✅ Data Quality Vocabulary
0 / 34 completed
1 / 34
A data quality dashboard shows a 2.3% null rate on a required field.
Which data quality dimension does this violate?
Data quality dimensions (DAMA framework):
| Dimension | Question it answers | Example violation |
|---|---|---|
| Completeness | Are all required values present? | 2.3% null rate on required field |
| Accuracy | Does data match reality? | Wrong address on customer record |
| Timeliness | Is data fresh enough for use? | Report uses yesterday's data |
| Consistency | Same values across systems? | CRM and warehouse disagree on revenue |
Key vocabulary: null rate, completeness check, required vs. optional fields, data quality threshold.
2 / 34
A pipeline has a Great Expectations suite checking for data validity.
What does "data validity" mean as a quality dimension?
Validity = data meets defined business rules and constraints:
| Validity check type | Example rule |
|---|---|
| Range validation | age BETWEEN 0 AND 150 |
| Format check | Date must match YYYY-MM-DD |
| Enum constraint | status IN ('active', 'inactive', 'pending') |
| Regex pattern | Email must match ^[\w.]+@[\w.]+$ |
Tools: Great Expectations expectations, dbt tests (accepted_values, not_null, relationships). Key vocabulary: constraint check, range validation, regex pattern check, referential integrity check.
3 / 34
A data monitoring alert fires: "Row count dropped from 1.2M to 890K yesterday."
In data monitoring, what does a sudden drop in row count typically signal?
Row count anomaly detection — a fundamental data monitoring check:
| Anomaly type | Likely cause |
|---|---|
| Sudden drop in row count | Pipeline failure, source API issue, schema change filtering rows |
| Sudden spike in row count | Duplicate ingestion, re-processing of historical data |
| Gradual decline | Source system usage decline or data sourcing issue |
Key vocabulary: data volume anomaly, ingestion failure, row count SLO, anomaly detection threshold, data observability (Monte Carlo, Anomalo).
4 / 34
An engineer says: "The data contract between the producer team and the consumer team was violated."
What is a data contract?
Data contracts = producer-consumer agreement on data structure and behaviour:
| Contract element | What it defines |
|---|---|
| Schema | Field names, types, nullability |
| Freshness SLO | How recent the data must be |
| Volume expectations | Expected row count ranges |
Prevents: "the upstream team changed a field name and broke my pipeline". Key vocabulary: schema contract, breaking schema change, producer obligations, consumer expectations, contract versioning.
5 / 34
A data reconciliation job reports: "0.4% discrepancy found between the source system and the warehouse."
What is data reconciliation?
Reconciliation = source vs. target comparison to verify complete, accurate ingestion:
| Reconciliation check | What it verifies |
|---|---|
| Row count match | Same number of records source vs. warehouse |
| Aggregate match | sum(revenue) matches between systems |
| No dropped rows | No records lost or duplicated in transit |
Discrepancy investigation: trace which pipeline stage introduced the difference. Key vocabulary: source-to-target reconciliation, reconciliation threshold (acceptable error %), discrepancy investigation, audit count check.
6 / 34
PR Description
Subject: Fix - Incorrect Data Type in User Profile Service
This PR addresses a data quality issue identified during code review. The `user_age` field in the User Profile service was incorrectly defined as a string instead of an integer. This resulted in validation errors and inconsistent data.
Which of the following best describes the problem highlighted in this PR description, concerning data quality?
Option A: The PR demonstrates effective unit testing practices.
Option B: The PR addresses a data type mismatch, representing a fundamental data quality issue.
Option C: The PR focuses solely on improving the user interface for age input.
Option D: The PR highlights the importance of code documentation.This question assesses understanding of how data quality issues manifest in practical scenarios like PR descriptions. The correct answer (data type mismatch) directly addresses the core problem: incorrect data types can lead to validation failures and inconsistencies – a key aspect of poor data quality. Options A, C, and D are tangential concerns related to development practices but don't represent the *quality* issue being addressed in the PR description; misusing data types is a foundational error.
7 / 34
Slack Message
@john.doe: "Just ran the daily data quality check on the customer orders table. Looks like we're seeing a significant number of records with missing postal codes – over 15%! This is impacting our shipping efficiency."
Which of the following best describes the issue @john.doe is reporting, in terms of data quality?
Option A: The problem relates to the speed of the daily report generation process.
Option B: The missing postal codes represent a data completeness dimension, specifically indicating insufficient data coverage.
