PCEA™ – Certified Entry-Level Automation Specialist with Python: EXAM SYLLABUS

Exam Syllabus Contents

• Block 1: Fundamentals of Automation
• Block 2: Basic Command-Line Automation with Python
• Block 3: Logging and Monitoring Essentials
• Block 4: Basic File and Data Automation
• Block 5: Basic Web and API Automation
• Block 6: Scheduling, Notifications, and Reporting

Block 1: Fundamentals of Automation

6 objectives covered by the block → 6 exam items (13% of total exam)

1.1 The Importance of Digitizing Tasks (1)

Objective 1.1.1 – Identify examples of routine and repetitive tasks suitable for automation

1. Provide real-world examples from IT (file backups, log cleanup), business (report generation, data entry), and home contexts (organizing photos, scheduling reminders).
2. Distinguish between tasks that are effective to automate (repetitive, rule-based, time-consuming) and those that are not (creative, judgment-based).

1.2 Benefits and Limitations of Automation (2)

Objective 1.2.1 – Explain the advantages of automation

1. Explain key benefits: consistency, accuracy, speed, scalability.
2. Identify cost savings, time savings, and productivity improvements achieved through automation.
3. Describe industry-wide benefits in IT (system monitoring), finance (data processing), and manufacturing (process control).
4. Describe how automation frees humans from routine tasks to focus on higher-value work.

Objective 1.2.2 – Describe challenges and limitations of automation

1. Identify potential drawbacks: setup cost, script errors, dependency on systems, and maintenance overhead.
2. Explain why automation cannot fully replace human creativity, adaptability, and judgment.
3. Recognize situations where manual intervention remains necessary.

1.3 Levels of Automation (1)

Objective 1.3.1 – Differentiate between scripting, process automation, and orchestration

1. Define basic scripting (e.g., Python scripts for file manipulation).
2. Describe process automation (workflow tools, task schedulers).
3. Explain orchestration (managing multiple processes and systems together).

1.4 Measuring the Value of Automation (1)

Objective 1.4.1 – Apply basic methods to calculate ROI of automation

1. Identify metrics such as time saved, error reduction, and cost savings.
2. Apply formulas to simple ROI scenarios (e.g., hours saved × hourly cost).
3. Interpret whether an automation initiative delivers measurable value.

1.5 Python as a Tool for Automation (1)

Objective 1.5.1 – Explain why Python is widely used for automation

1. Describe Python’s strengths: readability, cross-platform compatibility, comprehensive standard library, and strong community support.
2. List popular Python libraries for automation: subprocess, os, shutil, logging, requests, and schedule.
3. Explain how Python can integrate with operating system commands, APIs, and external tools.

Block 2: Basic Command-Line Automation with Python

9 objectives covered by the block → 9 exam items (19.5% of total exam)

2.1 Running Python Scripts from the Command Line (3)

Objective 2.1.1 – Execute Python scripts using terminal/command prompt

1. Demonstrate how to run a script with python script.py.
2. Demonstrate running scripts from different directories.
3. Diagnose and resolve common errors (wrong path, missing interpreter).

Objective 2.1.2 – Use script arguments with sys.argv

1. Demonstrate how to pass one or more arguments into a script at runtime (e.g., python script.py input.txt).
2. Access arguments in Python and apply them in tasks.
3. Analyze how argument values change program behavior (e.g., specifying input files, folder paths, or configuration options).
4. Implement a script that accepts a filename and prints its content or metadata.

Objective 2.1.3 – Explain the role of virtual environments in automation

1. Define what a Python virtual environment is and why it is used.
2. Explain how virtual environments isolate dependencies and improve script portability.
3. Demonstrate creating (python -m venv venv), activating, and deactivating a virtual environment.
4. Identify scenarios where using a virtual environment is critical (e.g., when deploying automation on different systems).

2.2 Script Configuration and Execution Basics (3)

Objective 2.2.1 – Use shebang lines for Unix/Linux/macOS automation

1. Explain the function of a shebang line in executable Python scripts.
2. Write the standard shebang #!/usr/bin/env python3 in a script.
3. Demonstrate how to make a script executable with chmod +x.
4. Verify execution of a script without explicitly calling the Python interpreter.

