DA0-002 Practice Test 30 — 15 Questions

Question 1

A data analyst is building a dashboard that will be used by both executives and operational managers. The executives need high-level summaries, while managers need granular details. Which dashboard design principle should the analyst apply?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Design the dashboard with drill-down capabilities from summary to detail.. Drill-down capabilities allow users to start with a high-level summary (e.g., total revenue by region) and then interactively navigate to granular details (e.g., individual transactions) without overwhelming either audience. This design principle supports both executive and operational manager needs within a single dashboard, maintaining clarity and performance by loading only the required level of detail on demand.

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Use a single chart that shows both summary and detail simultaneously.

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Display all available data on one page for transparency.

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Create two separate dashboards for each audience.

Question 2

Which TWO of the following are examples of quantitative data? (Choose TWO.)

Accepted Answer

Age in years. Age in years is a numerical measurement that can be counted or measured on a ratio scale, making it quantitative data. Quantitative data represents quantities that can be expressed numerically and subjected to mathematical operations, such as calculating the average age of a group.

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Product color

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Customer satisfaction rating (Poor, Fair, Good)

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Country of origin

Question 3

Refer to the exhibit. Before running the code, the original salary column had 50 missing values. The median was calculated as 52000. After imputation, which of the following statements is true?

Accepted Answer

The median remains unchanged. Imputing missing values with the median (52000) replaces only the 50 missing entries with that value, leaving all original non-missing values unchanged. Since the median is a positional statistic, adding values equal to the current median does not shift the middle position of the sorted data, so the median remains unchanged. This is why option C is correct.

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The mean decreased significantly

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The standard deviation increased

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The minimum value decreased

Question 4

Refer to the exhibit. Which data quality dimension is being violated?

Accepted Answer

Consistency. The exhibit shows the same customer ID (C001) associated with two different customer names ('John Smith' and 'Jon Smith'), which violates the consistency dimension. Consistency requires that data values be free from contradiction and adhere to the same representation rules across the dataset. Here, the conflicting names for the same identifier break referential integrity and data uniformity.

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Uniqueness

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Timeliness

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Completeness

Question 5

A company's database has a table 'orders' with columns: order_id, customer_id, order_date, and total_amount. A data analyst needs to identify customers who have placed more than 5 orders in the past year. Which data concept should be used to group orders by customer and count them?

Accepted Answer

Aggregation with GROUP BY. The requirement to count orders per customer requires grouping rows by customer_id and then applying a count function. The GROUP BY clause in SQL aggregates rows that share a common value (customer_id) into summary rows, and the COUNT function tallies the number of orders per group. This is the standard approach for such 'per-customer' aggregations.

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Joining with other tables

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Filtering with WHERE clause

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Sorting with ORDER BY

Question 6

A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?

Accepted Answer

Mode imputation. Mode imputation is the most appropriate method for a categorical variable because it replaces missing values with the most frequently occurring category, preserving the distribution of the data. Unlike mean or median imputation, which are designed for numerical data, mode imputation maintains the categorical nature of the variable and avoids introducing invalid values. This approach is simple and effective when missing data is random and the category is well-represented.

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Drop rows with missing values

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Mean imputation

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Median imputation

Question 7

A data scientist is building a model to predict customer churn. The company's internal CRM system provides customer demographics and transaction history. They also purchase demographic data from a third-party vendor. How should the purchased data be classified?

Accepted Answer

Secondary data. Purchased demographic data from a third-party vendor is classified as secondary data because it was originally collected by another entity for a different purpose and is being reused by the data scientist for churn prediction. Secondary data contrasts with primary data, which is collected firsthand for the specific analysis at hand. This classification is independent of whether the data is structured or unstructured.

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Internal data

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Structured data

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Primary data

Question 8

Which THREE data quality dimensions are commonly assessed in a data profiling task?

Accepted Answer

Consistency. Consistency is a core data quality dimension assessed in data profiling because it evaluates whether data values are free from contradiction and adhere to the same representation rules across records. In profiling tools like Informatica or Talend, consistency checks identify violations such as 'NY' vs 'New York' in a state column, ensuring semantic uniformity.

