02 Extracting, Transforming, and Loading Data

1. A financial organization is working on extracting customer transaction data for analysis. The data exists in separate repositories: one for transaction details and another for customer demographics. What is the best approach to consolidate the data?

A: Use a direct SQL query on both repositories without joining.
B: Combine the data into one dataset using an outer join.
C: Perform an inner join between the two datasets on customer ID.
D: Manually match the datasets using Excel.

2. During the data validation phase, a dataset contains corrupted data where one of the values for the "Color" feature appears as '��'. How should the team proceed to handle this irregularity?

A: Estimate the missing value based on similar records.
B: Drop the entire record to avoid complications.
C: Replace the value with a random color.
D: Ignore the error and move on with the analysis.

3. A company is using Word2vec to process a large corpus of text. However, many of the words in the corpus are rare and don't have sufficient representation. Which technique could help generate effective embeddings for these rare words?

A: Bag of words
B: GloVe
C: TF-IDF
D: fastText

4. You have to transform a dataset for a marketing campaign analysis. The campaign data spans multiple regions with different currency formats. What transformation task should be performed to streamline the data?

A: Convert all currencies into strings.
B: Normalize all currencies to a standard format, such as USD, and include a currency code.
C: Drop all non-USD data from the dataset.
D: Convert numeric values directly to integers to simplify calculations.

5. In an ETL pipeline, data is being extracted from an operational database and loaded into a data warehouse. However, the team notices that duplicate records are appearing in the warehouse. What is the most likely cause, and how should it be addressed?

A: The records are corrupted, and the database should be dropped.
B: The same records are being extracted multiple times, so deduplication should be performed.
C: The warehouse is incorrectly configured to store redundant data.
D: The records are expected to be duplicated and should be ignored.

6. An organization plans to extract data from a legacy system using SQL queries. The data is large and requires optimization to avoid performance issues. Which guideline should they follow to efficiently extract the data?

A: Use SELECT * to extract all columns from all tables.
B: Limit the number of columns and use indices for efficient querying.
C: Extract all data without filtering for analysis later.
D: Perform manual joins on the extracted data instead of using SQL queries.

7. A team is analyzing data that contains both structured and unstructured formats. How should they approach handling these data types for further processing?

A: Convert all data to unstructured format for easier analysis.
B: Convert unstructured data into structured format using appropriate parsing techniques.
C: Discard unstructured data and focus only on the structured data.
D: Analyze each data type separately without any conversions.

8. A sales team needs to create a data mart to provide quick access to aggregated sales performance data. Which of the following types of data repositories is most suitable for this purpose?

A: Data Lake
B: Operational Data Store
C: Data Mart
D: NoSQL Database

9. Your project involves integrating data from a CRM system and a marketing platform. While preparing the data for analysis, you notice that several rows have missing customer email addresses. What is the best way to handle these missing values?

A: Drop all records with missing values.
B: Replace missing emails with dummy data.
C: Estimate the missing emails using predictive modeling.
D: Leave the missing values as they are and proceed with the analysis.

10. An e-commerce company is processing a large volume of sales data in near real-time for fraud detection. What is the most appropriate big data characteristic that they should focus on to manage this task effectively?

A: Volume
B: Velocity
C: Variety
D: Veracity

11. A machine learning team is training a model using a dataset of customer demographics and purchase history. They need to ensure that the training process isn't biased by certain features. How should they approach the data cleaning process to mitigate bias

A: Focus on removing duplicate records.
B: Ensure that governance practices include stakeholder review of the cleaning process to mitigate bias.
C: Only clean the most important features, leaving the rest untouched.
D: Clean the data randomly without stakeholder input to avoid interference.

12. A data science team is working on extracting data from an internal customer support system and loading it into a cloud-based data warehouse. Which of the following factors should they prioritize when configuring the ETL pipeline?

A: High memory consumption
B: Security protections and data integrity
C: Random data transformations
D: Skipping the transformation step entirely

13. While processing data extracted from multiple sources, a practitioner encounters date values in different formats (e.g., '2024-03-01' and '01/03/2024'). What should the practitioner do to ensure consistency?

A: Convert all date formats to a string.
B: Normalize the dates into a consistent datetime format.
C: Remove all date fields to avoid confusion.
D: Leave the dates in their original format and analyze them separately.

14. You have extracted a large customer transaction dataset that includes several categorical features, such as customer region and payment method. To enhance the performance of your machine learning model, how should you transform these categorical features

A: Convert the categories to numeric values using one-hot encoding.
B: Keep them as strings for better interpretability.
C: Drop all categorical features as they may complicate the model.
D: Combine them into a single text feature for easier processing.

