basics (eg. zones, regions)

Overwhelmed ? Dont worry, lets take one small step at a time:

Bigquery :

• 30% of questions come from Bigquery, so prepare on the below topics
• Understand what is google Bigquery - Serverless, Petabyte Scale cloud data warehouse
• Bigquery Partitioned tables
• Bigquery Table Clustering
• How to use Partitioning and Clustering to reduce query costs
• Bigquery can automatically recognize schema of JSON files
• Understanding Bigquery Streaming inserts
• Bigquery ML
• Bigquery use cases : Analytics, Easily  works on huge volume of historical data, ANSI SQL Compliant
• Understanding access control of Bigquery tables - How are they managed via IAM and dataset permissions
• Learn about Bigquery Authorized views. How they can be used to grant access to users without granting permissions to underlying tables
• Bigquery logs can be monitored using stackdriver. Understand how to setup stack driver reporting for bytes used or for slot usage
• Understand Bigquery slots quota per project. How does it differ between pay-as-you-go model and fixed price model
• Learn about Bigquery federated querying

Bigtable :

• Understand Google Bigtable - No SQL, low latency database
• Bigtable schema design - Learn the best practices for choosing the row keys
• In general Bigtable performs good for lean and tall tables. So limit the number of columns
• Common use cases - Real time series data, IOT data, real time stock market data
• Understanding the nodes and how to scale write and read operations by choosing the number of nodes and also tweaking the disks between SSD and HDD

Cloud SQL:

• Cloud SQL is good for Transactional databases, where the data volume is less than 4 TB and max concurrent 4000 connections
• Choose Cloud SQL when the customer wants to port the application to cloud as it is and do not want to spend on rewriting the application
• Understand how to install Stackdriver agent to monitor db performance

Cloud Spanner:

• Cloud Spanner is good for transactional databases, which needs unlimited scalabilty and also scale concurrent connections, and when global consistency is needed
• Cloud spanner supports Primary Key and Secondary Key. Learn how to choose these keys and how they impact the data distribution across nodes and retrieval
• Learn how to improve performance by tweaking various options including the number of nodes, disk types etc

Datastore:

• Understand what is Datastore and its use cases

Dataflow:

• Understand what is Dataflow (uses Apache Beam internally)
• Get familiar with various dataflow transformations like IO, sideinputs, sideoutputs, runner
• Get familiar with various types of windowing - fixed, vs sliding vs session windows
• How to increase the performance of dataflow jobs
• How to update a real time streaming job and change the code
• Understand the difference between stopping vs draining a dataflow job
• Get familiar with Pcollections and how are they used

Dataprep:

• Understand what is Dataprep - Mostly used by analysts, who prefer gui, and Dataprep is commonly used for cleansing data whose schema changes frequently
• Dataprep internally uses dataflow to run the pipeline

Dataproc:

• Understand what is Dataproc
• On-prem hadoop instances can be migrated to Dataproc, use cloud storage in lieu of HDFS and use Bigtable to replace Hive
• Best practice to use Dataproc to run only one type of job and kill the cluster on completion
• If cost is an issue you can instantiate worker nodes with preemptive types
• Understand how to run initialization actions on your cluster
• Understand spark job processing and how to improve the performance of the Dataproc cluster

Cloud Storage:

• Thoroughly go through the Cloud Storage. Most questions touch upon Cloud Storage
• Understand the difference between regional and multi regional buckets and the cost associated and what is high availability
• Understand the various storage classes - Standard, Nearline, Coldline, Archive

Pub / Sub:

• Decouples sender and receiver, Stores messages for upto 7 days. Guarantees message delivery. Messages may come out of order
• Used for most of the real time application use cases
• Unserstand Pub/Sub Topics and subscriptions

Misc Products:

Briefly touch upon Stackdriver, CloudComposer and IOT Core

Machine Learning Basics:

• Refresh your machine learning concepts using this quick guide
• Understand when to build your own model vs using Auto ML vs Google ML libraries like (Vision API, translate API etc)
• Learn about Google AI Platform and how to use it to train and serve models
• Learn about the various hardware that google offers for training a model - CPUs, GPUs and TPUs

Good Luck with your exam !

nVector

posted on 24 Jul 20