DA0-001 Exam Simulation — 100 Questions

Question 1

Drag and drop the steps to clean a dataset with missing values in the correct order.

Question 2

Drag and drop the steps to normalize a database table from 1NF to 3NF in the correct order.

Question 3

Drag and drop the steps to create a data visualization dashboard in the correct order.

Question 4

Drag and drop the steps to implement a data classification policy in the correct order.

Question 5

Drag and drop the steps for the ETL (Extract, Transform, Load) process in the correct order.

Question 6

Drag and drop the steps to perform a data backup using the 3-2-1 rule in the correct order.

Question 7

Drag and drop the steps to conduct a hypothesis test in the correct order.

Question 8

Drag and drop the steps to perform a data audit in the correct order.

Question 9

Drag and drop the steps to resolve data integration conflicts in the correct order.

Question 10

Drag and drop the steps to perform a root cause analysis on data quality issues in the correct order.

Question 11

A data analyst is using SQL to extract data. The analyst wants to retrieve all records from a table named 'sales' where the 'amount' column is greater than 100. Which SQL clause should be used?

Accepted Answer

WHERE. The WHERE clause in SQL is used to filter records based on a specified condition, such as 'amount > 100'. It is applied directly to the rows in the 'sales' table before any grouping or ordering, making it the correct choice for retrieving only records where the amount exceeds 100.

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ORDER BY

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GROUP BY

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HAVING

Question 12

A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?

Accepted Answer

Use SMOTE. SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic samples for the minority class (negative comments) by interpolating between existing minority instances, rather than simply duplicating them. This addresses the 95:5 imbalance without the information loss of undersampling or the overfitting risk of naive oversampling.

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Use accuracy as the evaluation metric

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Undersample the majority class

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Oversample the majority class

Question 13

A data analyst creates a bubble chart showing country GDP (x-axis), life expectancy (y-axis), and population (bubble size). However, large bubbles overlap and obscure many data points. Which corrective action should the analyst take?

Accepted Answer

Set bubble opacity to 70%. Setting bubble opacity to 70% allows overlapping bubbles to become semi-transparent, so data points underneath remain visible. This technique preserves the original data representation (GDP, life expectancy, and population) without altering the chart's scale or removing data. It is a standard visualization practice for handling overplotting in dense scatter plots and bubble charts.

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Increase the chart canvas size

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Reduce all bubble sizes uniformly

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Remove outlier countries with large populations

Question 14

Which THREE factors should be considered when choosing a chart type for a dataset?

Accepted Answer

The data types (categorical, numerical, time series). Option B is correct because the data type (categorical, numerical, time series) determines which chart types are semantically valid. For example, a line chart requires a continuous numerical or time-series axis, while a bar chart works with categorical data. Choosing a chart that mismatches the data type can misrepresent the underlying distribution or trends.

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The animation capabilities of the software

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The color scheme of the company logo

Question 15

A data analyst creates a heatmap to show website click-through rates by hour and day of week. The heatmap uses a green-to-red gradient, but users cannot distinguish between moderate values. What is the best fix?

Accepted Answer

Switch to a diverging color scheme with a neutral center. A diverging color scheme with a neutral center (e.g., white or light gray) allows users to distinguish moderate values from extremes by providing a clear visual midpoint. The original green-to-red gradient is a sequential scheme that compresses moderate values into a narrow perceptual range, making them indistinguishable. Switching to a diverging palette solves this by mapping moderate values to the neutral center and extremes to the endpoints.

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Remove all but the highest and lowest values

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Add black borders around each cell

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Increase the size of each heatmap cell

Question 16

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. Option D is correct because 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 17

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 18

A healthcare database stores patient records. Each patient has a unique patient_id, and the database includes a table 'visits' with visit_id, patient_id, visit_date, and diagnosis_code. To ensure data integrity, which constraint should be applied to the patient_id column in the 'visits' table?

Accepted Answer

Foreign key. Option B is correct because a foreign key constraint ensures that patient_id in visits references a valid patient_id in the patient table. Option A is wrong because primary key ensures uniqueness in its own table. Option C is wrong because unique constraint prevents duplicates. Option D is wrong because check constraint validates values based on a condition.

Answer

Unique constraint

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Primary key

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Check constraint

Question 19

A data engineer is designing a data warehouse for a multinational corporation. The company has sales data from different regions with varying currencies and date formats. To ensure consistency, which data concept should be applied to standardize the data before loading into the warehouse?

Accepted Answer

Data transformation. Data transformation is the correct concept because it involves converting data from source formats (e.g., different currencies and date formats) into a consistent, standardized format before loading into the data warehouse. This process includes applying conversion rules, such as using ISO 8601 for dates and a single base currency (e.g., USD) with exchange rate tables, ensuring uniformity across all regional data. Without transformation, the warehouse would contain incompatible data types, breaking referential integrity and analytical queries.

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Data cleansing

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Data profiling

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Data masking

Question 20

A data analyst wants to compare the sales performance of four different stores over the same time period. Which chart type is most suitable?

Accepted Answer

Grouped bar chart. A grouped bar chart is the most suitable choice because it allows direct comparison of discrete categories (four stores) across a common time period, with each group representing a time interval and individual bars representing each store's sales. This chart type excels at side-by-side comparisons of multiple entities over the same categorical axis, making it ideal for the analyst's goal.

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Line chart with multiple lines

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Stacked bar chart

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Pie chart with multiple pies