DA0-002 Practice Test 31 — 15 Questions

Question 1

Refer to the exhibit. Which data concept does this exhibit best represent?

Accepted Answer

JSON schema. The exhibit shows a structured data format with key-value pairs, arrays, and nested objects enclosed in curly braces, which is the standard syntax for JSON (JavaScript Object Notation). A JSON schema defines the structure, data types, and validation rules for JSON data, making it the best fit for the exhibit. The presence of fields like "name", "age", and "address" with specific types (e.g., string, integer) confirms this is a schema definition, not a generic data representation.

Answer

Entity relationship diagram

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Data quality rule

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Data dictionary

Question 2

A data scientist is building a machine learning model to predict customer churn. The dataset includes both numerical features (age, income) and categorical features (gender, marital status). Which data concept describes the process of converting categorical features into numerical values that can be used by the algorithm?

Accepted Answer

Encoding. Encoding is the correct data concept because it transforms categorical features (like gender and marital status) into numerical representations (e.g., one-hot encoding, label encoding) that machine learning algorithms can process. Unlike feature scaling or dimensionality reduction, encoding directly addresses the incompatibility of non-numeric data with mathematical model operations.

Answer

Data sampling

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Feature scaling

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Dimensionality reduction

Question 3

When the analyst runs the query, it fails. What is the most likely reason?

Accepted Answer

The alias 'TotalValue' cannot be used in the WHERE clause.. The alias 'TotalValue' is defined in the SELECT clause but is referenced in the WHERE clause. In SQL, column aliases cannot be used in the WHERE clause because the WHERE clause is evaluated before the SELECT clause, so the alias does not yet exist at that point in the query execution order. This causes a syntax or 'unknown column' error.

Answer

The table name 'Products' is misspelled.

Answer

The data types of Price and Quantity are incompatible.

Answer

The expression 'Price * Quantity' is invalid in SQL.

Question 4

Which of the following is an example of qualitative data?

Accepted Answer

Customer feedback comments. Customer feedback comments are qualitative data because they consist of non-numerical, descriptive text that captures opinions, sentiments, or experiences. Unlike quantitative data, which can be measured or counted, qualitative data is categorical and often requires thematic analysis to derive insights.

Answer

Stock price

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Number of website visitors

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Product weight in grams

Question 5

A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positives correctly identified. With only 10% of actual churners detected, the model has a recall of 0.1, which directly highlights the failure to capture churners despite high overall accuracy.

Answer

Accuracy

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F1 score

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Precision

Question 6

Refer to the exhibit. An Avro schema is defined as shown. Which data design concept does this represent?

Accepted Answer

Schema-on-write. Avro requires defining the schema before writing data, imposing structure at write time (schema-on-write). Schema-on-read would apply structure when reading, and schema-less design has no predefined schema.

Answer

Schema-on-read

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Schema-less design

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Dynamic schema

Question 7

Refer to the exhibit. The data shown is an example of which data concept?

Accepted Answer

Structured data. The exhibit shows data organized into rows and columns with a fixed schema (e.g., 'Name', 'Age', 'City'), which is the defining characteristic of structured data. Structured data conforms to a predefined data model, typically stored in relational databases or spreadsheets, and can be easily queried using SQL. This tabular format with consistent data types per column is the classic example of structured data.

Answer

Unstructured data

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Metadata

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Semi-structured data

Question 8

An e-commerce company uses a star schema for its data warehouse. The fact table 'sales_fact' contains foreign keys to dimension tables: customer_dim, product_dim, time_dim, and store_dim. A business user wants to know the total sales for each product category in the last month. Which join operation is required to retrieve this data?

Accepted Answer

Inner join between fact table and dimension tables. To retrieve total sales for each product category, you need to join the fact table with the product dimension table to map product keys to categories, and with the time dimension table to filter on the last month. An inner join is correct because it returns only rows where matching keys exist in both tables, which is the standard approach for star-schema queries where all required dimension attributes are present. This ensures that only valid sales transactions with corresponding product and time entries are included in the aggregation.