Objective 2.2.2 – Apply output and error redirection for Python scripts

1. Redirect standard output (stdout) from a script into a text file using >.
2. Redirect error messages (stderr) into a separate file using 2>.
3. Apply combined redirection (&> or equivalents) to capture all script output.
4. Analyze script logs to separate successful runs from error traces.

Objective 2.2.3 – Describe environment variables in automation

1. Define environment variables and explain their purpose in automation.
2. Identify common environment variables such as PATH, HOME, or USERNAME.
3. Demonstrate how to read environment variables in Python using os.environ.
4. Apply environment variables to customize script behavior (e.g., dynamic file paths or system-specific configurations).
5. Demonstrate how to create and assign values to system environment variables (e.g., setting a temporary variable in the shell or using os.environ["VAR_NAME"] = "value" in Python).

2.3 Integration with System Tools (3)

Objective 2.3.1 – Explain how automation scripts are combined with OS-level tools

1. Describe Task Scheduler (Windows) and cron (Linux/macOS) as scheduling tools.
2. Explain how Python scripts can be configured to run automatically using these tools.
3. Evaluate scenarios where OS-level scheduling is more appropriate than in-script scheduling.
4. Provide examples such as scheduling a daily cleanup or weekly backup.

Objective 2.3.2 – Use subprocess to execute external commands

1. Execute shell commands from within Python using the subprocess module.
2. Capture command output and return codes to verify execution success.
3. Redirect external command output into files or Python variables for further processing.
4. Provide examples such as running ls (Linux/macOS) or dir (Windows) and analyzing results.

Objective 2.3.3 – Identify use cases for command-line automation

1. Recognize tasks that benefit from command-line automation, such as batch file renaming, backups, and log cleanup.
2. Evaluate how command-line automation reduces manual effort in repetitive tasks.
3. Compare scenarios where Python automation is more effective than manual command entry.
4. Provide examples of combining multiple scripts into a single automated workflow.

Block 3: Logging and Monitoring Essentials

7 objectives covered by the block → 7 exam items (15% of total exam)

3.1 Understanding the Role of Logging (2)

Objective 3.1.1 – Explain why logging is essential in automation

1. Describe the role of logging for debugging, monitoring, and auditing automated tasks.
2. Explain the limitations of using print() functions compared to logging.
3. Provide examples of automation tasks where logs are essential (e.g., system monitoring, error tracking).
4. Explain why sensitive information (e.g., passwords, API keys) should never be logged.

Objective 3.1.2 – Configure simple logging in Python

1. Demonstrate how to import and use the logging module.
2. Use logging.basicConfig to configure a simple logger.
3. Generate log messages with levels such as INFO, WARNING, and ERROR.
4. Write log outputs to both console and file.

3.2 Log Levels and Formats (3)

Objective 3.2.1 – Differentiate between logging levels

1. Define standard log levels: DEBUG, INFO, WARNING, ERROR, and CRITICAL.
2. Provide examples of when to use each level (e.g., DEBUG for troubleshooting, ERROR for critical issues).
3. Explain why consistent use of log levels helps organize and analyze automation logs.

Objective 3.2.2 – Apply custom formatting in logs

1. Configure log messages to include timestamps, log levels, and message details.
2. Demonstrate structured logging with custom formats for better monitoring.
3. Compare simple vs formatted log outputs to highlight clarity improvements.

Objective 3.2.3 – Implement logging during file operations

1. Record every time a file is read or written by the script.
2. Log the number of lines processed in a file.
3. Compare logs of successful operations vs failed attempts.
4. Explain how logging improves traceability in file automation.

3.3 Monitoring Automation Tasks (2)

Objective 3.3.1 – Implement basic monitoring strategies in automation

1. Record script start and end times automatically in logs.
2. Track the number of processed files, records, or tasks.
3. Detect and record errors during execution for later review.

Objective 3.3.2 – Use logs to debug automation workflows

1. Interpret log messages to identify causes of script failures.
2. Use log analysis to differentiate between normal and abnormal system behavior.
3. Provide scenarios such as diagnosing a failed backup or handling an API timeout.

