Snowpro Specialty: Snowpark Certification Exam (Sps-B01)

Course and Practice Exams, to help you become a Snowpark Specialist with a new Snowflake certification

What you'll learn

How to pass the new SnowPro Snowpark Specialist certification exam, issued recently by Snowflake

How to get your first advanced Specialized certification in Snowflake AI Data Cloud

How to acquire a hard specialized certification in Snowflake AI Data Cloud programming

Master Snowpark Python for yourself at the programming level

Requirements

The SnowPro Core certification is a pre-requisite for this specialty certification

Basic Python and SQL programming skills

Optional PySpark programming skills

Optional prior knowledge of Pandas

Optional data science and machine learning experience

Description

Who this course is forPeople trying to pass the new SnowPro Snowpark Specialist certification exam issued recently by Snowflake.Snowflake experts trying to improve their programming skills learning Snowpark.Python developers willing to acquire a certification in Snowflake AI Data Cloud programming.Data Engineers and AI or ML Engineers.Data Scientists and Data Application Developers.Data Analysts with some Python and SQL programming experience.This is not an introduction to Snowflake, as you should already have some rather advanced knowledge on this platform. Passing the SnowPro Core certification exam is also a requirement for this SnowPro Specialty: Snowpark Certification Exam (SPS-B01) advanced specialty exam.About your InstructorMy name is Cristian Scutaru and I’m a world-class expert in Snowflake, SnowPro SME (Subject Matter Expert) and former Snowflake Data Superhero.For several years, I helped Snowflake create many of the exam questions out there. Many of the Advanced exam questions and answers from the SnowPro exams have been indeed created by me.I passed over the years 5 out of the 6 SnowPro exams myself, all from the first attempt. In the last 3-4 years alone, I passed over 40 paid proctored certification exams overall. And I still never failed one, I was lucky so far.The course also contains one high-quality practice test with 30 exam-like questionsAll questions are closely emulated from those currently found in the actual SnowPro Specialty: Snowpark certification exam.All questions are curated and very similar to the actual Snowpark Specialist exam questions.Many exam questions are long and scenario-based.Most exam questions include long portions of code, as this certification is targeted in particular for Python developers.Detailed explanations with external references for any possible choice, in each practice test question.Quiz question types are mostly multi-choice and multi-select.Specifics of the real examAnnounced on Oct 21, 2024Between 65 and 80 questions (80 for the beta exam)Less than 2 hours time limit (115 minutes for the beta exam)Passing score of around 75% (must be estimated later on)$112 US fee per attempt for the beta exam (until Dec 20, 2024)or $225 US fee per attempt when going live (since Jan 2025)What the exam will test you forSpecialized knowledge, skills, and best practices used to build Snowpark DataFrame data solutions in Snowflake.Key Snowflake concepts, features, and programming constructs.Perform data transformations using Snowpark DataFrame functions.Query data sources as Snowpark DataFrame objects.Connect to Snowflake using a Snowpark Session object.Process results client-side or persist results in Snowflake through Snowpark DataFrame actions.Design a sequence of operations or conditional logic with Snowpark stored procedures.What the typical candidate may have1+ years of Snowpark experience with Snowflake, in an enterprise environment.Knowledge of the Snowpark API for Python and Snowpark’s client-side and server-side capabilities.Some experience with data migration.Advanced proficiency writing code in Python and/or PySpark.Exam domain breakdown (from the Study Guide)Snowpark Concepts - 15%Snowpark API for Python - 30%Snowpark for Data Transformations - 35%Snowpark Performance Optimization - 20%

Overview

Section 1: Introduction

Lecture 1 Course Structure and Content

Lecture 2 About the SnowPro Snowpark Certification

Lecture 3 About your Instructor

Lecture 4 How to Benefit Most from this Course

Lecture 5 Environment Setup

Lecture 6 Frequently Asked Questions

Section 2: Snowpark DataFrame Transformations

Lecture 7 Introduction to Snowpark DataFrame Transformations

Lecture 8 Snowpark DataFrame API Overview

Lecture 9 DataFrame Selections and Actions

Lecture 10 Filter and Transform Data

Lecture 11 Joins and Sets on DataFrames

Lecture 12 Clean and Sample Data with DataFrames

Lecture 13 DataFrame Aggregations

Lecture 14 Window Functions in Snowpark

Section 3: Snowpark DataFrame Integrations

Lecture 15 Introduction to Snowpark DataFrame Integrations

Lecture 16 Snowpark vs Pandas DataFrames

Lecture 17 Create Snowpark DataFrames

Lecture 18 Persist the Results of Snowpark DataFrames

Lecture 19 DML Operations using Snowpark DataFrames

Lecture 20 Process Semi-Structured Data from DataFrames

Section 4: Procedures and Functions in Snowpark

Lecture 21 Introduction to Procedures and Functions in Snowpark

Lecture 22 Create Procedures and Functions in SQL

Lecture 23 Call Procedures and Functions in Snowpark

Lecture 24 Python Worksheets

Lecture 25 Create Stored Procedures in Snowpark

Lecture 26 Create User-Defined Functions in Snowpark

Lecture 27 Vectorized User-Defined Functions

Lecture 28 Secure Stored Procedures and Functions

Lecture 29 Stages and File Operations

Lecture 30 Packages and Imports

Lecture 31 Run Stored Procedures in Snowpark

Section 5: Snowpark Configuration and Environments

Lecture 32 Introduction to Snowpark Configuration and Environments

Lecture 33 Snowpark-Optimized Warehouses

Lecture 34 Snowflake APIs

Lecture 35 Snowpark Sessions and Setup

Lecture 36 Observability in Snowpark

Lecture 37 Snowpark Test Environments

Lecture 38 Snowpark for Machine Learning

Section 6: Wrapping Up

Lecture 39 Sample Questions with Answers

Lecture 40 Congratulations, You Made It!

Lecture 41 Bonus Lecture

Data Engineers and AI or ML Engineers,Data Scientists and Data Application Developers,Data Analysts with some Python and SQL programming experience