DA0-002 Practice Test 12 — 15 Questions

Question 1

A retail company wants to predict sales based on advertising spend and season. Which data modeling technique should the analyst use?

Accepted Answer

Multiple linear regression. Multiple linear regression is the correct technique because the analyst needs to model a continuous outcome (sales) based on two or more predictor variables: advertising spend (continuous) and season (categorical, typically encoded as dummy variables). This allows the model to capture the independent effect of each predictor on sales, which simple linear regression cannot do because it only handles one predictor.

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Simple linear regression

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Logistic regression

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K-means clustering

Question 2

A healthcare organization maintains a database of patient records. The database has a table 'patients' with columns: patient_id (primary key), first_name, last_name, date_of_birth, gender, and last_visit_date. A data analyst is tasked with creating a report that lists all patients who have not visited in the last two years. The analyst writes a query: SELECT * FROM patients WHERE last_visit_date < DATEADD(year, -2, GETDATE()); However, the query returns zero rows, even though the analyst knows there are patients who have not visited for over two years. Upon inspection, the analyst discovers that the last_visit_date column contains NULL values for patients who have never visited. Which modification to the query should the analyst make to include patients with NULL last_visit_date?

Accepted Answer

Add OR last_visit_date IS NULL to the WHERE clause.. The original query uses a WHERE clause that compares last_visit_date to a computed date, but NULL comparisons in SQL always yield UNKNOWN, so rows with NULL last_visit_date are excluded. Adding OR last_visit_date IS NULL explicitly includes those rows, ensuring patients who have never visited are listed in the report.

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Remove the WHERE clause entirely.

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Use COALESCE(last_visit_date, '1900-01-01') in the WHERE clause.

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Add AND last_visit_date IS NOT NULL to the WHERE clause.

Question 3

A dashboard should be designed so that the most important metric is prominently displayed. This is an example of which design principle?

Accepted Answer

Visual hierarchy. Visual hierarchy is the design principle that dictates arranging elements to guide the viewer's eye to the most important information first. By making the key metric the most prominent element (e.g., larger, bolder, or top-left), the dashboard ensures immediate comprehension of the primary data point, which is critical for operational decision-making.

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Data-ink ratio

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Consistent color coding

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Appropriate precision

Question 4

A company requires real-time masking of credit card numbers for customer support agents while allowing full access for accountants. Which technique should be implemented?

Accepted Answer

Dynamic data masking. Dynamic data masking (DDM) applies masking rules at query runtime based on user privileges, allowing accountants full access while customer support agents see only masked credit card numbers. Unlike static masking, DDM does not alter the underlying stored data, making it ideal for real-time, role-based obfuscation without duplicating or transforming the database.

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Tokenization

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Static data masking

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Data encryption

Question 5

A company is designing a database for an e-commerce application that requires high transaction throughput and must guarantee that each transaction is processed atomically. Which property of ACID ensures that a transaction is either fully completed or not executed at all?

Accepted Answer

Atomicity. Atomicity guarantees that a transaction is treated as a single unit; it either completes entirely or is rolled back, preventing partial updates.

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Isolation

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Durability

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Consistency

Question 6

A data governance team is establishing policies to ensure data quality. They define rules for data accuracy, completeness, and consistency. Which data governance function is primarily responsible for defining and enforcing these rules?

Accepted Answer

Data quality management. Data quality management is the function that sets standards and processes to ensure data is accurate, complete, and consistent. Data stewardship often involves implementing these rules, but the overall responsibility lies with data quality management.

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Data stewardship

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Data ownership

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Master data management

Question 7

An e-commerce company wants to integrate product pricing data from competitor websites to adjust its own prices dynamically. They plan to scrape pricing pages every hour. However, the competitors' websites have anti-scraping measures such as IP blocking and CAPTCHAs. The company's legal team also advises caution regarding terms of service. Which data acquisition strategy is both effective and compliant?