Block 4: Basic File and Data Automation

8 objectives covered by the block → 8 exam items (17.5% of total exam)

4.1 File Operations with Python (3)

Objective 4.1.1 – Perform file and folder operations

1. List and verify files or directories using os functions such as os.listdir() and os.path.exists().
2. Create and delete directories programmatically to manage file structures.
3. Automate simple housekeeping tasks such as cleaning a temporary folder.
4. Open, read, write, and append to text files in different modes (r, w, a), including handling encodings like UTF-8.

Objective 4.1.2 – Use shutil for advanced operations

1. Copy files from one location to another using shutil.copy().
2. Move and rename files with shutil.move().
3. Apply directory-level operations for organizing large groups of files.
4. Implement a basic backup script to duplicate files into a backup folder.

Objective 4.1.3 – Detect and handle file errors

1. Identify common file errors such as missing files or permission issues.
2. Handle these errors using try/except blocks in Python.
3. Record error messages in logs for troubleshooting.
4. Evaluate scenarios where error handling prevents data loss.

4.3 CSV and JSON Processing (3)

Objective 4.3.1 – Differentiate between CSV and JSON formats

1. Recognize CSV use cases such as expense trackers and contact databases.
2. Recognize JSON use cases such as API responses and configuration files.
3. Evaluate the advantages and limitations of CSV and JSON formats.
4. Evaluate when to use each format in automation tasks.

Objective 4.3.2 – Process CSV files with Python’s csv module

1. Read CSV data into Python using csv.reader() and csv.DictReader() for row-by-row access.
2. Write CSV data using csv.writer() or csv.DictWriter() for flexible output.
3. Automate simple summaries such as totals, counts, or averages.
4. Demonstrate automation by combining data from multiple CSV files.

Objective 4.3.3 – Parse and generate JSON files with the json module

1. Convert Python dictionaries or lists into JSON strings with json.dumps().
2. Save JSON objects into files with json.dump().
3. Parse JSON data into Python objects using json.load() or json.loads().
4. Apply JSON automation to store structured data for reuse.

4.4 Professional Practices in Data Automation (2)

Objective 4.4.1 – Apply safe and reliable practices in file handling

1. Detect and handle empty files gracefully.
2. Manage corrupted files without script failure.
3. Apply context managers (with open()) to ensure files are always closed safely.
4. Use logging to capture data-related errors.
5. Explain why error handling is critical for reliability in automation.

Objective 4.4.2 – Explain ethical, security, and privacy considerations

1. Avoid overwriting or deleting important files by implementing safeguards.
2. Recognize the risks of storing or exposing sensitive data (e.g., passwords, medical data, financial data).
3. Describe best practices for file naming and version control to ensure traceability.
4. Evaluate responsible use of automation in handling confidential information.
5. Explain why temporary files pose privacy risks, and apply safeguards such as access control, secure deletion, and compliance with Privacy by Design principles (e.g., ISO 27701).

Block 5: Basic Web and API Automation

8 objectives covered by the block → 8 exam items (17.5% of total exam)

5.1 Introduction to Web Automation (2)

Objective 5.1.1 – Differentiate between web scraping and APIs

1. Explain the difference between retrieving information from raw HTML vs structured data from an API.
2. Compare web scraping (e.g., extracting headlines from a news site) with API-based data access (e.g., requesting weather information).
3. Identify which approach is more efficient or reliable in different scenarios.

Objective 5.1.2 – Identify ethical and legal considerations in web scraping

1. Respect website rules defined in robots.txt.
2. Recognize the risks of overloading websites with frequent automated requests.
3. Identify common anti-scraping techniques such as captchas, IP rate limiting, and request throttling.
4. Explain why consent and responsible use are critical in automation.

5.2 Using requests for Web Content (2)

Objective 5.2.1 – Fetch web pages with requests.get()

1. Import the requests library.
2. Retrieve the HTML content of a webpage using requests.get().
3. Interpret response objects and status codes (200, 404, 500).
4. Apply basic error handling to detect failed requests.