15. A data scientist needs to ensure that sensitive personally identifiable information (PII) in a dataset is protected before sharing the dataset with an external team. Which of the following techniques is most appropriate for this task?

A: Anonymize the data by scrubbing all PII.
B: Leave the PII intact, as it may be useful for analysis.
C: Encrypt the PII before sharing the dataset.
D: Use synthetic data to replace the original PII.

16. A retail company needs to load data into a SQL database for a sales dashboard. However, they are experiencing memory capacity issues when loading large datasets. What should they consider to overcome this issue?

A: Increase storage space on the database server.
B: Load the data in smaller chunks instead of all at once.
C: Use a NoSQL database instead.
D: Ignore memory constraints and proceed with the loading.

17. A team is preparing data to train a model that predicts customer churn. During the ETL process, they encounter incomplete rows of customer interaction data. What is the best course of action?

A: Remove all incomplete rows to ensure data quality.
B: Impute missing values using the median or mean.
C: Use a simple string placeholder for missing values.
D: Ignore the missing data and proceed with the model training.

18. You have a dataset containing product reviews, but the text data is unstructured. What transformation technique can you use to convert the text into a format suitable for machine learning?

A: One-hot encoding
B: Word embeddings such as Word2vec or GloVe
C: Parsing the text into numeric values
D: Convert all text to uppercase

19. A data engineer is tasked with transforming JSON data from a public API into a format that can be used for SQL-based analysis. What is the best approach?

A: Convert the JSON data into a CSV format.
B: Keep the data in its original JSON format and query it directly.
C: Manually rewrite the data into SQL syntax.
D: Load the JSON data into a binary format.

20. Your data science team is working on transforming and loading data for a fraud detection system. Which phase of the ETL process is most critical for ensuring that only clean and relevant data is used for modeling?

A: Extract
B: Transform
C: Load
D: Analyze

21. A data analyst is working with a dataset that includes a column for transaction amounts. However, some of the amounts are formatted inconsistently (e.g., some values are in dollars, others in euros). What should the analyst do to prepare the data for ana

A: Drop all non-dollar transactions from the dataset.
B: Convert all transactions to a common currency and standardize the format.
C: Convert the currency values to strings to avoid data loss.
D: Leave the currency formats as is and analyze them separately.

22. While loading a large dataset into a data warehouse, the ETL process fails due to a schema mismatch between the source and target databases. What is the most likely cause, and how should it be resolved?

A: The source data contains missing values, so it should be removed.
B: The target database schema is outdated, and the ETL script needs to be updated to match.
C: The source data contains too many columns, so they should be truncated.
D: The ETL process should be retried without making changes.

23. A data engineer needs to merge two datasets, one containing customer demographics and the other containing their purchase history. However, some customers exist in one dataset but not the other. What type of join should be used to ensure no records are l

A: Inner join
B: Left outer join
C: Right outer join
D: Full outer join

24. In a large ETL pipeline, a data engineer notices that some data from external APIs is frequently outdated. What should the team do to ensure the data remains current?

A: Increase the frequency of data extraction to account for API delays.
B: Store the outdated data and update it manually.
C: Ignore the outdated data and continue with the ETL process.
D: Remove the data from the analysis entirely.

25. A marketing team wants to segment customers based on purchase behavior for a targeted email campaign. However, the customer data is missing many transaction details. What should the team do before proceeding with the segmentation?

A: Impute the missing transaction details using predictive models.
B: Remove all customers with missing transaction details from the analysis.
C: Use only the available transaction data without making any modifications.
D: Estimate transaction behavior based on demographics alone.

26. A retailer is extracting data from their point-of-sale (POS) system into a CSV file for further analysis. However, they realize that the CSV file includes many duplicated records. What should be the next step in the ETL process?

A: Load the data into the analysis tool, then filter duplicates later.
B: Deduplicate the CSV file during the transformation phase.
C: Ignore the duplicates and proceed with the ETL process.
D: Convert the CSV file into a binary format to handle duplicates.

27. Your team is using SQL to aggregate data on employee performance. You want to calculate the average years of service in each department. Which SQL query would be appropriate for this task?

A: SELECT * FROM employees WHERE department = 'HR';
B: SELECT department, AVG(yrs_service) AS avg_years FROM employees GROUP BY department;
C: SELECT department, SUM(yrs_service) FROM employees;
D: SELECT department, COUNT(yrs_service) FROM employees;

28. You are tasked with managing a data lake for a manufacturing company. The data consists of structured sensor readings and unstructured maintenance reports. How should you organize the data for efficient querying and analysis?

A: Store the sensor data in a traditional SQL database and the maintenance reports in a NoSQL database.
B: Convert all data into structured formats and store them in a data mart.
C: Store both structured and unstructured data in their original formats in the data lake.
D: Manually restructure the unstructured data to fit the structured format.