Accepted Answer

Negotiate with competitors to obtain pricing data via API agreements. Negotiating with competitors to obtain pricing data via API agreements is the most compliant approach, as it avoids violating terms of service and ensures reliable data access. Using rotating proxies and automation to bypass anti-scraping measures may be effective but could violate laws or terms of service. Manual recording is too slow and not dynamic. Using a public data aggregator may not provide the specific competitor data needed and could be costly.

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Use a public data aggregator that already provides competitor pricing with permission

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Use a rotating proxy service and human-like browser automation to bypass blocks

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Instruct staff to manually record prices once a week

Question 8

A company uses an OLTP system for processing customer transactions. Which characteristic is most important for this system to ensure that each transaction is processed reliably, even if multiple users access the system simultaneously?

Accepted Answer

It follows ACID properties. ACID properties (Atomicity, Consistency, Isolation, Durability) are essential for OLTP systems to ensure reliable transaction processing.

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It uses a columnar storage format

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It stores data in a denormalized schema

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It supports complex analytical queries

Question 9

A dataset contains customer records with a column for 'Phone Number' that should be unique. However, the analyst finds several duplicate phone numbers. Which data quality dimension is primarily affected?

Accepted Answer

Uniqueness. Uniqueness refers to the expectation that each record or attribute value should be unique. Duplicate phone numbers violate uniqueness.

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Completeness

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Accuracy

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Consistency

Question 10

Which database index type is most commonly used for exact-match lookups and range queries in a B-tree structure?

Accepted Answer

B-tree index. A B-tree index is the correct answer because it maintains sorted data in a balanced tree structure, enabling both exact-match lookups (via equality searches) and efficient range queries (via ordered traversal of leaf nodes). This dual capability makes it the standard index type in relational databases like MySQL, PostgreSQL, and Oracle for general-purpose querying.

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Hash index

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Clustered index

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Bitmap index

Question 11

A data analyst trains a complex model that achieves 99% accuracy on training data but only 65% on new data. What is the most likely issue?

Accepted Answer

Overfitting. The model performs exceptionally well on training data (99% accuracy) but poorly on new data (65% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data rather than generalizing to unseen data, often due to excessive complexity (e.g., too many parameters or deep layers). This results in high variance and poor performance on validation or test sets.

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Underfitting

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Multicollinearity

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High bias

Question 12

Which TWO of the following are characteristics of structured data? (Choose TWO.)

Accepted Answer

Has a defined schema. Structured data is defined by having a predefined schema, which specifies the data types, constraints, and relationships for each field. This schema ensures consistency and allows for efficient querying and validation. Option A is correct because a defined schema is a fundamental characteristic of structured data, as seen in relational database tables where each column has a specific data type and constraints.

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Requires NoSQL databases for storage

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Often contains natural language text

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Cannot be queried using SQL

Question 13

A financial institution wants to analyze transaction networks to detect fraud rings. Which database type is best suited for this analysis?

Accepted Answer

Graph database. A graph database is designed to store and traverse relationships between entities, making it ideal for analyzing transaction networks where connections between accounts, merchants, and transactions reveal fraud rings. Its native graph model (nodes and edges) allows efficient pattern matching and pathfinding queries, such as detecting circular transactions or shared attributes, which are common in fraud detection.

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Wide-column store

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Key-value store

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Document store

Question 14

During data exploration, an analyst notices that the target variable has a heavily right-skewed distribution. Which data transformation would be most appropriate to make the distribution more symmetric?

Accepted Answer

Log transformation. Log transformation is appropriate for heavily right-skewed distributions because it compresses the high values and spreads out the low values, making the distribution more symmetric. Square root transformation is better for moderate skew, and reciprocal transformation is for severe skew. Therefore, option A (Log transformation) is correct.

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Reciprocal transformation

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No transformation needed

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Square root transformation

Question 15

Which THREE of the following are appropriate ways to handle outliers when communicating data insights?

Accepted Answer

Document the outlier and its potential impact in the report.. Documenting the outlier and its potential impact in the report is a best practice for transparent and ethical data communication. It allows stakeholders to understand the anomaly's influence on the analysis and make informed decisions, rather than hiding or misrepresenting the data.

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Ignore the outlier and proceed with the analysis.

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Remove the outlier from the dataset to clean the data.