DA0-001 Comparing and Contrasting Data Concepts Practice Question
This DA0-001 practice question tests your understanding of comparing and contrasting data concepts. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A financial services company is migrating its customer data from a legacy on-premises relational database to a cloud-based data warehouse. The legacy database uses a denormalized schema with a single table 'customer_master' that contains all customer attributes, including repeated groups for multiple accounts per customer (account1_type, account1_balance, account2_type, account2_balance, etc.). The data warehouse team wants to implement a normalized star schema with separate dimension and fact tables. During the ETL process, the team encounters an error: 'Data truncation: string data right truncation' when loading account_type values into the dim_account table. The account_type column in dim_account is defined as VARCHAR(10), but the source data contains account types like 'SavingsPlus' (11 characters) and 'CheckingPremium' (15 characters). The team must resolve this issue without losing data. Which course of action should the team take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the VARCHAR length of dim_account.account_type to accommodate the longest account type.
Option D is correct because increasing the VARCHAR length of dim_account.account_type to accommodate the longest account type (e.g., VARCHAR(15) for 'CheckingPremium') resolves the data truncation error without data loss. This aligns with the star schema design principle of preserving source data integrity while ensuring the column definition matches the actual data length. The team must avoid truncation or NULL insertion to maintain accurate dimensional attributes for analytics.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Truncate the account_type values to 10 characters during ETL.
Why it's wrong here
Truncation causes data loss.
✗
Change the data type of dim_account.account_type to TEXT.
Why it's wrong here
TEXT is valid but not optimal; better to increase VARCHAR length.
✗
Ignore the error and continue loading with NULL values for truncated rows.
Why it's wrong here
Ignoring errors leads to incomplete data.
✓
Increase the VARCHAR length of dim_account.account_type to accommodate the longest account type.
Why this is correct
This resolves truncation without data loss.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may choose truncation (Option A) or NULL insertion (Option C) as quick fixes, overlooking the requirement to preserve data integrity, or mistakenly think TEXT (Option B) is a safe catch-all without considering performance implications in a data warehouse context.
Detailed technical explanation
How to think about this question
In a cloud data warehouse like Snowflake or Amazon Redshift, VARCHAR columns are stored as variable-length strings with a maximum specified length; exceeding that length triggers a truncation error. The star schema's dimension tables should use appropriate VARCHAR lengths to match source data, as TEXT types are often stored as LOBs (Large Objects) with different storage and performance characteristics. Real-world ETL pipelines frequently encounter this issue when source schemas evolve, requiring proactive schema validation and ALTER TABLE statements to adjust column definitions.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Comparing and Contrasting Data Concepts — This question tests Comparing and Contrasting Data Concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the VARCHAR length of dim_account.account_type to accommodate the longest account type. — Option D is correct because increasing the VARCHAR length of dim_account.account_type to accommodate the longest account type (e.g., VARCHAR(15) for 'CheckingPremium') resolves the data truncation error without data loss. This aligns with the star schema design principle of preserving source data integrity while ensuring the column definition matches the actual data length. The team must avoid truncation or NULL insertion to maintain accurate dimensional attributes for analytics.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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