A data analyst has two DataFrames: `sales_2023` with columns `order_id`, `product_id`, `amount`, and `sales_2024` with the same columns. They need to create a single DataFrame containing all orders from both years for annual reporting. Which operation should they use?

pd.merge(sales_2023, sales_2024, on='order_id')

sales_2023.join(sales_2024, how='outer')

sales_2023.append(sales_2024, ignore_index=True)

pd.concat([sales_2023, sales_2024], ignore_index=True)

During data integration, an analyst notices that the `customer_id` column in one source has missing values for some rows. After merging, those customers are missing from the final dataset. What is the best approach to handle this?

Drop all rows with missing customer IDs

Use an outer merge to keep all rows

Investigate the source system to determine if the missing IDs can be recovered

Fill missing IDs with a placeholder value like -1

An analyst is merging customer data from two systems. In System A, `customer_id` is an integer. In System B, `customer_id` is a string with leading zeros (e.g., '0123'). After merging, they notice that some customers appear twice. What is the most likely cause and the best solution?

Cause: data type mismatch; Solution: convert both columns to string and strip leading zeros before merging

Cause: schema mismatch in column names; Solution: rename columns before merging

Cause: duplicate records in System B; Solution: drop duplicates before merging

Cause: missing values in System A; Solution: fill missing IDs with a placeholder

Python InstitutePCAD - Certified Associate in Data Analytics with Python

Domain 1Objective 1

Data Collection, Integration, and Storage PCAD-31 Practice Questions (Page 1)

Part of the Data Acquisition and Pre-Processing domain, which accounts for 29.2% of the PCAD-31 exam. Python Institute does not publish an official question count, but from its 60-minute exam (~25–40 total, ~7–7 in this domain), expect 2–2 from this objective — we provide 25 practice questions to prepare you well beyond it. (estimate)

25questions here

5free pages

6concepts

29.2%of the exam

Questions 1–5

1foundation · easy
When integrating data from two sources, one source stores dates as strings in 'YYYY-MM-DD' format and the other as 'DD/MM/YYYY'. What type of data integration challenge does this represent?
Select an answer first
2application · medium
A data analyst has two DataFrames: `sales_2023` with columns `order_id`, `product_id`, `amount`, and `sales_2024` with the same columns. They need to create a single DataFrame containing all orders from both years for annual reporting. Which operation should they use?
Select an answer first
3application · medium
During data integration, an analyst notices that the `customer_id` column in one source has missing values for some rows. After merging, those customers are missing from the final dataset. What is the best approach to handle this?
Select an answer first
4foundation · easy
A company needs to store highly structured data for complex analytical queries that involve aggregations across millions of records. Which storage solution is most appropriate?
Select an answer first
5expert · hard
An analyst is merging customer data from two systems. In System A, `customer_id` is an integer. In System B, `customer_id` is a string with leading zeros (e.g., '0123'). After merging, they notice that some customers appear twice. What is the most likely cause and the best solution?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to PCAD-31

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by Python Institute. “PCAD-31” is a trademark of its owner, used for identification only.

Data Collection, Integration, and Storage PCAD-31 Practice Questions (Page 1)

Questions 1–5

When integrating data from two sources, one source stores dates as strings in 'YYYY-MM-DD' format and the other as 'DD/MM/YYYY'. What type of data integration challenge does this represent?

A data analyst has two DataFrames: `sales_2023` with columns `order_id`, `product_id`, `amount`, and `sales_2024` with the same columns. They need to create a single DataFrame containing all orders from both years for annual reporting. Which operation should they use?

During data integration, an analyst notices that the `customer_id` column in one source has missing values for some rows. After merging, those customers are missing from the final dataset. What is the best approach to handle this?

A company needs to store highly structured data for complex analytical queries that involve aggregations across millions of records. Which storage solution is most appropriate?

An analyst is merging customer data from two systems. In System A, `customer_id` is an integer. In System B, `customer_id` is a string with leading zeros (e.g., '0123'). After merging, they notice that some customers appear twice. What is the most likely cause and the best solution?

Keep studying — Data Acquisition and Pre-Processing