MedWatchers OA - 2025 Data Engineer Role

MedWatchers OA - 2025 Data Engineer Role

MedWatchers OA - 2025 Data Engineer Role

Recently, I participated in an online assessment for a Data Engineer role at MedWatchers, and I must say, it was a bit more challenging than I initially anticipated. While I expected to tackle questions mainly focused on Python and SQL, the assessment included a diverse range of topics. Here’s a breakdown of my experience.

Assessment Structure

The assessment consisted of five questions to be completed within a total time limit of 30 minutes on the TestDome platform. Here’s how the questions were distributed:

  • 1 Question for Python - 14 minutes
  • 1 Question for Bash - 5 minutes
  • 1 Question for SQL - 2 minutes (a bit tight, as it takes time to read)
  • 1 Question for Statistics - 5 minutes
  • 1 Question for Abstract Reasoning - 4 minutes

Overall Difficulty: Moderate

Question 1: Bash Scripting

The first question tested knowledge of Bash scripting, presenting a series of commands and asking about their implications. Here’s the provided script:

bash #!/bin/bash echo “File contents” > .newfile mkdir .newdir echo “More contents” > .newdir/.anotherfile

Based on this script, the following statements were evaluated:

  1. ls .newdir/ will display .anotherfile.
  2. cat .newdir/.anotherfile will display “More contents”.
  3. rmdir .newdir/ will remove both .newdir and .newdir/.anotherfile.
  4. ls -aR will display both .newfile and .newdir/.anotherfile.
  5. rm -rf .newdir will remove both .newdir and .newdir/.anotherfile.

Correct Answers:

  • cat .newdir/.anotherfile will display “More contents”.
  • ls -aR will display both .newfile and .newdir/.anotherfile.
  • rm -rf .newdir will remove both .newdir and .newdir/.anotherfile.

Question 2: Python Problem

The second question involved writing a function to determine how many times people can cross a hypothetical bridge. I had to implement the following function:

python def crossBridge(nums: list[int]) -> int: return (min(nums) - 1) // 2

This concise solution efficiently computes the maximum crossings based on the minimum value in the list, showcasing the importance of understanding problem constraints.

Question 3: SQL Query Analysis

The third question required analyzing an SQL query based on a guestData table containing several records. The task was to identify which SQL queries could return only one record. The table had columns such as guestid, firstName, lastName, age, and contactNumber.

Given the following queries, we needed to select those that would yield exactly one record:

  1. SELECT * FROM guestData WHERE lastName LIKE "m%" OR lastName LIKE "n%"
  2. SELECT * FROM guestData WHERE age BETWEEN 5 AND 16;
  3. SELECT * FROM guestData WHERE age = 16 AND age = 34;
  4. SELECT * FROM guestData WHERE firstName LIKE "a_";
  5. SELECT * FROM guestData WHERE contactNumber LIKE "_75%" OR firstName LIKE "r%";

Correct Answers:

  • SELECT * FROM guestData WHERE age BETWEEN 5 AND 16;
  • SELECT * FROM guestData WHERE age = 16 AND age = 34;

Question 4: Visual Reasoning

The fourth question involved visual reasoning, but I unfortunately cannot recall the specific problem. It typically assesses one’s ability to identify patterns or relationships in visual data.

Question 5: Statistical Analysis

The final question required statistical analysis of three manufacturing methods to determine which method produced the greatest number of parts per hour. The data was presented in a table format, and we were tasked with drawing conclusions based on a 5% significance level.

The data collected was as follows:

Method A Method B Method C
97 93 99
73 100 94
93 93 87
100 55 66
73 77 59
91 91 75
100 85 84
86 73 72

Unlock your potential—schedule 1-on-1 coaching today to master Data Engineering skills!

Schedule Now

Related Posts