This time I decided to strengthen my knowledge in the field of Data Engineering, and out of the Norwayending list of useful chops a Data Scientist can choose to develop, why Data Engineering?
As a DS( Data Scientist) with 4 times of experience delivering end- to- results to guests, I must confess that I've Norway had the occasion to work in a platoon with a DE( Data mastermind), still, utmost of the systems in need of a DS will also bear more or less trouble in conditioning like ETLs, robotization flows, optimization of logical databases, Spark cluster operation, and more.
Quite frequently, the reality in numerous systems is that a small data engineering workload doesn't justify incorporating a DE into the platoon and these conditions are covered by someone differently. This becomes a great occasion to wear a different chapeau and learn some of these transversal chops.
I decided to challenge my current position of experience in this field by taking the Azure Data mastermind instrument( DP 203) and also to gain further exposure to the Azure ecosystem since this is the favored pall seller in my current design. Thankfully the instruments are covered by my employer so why not give it a shot?
print of my Azure emblem
DP- 203 test prints
The Azure Data Engineering test consists of around 50 questions, including single and multiple-choice questions and a case study with multiple questions. The fleeting score is 700 out of 1000 points.
test DP- 203 Data Engineering on Microsoft Azure- Learn
Languages English, Chinese( Simplified), Japanese, Korean, German, French, Spanish, Portuguese( Brazil), Arabic(Saudi…
To prepare for the test I used the sanctioned MS literacy paths, which I rounded with some practice in Azure to consolidate my knowledge, the usual exploration in Google to dive into new generalities, and skimming through some of the Azure attestations for the most applicable services like Data Factory, Synapse, Azure SQL, or Databricks.
Screenshot from my completed literacy paths
Also, I rehearsed with several test dumps from Udemy, Whizlabs, and Exam Topics. I decided to try several sources and I wasn't completely satisfied with any of them, unfortunately, I came across several questions that sounded inapplicable, were inadequately written, or indeed had questionable answers.
Still, I suppose this step is critical to know what to anticipate in the factual test and find any knowledge gaps.
Compared to my former experience taking the DP100 test( Azure Data Scientist), this was a more grueling test due to the large quantum of motifs in the compass and having lower familiarity with the field.
Overall, I learned numerous intriguing motifs like optimizing databases with indicators, partitions, and distributions, strategies to optimize storehouses, stylish practices to secure data end to end, and more. still, like I always find with these examinations, after a while rather than learning general generalities, your time shifts into preparing veritably specific and frequently pointless questions with
Screenshot from my test score report
So, should you consider this instrument?
Going back to our original question, the answer will be particular and substantially depend on your bournes.
Still, this instrument will expose you to some applicable motifs, still you won't gain the solid foundations that would help you progress in a career as a DE. If you're looking to become a Data mastermind or transition towards that part.
I like to look at these instruments as evidence that you're familiar with the Azure services in question, you're suitable to navigate through the comprehensive Azure ecosystem and can make simple Data Engineering results. In this case, it can help you gain credibility in front of a customer or implicit employer.
Still, from the other side of the table, I would anticipate any DE with work experience and solid foundations to be suitable to decide their knowledge of Azure or any other platform within a reasonable time, and only if I was in critical need of hiring a DE for an Azure design would this instrument catch my eye.
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