Note that this exam was retired by Microsoft on June 30, 2022 and has not been replaced by any Python specific certification. However, after we will maintain a core LDI Python syllabus similar to the legacy 98-381 course as we believe a fundamental foundation in Python is essential for data science.

Click Here to be taken to the LDI Quizzes and Courses for this syllabus

Exam 98-381: Introduction to Programming Using Python

Audience Profile
Candidates for this exam should be able to recognize and write syntactically correct Python code, recognize data types supported by Python, and be able to recognize and write Python code that will logically solve a given problem.

Candidates are expected to have had, at a minimum, instruction and/or hands-on experience of approximately 100 hours with the Python programming language, be familiar with its features and capabilities, and understand how to write, debug, and maintain well-formed, well documented Python code.

Skills Measured
NOTE: The bullets that appear below each of the skills measured are intended to illustrate how we are assessing that skill. This list is not definitive or exhaustive.

Perform Operations using Data Types and Operators (20-25%)

Evaluate an expression to identify the data type Python will assign to each variable

  • identify str, int, float, and bool data types

Perform data and data type operations

  • convert from one data type to another type; construct data structures; perform indexing and slicing operations

Determine the sequence of execution based on operator precedence

  • assignment; comparison; logical; arithmetic; identity (is); containment (in)

Select the appropriate operator to achieve the intended result

  • assignment; comparison; logical; arithmetic; identity (is); containment (in)

Control Flow with Decisions and Loops (25-30%)

Construct and analyze code segments that use branching statements

  • if; elif; else; nested and compound conditional expressions

Construct and analyze code segments that perform iteration

  • while; for; break; continue; pass; nested loops and loops that include compound conditional expressions

Document and Structure Code (15-20%)

Document code segments using comments and documentation strings

  • use indentation, white space, comments, and documentation strings; generate documentation by using pydoc

Construct and analyze code segments that include function definitions

  • call signatures; default values; return; def; pass

Perform Troubleshooting and Error Handling (5-10%)

Analyze, detect, and fix code segments that have errors

  • syntax errors; logic errors; runtime errors

Analyze and construct code segments that handle exceptions

  • try; except; else; finally; raise

Perform Operations Using Modules and Tools (1-5%)

Perform basic operations using built-in modules

  • math; datetime; io; sys; os; os.path; random

Solve complex computing problems by using built-in modules

  • math; datetime; random