Option C: The issue highlights a need for improved customer segmentation strategies.
Option D: The problem is solely a matter of database indexing optimization.This situation exemplifies how data quality issues manifest in real-world scenarios. The core concern is the 'missing postal codes' – this falls under the data completeness dimension, meaning there's a lack of required information within the dataset. Incorrectly identifying it as report generation speed or customer segmentation misses the fundamental problem: the data itself isn't meeting expected standards. The incorrect options focus on peripheral issues; the primary concern is the absence of data.
8 / 34
You're reviewing a new API response from the e-commerce platform. The response contains an error message: `Invalid data format: Expected integer, received string for 'order_total'.` Considering this scenario, which data quality dimension is primarily being violated?
This error message clearly indicates a problem with 'conformance,' where the API response isn't adhering to its documented schema. The expected data type (integer) was not received, representing a deviation from the agreed-upon format. Failing to meet defined specifications is a core aspect of data conformity and directly impacts the reliability of the data being exchanged. Incorrectly specifying data types or formats often leads to downstream processing errors.
9 / 34
PR Description
Subject: Fix - Incorrect Data Type in User Profile Service
This PR addresses a data quality issue identified during code review. The `user_age` field in the User Profile service was incorrectly defined as a string instead of an integer. This resulted in validation errors and inconsistent data.
Which of the following best describes the problem highlighted in this PR description, concerning data quality?
Option A: The PR demonstrates effective unit testing practices.
Option B: The PR addresses a data type mismatch, representing a fundamental data quality issue.
Option C: The PR focuses solely on improving the user interface for age input.
Option D: The PR highlights the importance of code documentation.This question assesses understanding of how data quality issues manifest in practical scenarios like PR descriptions. The correct answer (data type mismatch) directly addresses the core problem: incorrect data types can lead to validation failures and inconsistencies – a key aspect of poor data quality. Options A, C, and D are tangential concerns related to development practices but don't represent the *quality* issue being addressed in the PR description; misusing data types is a foundational error.
10 / 34
Slack Message
@john.doe: "Just ran the daily data quality check on the customer orders table. Looks like we're seeing a significant number of records with missing postal codes – over 15%! This is impacting our shipping efficiency."
Which of the following best describes the issue @john.doe is reporting, in terms of data quality?
Option A: The problem relates to the speed of the daily report generation process.
Option B: The missing postal codes represent a data completeness dimension, specifically indicating insufficient data coverage.
Option C: The issue highlights a need for improved customer segmentation strategies.
Option D: The problem is solely a matter of database indexing optimization.This situation exemplifies how data quality issues manifest in real-world scenarios. The core concern is the 'missing postal codes' – this falls under the data completeness dimension, meaning there's a lack of required information within the dataset. Incorrectly identifying it as report generation speed or customer segmentation misses the fundamental problem: the data itself isn't meeting expected standards. The incorrect options focus on peripheral issues; the primary concern is the absence of data.
11 / 34
You're reviewing a new API response from the e-commerce platform. The response contains an error message: `Invalid data format: Expected integer, received string for 'order_total'.` Considering this scenario, which data quality dimension is primarily being violated?
This error message clearly indicates a problem with 'conformance,' where the API response isn't adhering to its documented schema. The expected data type (integer) was not received, representing a deviation from the agreed-upon format. Failing to meet defined specifications is a core aspect of data conformity and directly impacts the reliability of the data being exchanged. Incorrectly specifying data types or formats often leads to downstream processing errors.
12 / 34
PR Description
Subject: Fix - Incorrect Data Type in User Profile Service
This PR addresses a data quality issue identified during code review. The `user_age` field in the User Profile service was incorrectly defined as a string instead of an integer. This resulted in validation errors and inconsistent data.
Which of the following best describes the problem highlighted in this PR description, concerning data quality?
Option A: The PR demonstrates effective unit testing practices.
Option B: The PR addresses a data type mismatch, representing a fundamental data quality issue.
Option C: The PR focuses solely on improving the user interface for age input.
Option D: The PR highlights the importance of code documentation.This question assesses understanding of how data quality issues manifest in practical scenarios like PR descriptions. The correct answer (data type mismatch) directly addresses the core problem: incorrect data types can lead to validation failures and inconsistencies – a key aspect of poor data quality. Options A, C, and D are tangential concerns related to development practices but don't represent the *quality* issue being addressed in the PR description; misusing data types is a foundational error.