Objective 5.2.2 – Parse JSON responses from web services

1. Recognize JSON as a standard data format for web APIs.
2. Load JSON responses into Python dictionaries using response.json().
3. Process and extract values from JSON objects.
4. Demonstrate saving API responses into a file (CSV or JSON) for later use.

5.3 Parsing HTML with BeautifulSoup (2)

Objective 5.3.1 – Extract information from simple HTML pages

1. Use BeautifulSoup to parse HTML documents.
2. Find and extract specific elements such as titles, links, or paragraphs.
3. Loop through multiple elements to build lists of results (e.g., all headlines).

Objective 5.3.2 – Apply logging in web automation

1. Record whether a request succeeded or failed.
2. Log the number of elements scraped from a webpage.
3. Analyze logs to detect unusual behavior (e.g., missing elements, errors).

5.4 Working with APIs (2)

Objective 5.4.1 – Explain REST API basics

1. Define common HTTP methods: GET, POST.
2. Identify JSON as the most common format for REST API responses.
3. Recognize the difference between requesting data and sending data.
4. Identify common restrictions such as API keys, authentication, and request rate limits.
5. Differentiate between public APIs (freely accessible) and private APIs (restricted access).

Objective 5.4.2 – Fetch and interpret data from a simple API

1. Perform a request to a simple API (e.g., weather, exchange rates, quotes).
2. Parse the returned JSON data into Python structures.
3. Store results in local files such as CSV or JSON for reporting.

Block 6: Scheduling, Notifications, and Reporting

8 objectives covered by the block → 8 exam items (17.5% of total exam)

6.1 Scheduling Basics (2)

Objective 6.1.1 – Explain the need for scheduling in automation

1. Identify common repetitive tasks suitable for scheduling (e.g., backups, report generation, log cleanup).
2. Distinguish between manual script execution and automated scheduled runs.
3. Explain how scheduling improves reliability, consistency, and efficiency.

Objective 6.1.2 – Schedule scripts using Python’s schedule module

1. Install and import the schedule library.
2. Demonstrate how to run a simple job at fixed intervals (e.g., every 10 minutes).
3. Use time-based logic to execute tasks daily or weekly.
4. Demonstrate combining schedule with time.sleep() for continuous execution.

6.2 System-Level Scheduling (2)

Objective 6.2.1 – Describe cron jobs and Windows Task Scheduler

1. Define cron jobs in Linux/macOS and Task Scheduler in Windows.
2. Explain key differences between scheduling in Unix-like vs Windows environments.
3. Explain simple examples such as scheduling a Python script to run once per day.

Objective 6.2.2 – Recognize advantages and limitations of system scheduling

1. Recognize strengths: flexibility, reliability, running scripts without user input.
2. Identify risks such as misconfigured jobs, overlapping executions, and missed triggers.
3. Evaluate when to use system-level scheduling instead of Python-based scheduling.

6.3 Notifications and Alerts (2)

Objective 6.3.1 – Send email notifications with smtplib

1. Configure a simple SMTP connection in Python.
2. Automate sending plain-text emails (e.g., task completion or error alerts).
3. Log outgoing messages for record-keeping.
4. Recognize the need for secure handling of email credentials.

Objective 6.3.2 – Describe desktop notification options

1. Identify cross-platform notification tools such as plyer.
2. Demonstrate creating a simple desktop notification.
3. Provide use cases such as reminders, process completions, or status updates.
4. Evaluate when desktop notifications are useful vs when email alerts are more appropriate.

6.4 Reporting in Automation (2)

Objective 6.4.1 – Generate simple reports from automation tasks

1. Summarize task results in plain text files for record-keeping.
2. Generate basic HTML reports with headings, tables, or lists for easier readability.
3. Create simple daily or weekly reports with timestamps to track progress over time.
4. Automate the saving of generated reports into designated directories for organization and retrieval.

Objective 6.4.2 – Apply logging to scheduled and reporting workflows

1. Record report generation steps in log files.
2. Log both successful and failed reporting tasks.
3. Analyze logs to identify scheduling or reporting errors.
4. Explain how logging improves auditability and troubleshooting in automated workflows.