Module 1: Foundations of Data Analysis

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In this post, I provide accurate answers and detailed explanations for Module 1: Foundations of Data Analysis of Course 8: Data Analytics and Databases on AWS  Software Development – AWS Cloud Technology Consultant Professional Certificate

Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.

ETL Process

Practice Assignment

1. Suppose that you are working as a cloud consultant for a data analytics company. The company assists a large number of customers and takes their feedback seriously.

To continuously improve business operations and maintain customer trust, the company provides a feedback form on their website. However, the collected data seems to include many discrepancies. The company leadership considers implementing a different process to collect and analyze the information. In a meeting, you recommend using an extract, transform, and load (ETL) solution for the feedback form.

Before you start to design the solution, use the following table to identify the elements that belong to each ETL process.

  • Extract:
    Capture data from the form, store the data in a staging area, set the data validation rules
    (Explanation: Extraction is about collecting data from a source and staging it for further processing.)
  • Transform:
    Cleanse the data, revise the formatting of the data, remove personally identifiable information (PII)
    (Explanation: Transformation involves cleaning and preparing data to make it usable.)
  • Load:
    Update the target database, perform the final validation, schedule regular backups
    (Explanation: Loading refers to moving the processed data into its final destination.)

Module 1 Quiz

Graded Assignment

2. For data analysis, it is common to collect structured, semistructured, and unstructured data. What is the primary characteristic of structured data?

  • It is unorganized and is stored in random manner.
  • It contains information such as images and videos.
  • It follows a specific schema or format. ✅

  • It has a flexible schema that varies within the same dataset.

Explanation:
Structured data is highly organized and stored in a predefined format such as rows and columns in a relational database. It adheres to a strict schema, making it easily searchable and analyzable using standard tools like SQL. Examples include spreadsheets and relational database tables.

3. A cloud consultant is working for a company that uses cloud services for different aspects of their business. The company has deployed a diverse range of resources, including virtual machines, storage buckets, and databases. However, none of these resources have tags, and the data analytics team would like to track and optimize different components of the environment. The consultant decides to recommend the use of tag metadata to help the data analytics team perform their analysis. Which option BEST describes how the data analytics team should implement resource metadata?

  • Attach specific labels to resources. ✅

  • Encrypt all data stored in the cloud.
  • Implement advanced learning algorithms.
  • Upgrade hardware components.

Explanation:
Labels or tags are a type of metadata used to organize cloud resources. By tagging virtual machines, storage, and databases with metadata (like project name, department, or environment), analytics teams can filter, group, and analyze resource usage more effectively.

4. True or False:

A large organization is looking for a new approach to collect data from their digital sales campaign. They are considering two different approaches to process and analyze data: extract, transform, and load (ETL) and extract, load, and transform (ELT). According to the ETL approach, data is first loaded into a data lake; however, in the ELT approach, data is transformed before being loaded.

  • True
  • False ✅

Explanation:
The statement mixes up ETL and ELT processes.

  • ETL (Extract, Transform, Load): Data is first extracted, then transformed, and finally loaded into the destination (e.g., data warehouse).
  • ELT (Extract, Load, Transform): Data is extracted, then loaded directly into the destination (e.g., data lake or data warehouse), and then transformed there.

Related contents:

Module 2: ETL Pipeline and Database Foundations
Module 3: AWS Services for ETL

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