13 / 34
Slack Message
@john.doe: "Just ran the daily data quality check on the customer orders table. Looks like we're seeing a significant number of records with missing postal codes – over 15%! This is impacting our shipping efficiency."
Which of the following best describes the issue @john.doe is reporting, in terms of data quality?
Option A: The problem relates to the speed of the daily report generation process.
Option B: The missing postal codes represent a data completeness dimension, specifically indicating insufficient data coverage.
Option C: The issue highlights a need for improved customer segmentation strategies.
Option D: The problem is solely a matter of database indexing optimization.This situation exemplifies how data quality issues manifest in real-world scenarios. The core concern is the 'missing postal codes' – this falls under the data completeness dimension, meaning there's a lack of required information within the dataset. Incorrectly identifying it as report generation speed or customer segmentation misses the fundamental problem: the data itself isn't meeting expected standards. The incorrect options focus on peripheral issues; the primary concern is the absence of data.
14 / 34
You're reviewing a new API response from the e-commerce platform. The response contains an error message: `Invalid data format: Expected integer, received string for 'order_total'.` Considering this scenario, which data quality dimension is primarily being violated?
This error message clearly indicates a problem with 'conformance,' where the API response isn't adhering to its documented schema. The expected data type (integer) was not received, representing a deviation from the agreed-upon format. Failing to meet defined specifications is a core aspect of data conformity and directly impacts the reliability of the data being exchanged. Incorrectly specifying data types or formats often leads to downstream processing errors.
15 / 34
PR Description
Subject: Fix - Incorrect Data Type in User Profile Service
This PR addresses a data quality issue identified during code review. The `user_age` field in the User Profile service was incorrectly defined as a string instead of an integer. This resulted in validation errors and inconsistent data.
Which of the following best describes the problem highlighted in this PR description, concerning data quality?
Option A: The PR demonstrates effective unit testing practices.
Option B: The PR addresses a data type mismatch, representing a fundamental data quality issue.
Option C: The PR focuses solely on improving the user interface for age input.
Option D: The PR highlights the importance of code documentation.This question assesses understanding of how data quality issues manifest in practical scenarios like PR descriptions. The correct answer (data type mismatch) directly addresses the core problem: incorrect data types can lead to validation failures and inconsistencies – a key aspect of poor data quality. Options A, C, and D are tangential concerns related to development practices but don't represent the *quality* issue being addressed in the PR description; misusing data types is a foundational error.
16 / 34
Slack Message
@john.doe: "Just ran the daily data quality check on the customer orders table. Looks like we're seeing a significant number of records with missing postal codes – over 15%! This is impacting our shipping efficiency."
Which of the following best describes the issue @john.doe is reporting, in terms of data quality?
Option A: The problem relates to the speed of the daily report generation process.
Option B: The missing postal codes represent a data completeness dimension, specifically indicating insufficient data coverage.
Option C: The issue highlights a need for improved customer segmentation strategies.
Option D: The problem is solely a matter of database indexing optimization.This situation exemplifies how data quality issues manifest in real-world scenarios. The core concern is the 'missing postal codes' – this falls under the data completeness dimension, meaning there's a lack of required information within the dataset. Incorrectly identifying it as report generation speed or customer segmentation misses the fundamental problem: the data itself isn't meeting expected standards. The incorrect options focus on peripheral issues; the primary concern is the absence of data.
17 / 34
You're reviewing a new API response from the e-commerce platform. The response contains an error message: `Invalid data format: Expected integer, received string for 'order_total'.` Considering this scenario, which data quality dimension is primarily being violated?
This error message clearly indicates a problem with 'conformance,' where the API response isn't adhering to its documented schema. The expected data type (integer) was not received, representing a deviation from the agreed-upon format. Failing to meet defined specifications is a core aspect of data conformity and directly impacts the reliability of the data being exchanged. Incorrectly specifying data types or formats often leads to downstream processing errors.
18 / 34
You're investigating a spike in error rates for the `orders` service. The monitoring dashboard shows a sudden increase in requests failing with a 'Timeout' error code (504). Considering this scenario, what does the term 'latency' most accurately refer to?
Latency refers to the delay experienced when transmitting data over a network. While related to response time, it's the *delay itself*, focusing on the duration of the transmission process – this is distinct from throughput or availability. Incorrect options relate to other performance metrics like concurrency and service uptime.
