How I Obtained 4 SnowPro Advanced Certifications (Architect, Administrator, Data Engineer and Data Analyst) in 5 Months

I recently achieved the SnowPro Advanced Data Engineer certification. This post aims to demonstrate how you can study easily, effectively, and efficiently to obtain three certifications that can open doors to a world of job opportunities with major companies migrating their data infrastructure to a robust platform like Snowflake.

First and foremost, I must clarify that although I obtained the certifications in less than 4 months, three months earlier I had failed an exam for the SnowPro Advanced Administrator certification. From that failure, I identified my study mistakes and corrected them, resulting in three consecutive major wins.

The first thing I did was plan the order in which I would take the exams. After reading various posts, analyzing, and researching the official guides, I decided that my first goal would be the Administrator certification, followed by the Architect, and finally the Data Engineer certification. This is because although many concepts overlap in all three exams, from my perspective, the SnowPro Administrator exam assesses the functioning and knowledge of the platform’s management and performance features, while the SnowPro Advanced Architect Certification evaluates how they complement each other when proposing a comprehensive solution for a company. The SnowPro Data Engineer exam focuses more on how to use this knowledge for data loading, transformation, and optimization.

Then I searched for the most useful study material, and undoubtedly, the official study guides provided by Snowflake were invaluable. These guides were my compass, indicating where I should focus my efforts. I must clarify that although I used them in my first failed attempt, I did not fully leverage their value. I focused on reading the documentation without realizing that the most valuable material they offer is the Lab Guides and Additional Assets provided for each domain evaluated in the exam. Reviewing the weight of each domain in the exam was crucial since, for example, when analyzing the SnowPro Advanced Administrator guide, the first three domains had a weight of over 70%, indicating that mastering those three domains would bring me closer to the final goal.

Subsequently, I took the practice tests offered by Snowflake. Although they come at a cost, they are indicative of the types of questions one will encounter in the actual exam. The best part is that I could analyze those answers calmly and correct mistakes before the big day.

I purchased several courses on Udemy, read articles on the domains evaluated in the exam, and sought advice from those who had already taken them. I found six tips that were key for me:

1. When answering the exam, it’s best to eliminate incorrect answers first and then focus on the one(s) that are true. This helps reinforce knowledge of the evaluated topic.
2. Read the questions carefully; there are always small traps in them.
3. Studying in a group can cut the study time by half or more.
4. Practice as many scenarios as possible on a Snowflake trial account.
5. Don’t wait too long between certifications to keep the knowledge fresh.
6. Don’t be afraid to present it; in the end, the only thing you’ll gain is more knowledge.

Now I’m beginning the preparation for the SnowPro Advanced Data Analyst certification, which I will obtain before the end of February. I’ll leverage the fact that I’ve already covered many topics in the previous certifications and pay homage to my past self, remembering that it was there where I started in this exciting world of data.

In summary, this post aims to provide a guide based on what worked for me. I hope it can help others, as these certifications are not easy to obtain, but the effort is worth it for the job opportunities and personal satisfaction they bring.

Update

In March 2024, I obtained the Data Analyst certification. Although I followed a similar study method as before, I had to dedicate additional hours to review concepts that are more specific to the field of data analysis. Some examples include:

• The choice of the appropriate type of chart depending on the context.
• The concept of correlation: what it is, how it is understood, and what it means for it to be negative or positive. Additionally, how it is represented visually in a chart.

It is important to note that an intermediate to advanced level of SQL knowledge is essential. As a Data Analyst, it is crucial to handle JSON inputs, manage NULL fields, and understand table constraints. Query optimization is not optional; it is a requirement.

Finally, obtaining this certification was a gratifying experience, as it allowed me to reaffirm the knowledge I had acquired and pay tribute to the first role I had in the world of data.