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Scalability

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Availability

Question 9

Which TWO of the following are considered structured data?

Accepted Answer

A relational database table. A relational database table stores data in a predefined schema of rows and columns, where each column has a fixed data type. This rigid structure allows for efficient querying, indexing, and relational operations, making it a classic example of structured data.

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A PDF report with free-form text

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A JPEG image of a product

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A JSON file with nested key-value pairs

Question 10

An e-commerce company stores customer support emails in a text database, product images in a blob store, and sales transactions in a SQL table. Which data store holds only structured data?

Accepted Answer

SQL table. Structured data conforms to a predefined schema with rows and columns, enforcing data types and relationships. A SQL table is the canonical example of a structured data store because it organizes data into tables with fixed schemas, supports ACID transactions, and enables relational queries via SQL. In contrast, blob stores and text databases store unstructured or semi-structured data without a rigid schema.

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Blob store

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Text database

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None

Question 11

An organization wants to assign responsibility for data quality and metadata management. Which role is primarily accountable for defining data standards and ensuring data quality across a specific domain?

Accepted Answer

Data steward. The data steward is the role primarily accountable for defining data standards and ensuring data quality within a specific domain. This aligns with the DAMA-DMBOK framework, where the data steward acts as the business-side owner of data content, establishing rules for data entry, validation, and metadata management to maintain consistency and accuracy.

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Data analyst

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Data owner

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Data custodian

Question 12

A data analyst receives a dataset with a column 'salary' that contains values like '45,000', '55,000', and '65,000'. The analyst notices that the values are stored as text. Which data concept should be applied to convert the salary column from text to numeric format for analysis?

Accepted Answer

Data type conversion. Data type conversion is the correct concept because the salary values are stored as text (string) but need to be converted to a numeric type (e.g., integer or float) for mathematical operations like aggregation or averaging. In tools like Python (pandas `astype(float)`), SQL (`CAST(salary AS INTEGER)`), or Excel (`VALUE()` function), this explicit conversion ensures the data is treated as numbers, not strings. Without conversion, operations like `SUM` or `AVG` would fail or produce incorrect results.

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Data imputation

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Data validation

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Data normalization

Question 13

A retail company stores customer purchase history in a relational database. The database contains a table 'transactions' with columns: transaction_id, customer_id, product_id, quantity, price, and transaction_date. A data analyst needs to create a report that shows total revenue per customer for the last quarter. Which data concept describes the relationship between customer_id and total revenue?

Accepted Answer

Derived attribute. Total revenue is calculated by summing (quantity * price) for each customer, making it a derived attribute because it is computed from existing stored data (quantity and price) rather than stored directly. In the context of the 'transactions' table, customer_id is a stored key, but total_revenue is not stored; it is derived via aggregation, which matches the definition of a derived attribute in database design.

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Foreign key

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Composite attribute

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Atomic attribute

Question 14

A company needs to store raw, unprocessed data from IoT sensors for future machine learning experiments. The data is in various formats and schemas are not yet defined. Which storage solution is most appropriate?

Accepted Answer

Data lake. A data lake is the correct choice because it stores raw, unprocessed data in its native format (structured, semi-structured, or unstructured) without requiring a predefined schema. This aligns perfectly with the need to ingest IoT sensor data in various formats for future machine learning experiments, where schemas are not yet defined. Unlike data warehouses or data marts, a data lake supports schema-on-read, allowing the data to be transformed and queried later as needed.

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Data mart

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Data warehouse

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Operational database

Question 15

Which TWO of the following are characteristics of a data lake?

Accepted Answer

Retains raw data in native format. A data lake retains raw data in its native format, meaning data is ingested without transformation or schema enforcement. This allows storage of structured, semi-structured, and unstructured data as-is, preserving fidelity for future analytics. Unlike a data warehouse, a data lake does not require upfront schema definition, enabling flexible exploration and machine learning workloads.

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Optimized for OLTP

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Stores only structured data

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Enforces ACID transactions