19 / 34
During a Slack conversation with the data engineering team, Sarah says: 'We need to ensure the ETL process is generating consistent data profiles across all downstream systems.' What does she primarily mean by 'data profiles'?
A 'data profile' describes the characteristics of a dataset. It goes beyond simply stating what fields exist; it provides statistical summaries and distributions – like average values, ranges, or null counts. This is crucial for understanding data quality issues and ensuring consistency across systems. Options A, C, and D relate to different aspects of data processing.
20 / 34
Reviewer Mark comments on a code change:
`// TODO: Validate that 'product_price' is always a positive number. Currently, we allow negative values due to a bug fix in the legacy system.` What does Mark primarily mean by 'data validation' in this context?
Mark is referring to data validation – the process of verifying that data meets specified criteria, such as being positive. This prevents incorrect values from entering the system and causing problems later. The key here is proactively checking for validity *before* processing, not just reacting to errors.
21 / 34
Alex sends this message in a Slack channel:
`'I'm seeing a high number of records with null values in the 'customer_segment' field. This could be affecting our marketing campaigns and potentially skewing our customer analysis.' What is 'null' referring to in this message, and why does it represent a data quality concern?
'Null' represents the absence of a value – it signifies that a particular field in a database record doesn't contain any information. This is problematic because missing data can lead to inaccurate analysis and flawed decision-making within marketing campaigns. It highlights a gap in expected data.
22 / 34
The API returns the following response:
`{
"status": "error",
"code": 400,
"message": "Invalid data format: Expected string, received integer for 'product_id'."
}`. What does this message indicate about the quality of the incoming data?
This error message clearly states that the 'product_id' field was expected to be a string but received an integer. This indicates a data format mismatch – a fundamental data quality issue. Incorrect data types can cause processing failures and inaccurate results.
23 / 34
A pull request description reads:
`'Fix: Corrected the calculation for 'discount_amount'. The previous formula was incorrectly using the product price as a percentage, leading to inaccurate discounts. This change ensures the discount is calculated correctly based on the actual product cost.' What aspect of data accuracy does this PR primarily address?
This PR focuses on 'data correctness' – making sure the calculated discount amount accurately represents the intended discount based on the product cost. The original issue stemmed from an incorrect formula, demonstrating a flawed calculation and therefore, incorrect data. This is core to data quality.
24 / 34
During the daily stand-up meeting, David says: 'We're seeing an increasing number of records in the sales table where the 'customer_country' field is blank. We need to investigate why this is happening and implement a process to ensure accurate country data is captured.' What does David mean by 'blank' in this context, and what action should he take?
'Blank' signifies that a particular field in the sales table lacks any data – it's empty. This represents a significant data quality issue because it prevents accurate reporting and analysis based on customer location. David needs to investigate *why* records are missing this crucial information.
25 / 34
Reviewer Mark comments on a code change:
`// TODO: Validate that 'product_price' is always a positive number. Currently, we allow negative values due to a bug fix in the legacy system.` What does Mark primarily mean by 'data validation' in this context?
Mark is referring to data validation – the process of verifying that data meets specified criteria, such as being positive. This prevents incorrect values from entering the system and causing problems later. The key here is proactively checking for validity *before* processing, not just reacting to errors.
26 / 34
Alex sends this message in a Slack channel:
`'I'm seeing a high number of records with null values in the 'customer_segment' field. This could be affecting our marketing campaigns and potentially skewing our customer analysis.' What is 'null' referring to in this message, and why does it represent a data quality concern?
'Null' represents the absence of a value – it signifies that a particular field in a database record doesn't contain any information. This is problematic because missing data can lead to inaccurate analysis and flawed decision-making within marketing campaigns. It highlights a gap in expected data.
27 / 34
The API returns the following response:
`{
"status": "error",
"code": 400,
"message": "Invalid data format: Expected string, received integer for 'product_id'."
}`. What does this message indicate about the quality of the incoming data?
This error message clearly states that the 'product_id' field was expected to be a string but received an integer. This indicates a data format mismatch – a fundamental data quality issue. Incorrect data types can cause processing failures and inaccurate results.
28 / 34
A pull request description reads:
`'Fix: Corrected the calculation for 'discount_amount'. The previous formula was incorrectly using the product price as a percentage, leading to inaccurate discounts. This change ensures the discount is calculated correctly based on the actual product cost.' What aspect of data accuracy does this PR primarily address?