Answer

Self-join on the fact table

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Cross join between fact and dimension tables

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Left outer join between fact and dimension tables

Question 9

Refer to the exhibit. A data analyst attempts to visualize the dataset but receives a permission error. The analyst's username is 'analyst2'. What is the most likely cause?

Accepted Answer

The analyst's username is not in the users list. The policy lists only 'admin' and 'analyst'; 'analyst2' is not included, so access is denied.

Answer

The analyst lacks write permission

Answer

The analyst lacks read permission

Answer

JSON syntax error prevents parsing

Question 10

A financial application requires fast query performance for aggregations on large historical datasets. The schema has many lookup tables. Which schema design is most efficient for this workload?

Accepted Answer

Star schema. The star schema is most efficient for this workload because it denormalizes lookup tables into dimension tables, reducing the number of joins required for aggregations. This design optimizes query performance for large historical datasets by enabling faster full table scans and simpler query plans, which is critical for financial applications needing rapid aggregations.

Answer

Snowflake schema

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Wide table

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Third normal form (3NF)

Question 11

A data analyst is building a linear regression model to predict sales based on advertising spend across TV, radio, and newspaper channels. Which TWO diagnostics should the analyst perform to validate the model assumptions?

Accepted Answer

Q-Q plot to assess normality of residuals. A Q-Q plot is used to assess whether the residuals of a linear regression model are approximately normally distributed, which is a key assumption for valid inference (e.g., p-values and confidence intervals). Option E is correct because residual plots (e.g., fitted vs. residuals) are the standard diagnostic to check for homoscedasticity—constant variance of errors across all levels of the independent variables—another core assumption of ordinary least squares regression.

Answer

Durbin-Watson test for autocorrelation

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Variance inflation factor (VIF) for multicollinearity

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Cook's distance to identify influential points

Question 12

Refer to the exhibit. An analyst runs the following query:
SELECT product_id, AVG(quantity)
FROM sales
GROUP BY product_id
HAVING AVG(quantity) > 8;
Which product_id(s) will be returned?

Accepted Answer

P001 and P003. The query groups sales by product_id and filters groups where the average quantity exceeds 8. From the exhibit (not shown but implied), only product_ids P001 and P003 have an AVG(quantity) > 8, so they are returned. The HAVING clause operates on aggregated data after GROUP BY, unlike WHERE which filters rows before aggregation.

Answer

P001 only

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P002 only

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P003 only

Question 13

During an ETL process, a data quality check fails due to duplicate customer IDs. Which data quality dimension is violated?

Accepted Answer

Uniqueness. Duplicate customer IDs violate the uniqueness dimension because uniqueness ensures that each record in a dataset has a distinct identifier with no duplicates. In an ETL process, a primary key or unique constraint on the customer ID column would reject duplicate values, causing the data quality check to fail. This is distinct from consistency, which checks for logical agreement across data sources.

Answer

Consistency

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Completeness

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Accuracy

Question 14

Refer to the exhibit. A database administrator notices that queries filtering on both CustomerID and OrderDate are slow. Which single change would most likely improve performance for such queries?

Accepted Answer

Add a composite index on (CustomerID, OrderDate). A composite index on (CustomerID, OrderDate) allows the database to use a single index to filter on both columns, which is more efficient than using separate indexes and combining results.

Answer

Partition the table by OrderDate

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Convert TotalAmount to VARCHAR

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Remove the primary key constraint

Question 15

A data quality report shows that 95% of records have all required fields completed, but 20% of the completed fields contain values that are outside valid ranges. Which data quality dimension is most affected?

Accepted Answer

Accuracy. Accuracy measures how well data reflects real-world values or a defined standard. Here, 20% of completed fields contain values outside valid ranges, meaning the data is present but incorrect, directly degrading accuracy. Completeness (95% filled) is high, but the core issue is that the values themselves are wrong, not missing or late.

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Consistency

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Timeliness

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Completeness