29. A data team is tasked with consolidating data from multiple sources, including cloud-based storage, internal databases, and third-party APIs. Which factor should they prioritize to ensure smooth integration?

A: Data availability and access permissions across all sources
B: Ensuring all data sources use the same database technology
C: Transforming all data into text files for easier handling
D: Ignoring access permissions to speed up the process

30. A data scientist is working on a machine learning model for predictive maintenance in a manufacturing plant. The model uses both real-time sensor data and historical failure data. How should the data scientist handle the velocity of the real-time data?

A: Batch process the sensor data periodically.
B: Stream the data continuously into a real-time analytics platform.
C: Ignore the real-time data and focus on historical data.
D: Use random subsets of the real-time data for analysis.

31. While analyzing transaction data from an e-commerce site, a data analyst notices that several customers have unusually high purchase amounts. Upon further investigation, they discover that these records are duplicates. What is the best way to handle thes

A: Remove all high-value transactions.
B: Manually review each record before deciding to remove duplicates.
C: Automatically remove all duplicate records during the ETL process.
D: Ignore the duplicates since they may be useful.

32. Your data team is tasked with loading transactional data into a NoSQL database for real-time analytics. What characteristic of NoSQL databases makes them suitable for this task?

A: They are optimized for complex relational queries.
B: They handle large volumes of unstructured data efficiently.
C: They require a fixed schema, which simplifies data loading.
D: They support strong data integrity enforcement.

33. A company is extracting sales data from multiple retail outlets across the country. They need to ensure that this data is stored securely in a centralized cloud-based data warehouse. What is the most important consideration when configuring the data load

A: Ensure data encryption during transmission and at rest.
B: Focus on maximizing the speed of data uploads.
C: Disable security settings to simplify access.
D: Store all data in a public-access cloud environment.

34. A machine learning model being trained on a dataset with missing values produces poor predictions. Which imputation technique should the data scientist consider to improve the model’s accuracy?

A: Remove all records with missing values.
B: Replace missing values with the mean or median of the feature.
C: Ignore the missing values and proceed with the model training.
D: Replace missing values with random numbers.

35. An organization is preparing to load data into a relational database for reporting purposes. However, they need to ensure that the database can handle a high volume of queries without slowing down. What strategy should they use?

A: Optimize the database by creating appropriate indices for frequently queried columns.
B: Remove all indices to improve data load speeds.
C: Store the data in text files for faster querying.
D: Use a NoSQL database instead of a relational one.

36. While loading product data from an external API, your team discovers that the API frequently returns inconsistent data formats, such as different units for weight (e.g., pounds, kilograms). What is the best approach to standardize the data?

A: Drop all records with inconsistent units.
B: Convert all units to a standard format, such as metric, during the transformation phase.
C: Leave the data as is and handle the inconsistencies during analysis.
D: Create separate datasets for each unit type.

37. An AI team is building a recommendation system for a video streaming platform. To train their model, they need to extract user interaction data from various databases. What key factor should they prioritize during the extraction process?

A: Ensure the data is anonymized to protect user privacy.
B: Focus on extracting only the most recent interactions.
C: Extract all available data without filtering for relevance.
D: Manually query each database to gather the data.

38. You are tasked with preparing a dataset for machine learning training, but the dataset contains several outliers that could affect the model’s performance. What is the best way to handle these outliers?

A: Remove all outliers from the dataset.
B: Leave the outliers as they are and proceed with training.
C: Investigate the cause of the outliers and decide whether to remove them or adjust the data.
D: Replace outliers with average values from the dataset.

39. A company is consolidating historical sales data from several years, stored in multiple formats. Which transformation should they perform first to make this data ready for analysis?

A: Convert all data to a common format, such as CSV or SQL.
B: Drop older data to reduce the dataset size.
C: Aggregate data by year before transforming.
D: Ignore format inconsistencies and analyze directly.

40. An e-commerce company plans to use historical purchase data for customer segmentation. However, the data contains multiple entries for the same customer with different purchase details. What is the best approach to prepare this data for segmentation?

A: Merge entries for each customer to consolidate their purchase history.
B: Treat each entry as a unique customer.
C: Discard all customers with multiple entries.
D: Only use the latest entry for each customer.

41. A company is developing a predictive model for product demand but is concerned about data quality in their training dataset. What should they prioritize to ensure the model’s accuracy?

A: Validate the dataset for completeness and consistency before training.
B: Train the model without any data quality checks.
C: Remove all records with missing or incomplete data.
D: Use synthetic data to replace missing values.

02 Extracting, Transforming, and Loading Data - Questions Quiz