This PR focuses on 'data correctness' – making sure the calculated discount amount accurately represents the intended discount based on the product cost. The original issue stemmed from an incorrect formula, demonstrating a flawed calculation and therefore, incorrect data. This is core to data quality.
29 / 34
During the daily stand-up meeting, David says: 'We're seeing an increasing number of records in the sales table where the 'customer_country' field is blank. We need to investigate why this is happening and implement a process to ensure accurate country data is captured.' What does David mean by 'blank' in this context, and what action should he take?
'Blank' signifies that a particular field in the sales table lacks any data – it's empty. This represents a significant data quality issue because it prevents accurate reporting and analysis based on customer location. David needs to investigate *why* records are missing this crucial information.
30 / 34
Reviewer Mark comments on a code change:
`// TODO: Validate that 'product_price' is always a positive number. Currently, we allow negative values due to a bug fix in the legacy system.` What does Mark primarily mean by 'data validation' in this context?
Mark is referring to data validation – the process of verifying that data meets specified criteria, such as being positive. This prevents incorrect values from entering the system and causing problems later. The key here is proactively checking for validity *before* processing, not just reacting to errors.
31 / 34
Alex sends this message in a Slack channel:
`'I'm seeing a high number of records with null values in the 'customer_segment' field. This could be affecting our marketing campaigns and potentially skewing our customer analysis.' What is 'null' referring to in this message, and why does it represent a data quality concern?
'Null' represents the absence of a value – it signifies that a particular field in a database record doesn't contain any information. This is problematic because missing data can lead to inaccurate analysis and flawed decision-making within marketing campaigns. It highlights a gap in expected data.
32 / 34
The API returns the following response:
`{
"status": "error",
"code": 400,
"message": "Invalid data format: Expected string, received integer for 'product_id'."
}`. What does this message indicate about the quality of the incoming data?
This error message clearly states that the 'product_id' field was expected to be a string but received an integer. This indicates a data format mismatch – a fundamental data quality issue. Incorrect data types can cause processing failures and inaccurate results.
33 / 34
A pull request description reads:
`'Fix: Corrected the calculation for 'discount_amount'. The previous formula was incorrectly using the product price as a percentage, leading to inaccurate discounts. This change ensures the discount is calculated correctly based on the actual product cost.' What aspect of data accuracy does this PR primarily address?
This PR focuses on 'data correctness' – making sure the calculated discount amount accurately represents the intended discount based on the product cost. The original issue stemmed from an incorrect formula, demonstrating a flawed calculation and therefore, incorrect data. This is core to data quality.
34 / 34
During the daily stand-up meeting, David says: 'We're seeing an increasing number of records in the sales table where the 'customer_country' field is blank. We need to investigate why this is happening and implement a process to ensure accurate country data is captured.' What does David mean by 'blank' in this context, and what action should he take?
'Blank' signifies that a particular field in the sales table lacks any data – it's empty. This represents a significant data quality issue because it prevents accurate reporting and analysis based on customer location. David needs to investigate *why* records are missing this crucial information.
Frequently Asked Questions
What does the "Data Quality Vocabulary" exercise practise?
Practice English vocabulary for data quality: completeness, accuracy, validity, data contracts, row count anomalies, and reconciliation for data engineers and analysts.
How many questions are in this exercise?
This exercise has 34 questions, each multiple-choice with a full explanation shown after you answer.
What English level is this exercise for?
This exercise is tagged Intermediate. If the vocabulary feels difficult, browse the Data Engineering Language category page for an easier module to start with.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free with no account, sign-up, or paywall.
Do I get feedback if I answer incorrectly?
Yes — whichever option you choose, right or wrong, you'll immediately see an explanation clarifying the correct term and why the other options don't fit.
Can I retry this exercise?
Yes — once you finish all the questions, a "Try again" button on the results screen resets the exercise so you can practise as many times as you like.
Do I need an account to track my progress?
No account is required. Your progress bar and score for this session are tracked in the browser as you go, but nothing is saved once you leave the page.
Is "Data Quality Vocabulary" part of a larger series?
Yes — it's one exercise in the Data Engineering Language category on CoderSlingo. See the category page for the full list of related exercises on similar terminology.
Can I link directly to this exercise?
Yes — this exercise has its own permanent URL, so you can bookmark it or share the link directly with a colleague or study partner.
Where can I find more exercises like this one?
See the Data Engineering Language category page for related exercises, or browse the main Exercises hub for other IT English topics.