DA0-002 Multiple Select — 50 Questions

Question 1

Which TWO of the following are true about correlation and causation? (Select TWO).

Accepted Answer

Correlation does not imply causation. Correlation measures the strength and direction of a linear relationship between two variables, but it does not imply that one variable causes the other. Causation requires controlled experiments with randomization to rule out confounding variables and establish a cause-effect relationship.

Answer

Correlation measures both linear and nonlinear relationships

Answer

Causation can always be inferred from a controlled experiment without randomization

Answer

If two variables are highly correlated, one must cause the other

Question 2

A business analyst is asked to distinguish between KPIs and metrics for a new dashboard. According to CompTIA Data+, which TWO statements correctly differentiate KPIs from metrics? (Choose two.)

Accepted Answer

KPIs are directly tied to strategic business objectives. KPIs are tied to strategic objectives and are often leading or lagging indicators. Metrics are operational measurements that may support KPIs but are not necessarily strategic.

Answer

KPIs and metrics are interchangeable terms

Answer

Metrics are always more important than KPIs

Answer

KPIs are always lagging indicators

Question 3

A data analyst is performing data profiling on a customer table. Which TWO of the following are key metrics to assess data quality? (Select TWO.)

Accepted Answer

Minimum and maximum values. Null counts indicate missing values, and min/max values can reveal outliers or unexpected ranges. Row count alone doesn't assess quality; cardinality and data type verification are also important but the question asks for key metrics among the options.

Answer

Row count

Answer

Cardinality

Answer

Data type verification

Question 4

An organization is implementing a data lake to store raw data from various sources. Which THREE characteristics are typically associated with a data lake compared to a data warehouse?

Accepted Answer

Supports batch and real-time processing. Data lakes are designed to ingest and process data in both batch and real-time streaming modes, unlike data warehouses which primarily handle batch ETL workloads. This flexibility is enabled by technologies like Apache Kafka for streaming and Apache Spark for batch processing, allowing the data lake to support diverse ingestion patterns without pre-schematization.

Answer

Supports only structured data

Answer

Requires data transformation before loading

Question 5

A company needs to store data that is highly interconnected, such as social network relationships. Which two database types are best suited for this? (Select TWO.)

Accepted Answer

Document store (e.g., MongoDB). Graph databases excel at storing relationships, and NoSQL databases support flexible schemas for interconnected data.

Answer

Relational database

Answer

Wide-column store (e.g., Cassandra)

Answer

Key-value store (e.g., Redis)

Question 6

Which THREE are best practices for acquiring data via web scraping? (Select exactly 3)

Accepted Answer

Respect robots.txt. Options B, C, and E are correct because best practices for web scraping include respecting robots.txt (ethical and legal compliance), identifying yourself with a user-agent (transparency), and limiting request rate (avoid overloading servers). Option A is incorrect because using multiple IP addresses is often used to circumvent blocking, which can violate terms of service and is not a recommended best practice. Option D is incorrect because scraping all data without regard to terms is unethical and potentially illegal.

Answer

Use multiple IP addresses

Answer

Scrape all data without regard to terms

Question 7

Which TWO of the following are commonly used techniques for handling missing data in a dataset? (Select TWO).

Accepted Answer

Mean imputation. Mean imputation is a commonly used technique for handling missing numerical data where the missing value is replaced with the mean of the observed values for that feature. It preserves the sample size and is simple to implement, though it can reduce variance and distort relationships if data is not missing completely at random.

Answer

Mode imputation

Answer

Dropping columns with missing data

Answer

Dropping rows with missing data

Question 8

Which TWO of the following are examples of supervised learning algorithms?

Accepted Answer

Linear regression. Linear regression is a supervised learning algorithm because it learns a mapping from input features to a continuous target variable using labeled training data. The model minimizes the difference between predicted and actual values (e.g., via ordinary least squares) to make predictions on new data.

Answer

K-means clustering

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Principal component analysis (PCA)

Answer

Apriori algorithm

Question 9

Which THREE of the following are assumptions of linear regression? (Select THREE).

Accepted Answer

Independence of errors. Independence of errors is a core assumption of linear regression, meaning the residuals (errors) should not be correlated with each other. This is critical for valid inference because correlated errors violate the Gauss-Markov theorem, leading to biased standard errors and unreliable hypothesis tests. In time series data, this assumption is often violated due to autocorrelation, which can be detected using the Durbin-Watson test.

Answer

Normal distribution of independent variables

Answer

Multicollinearity among independent variables

Question 10

A data analyst frequently receives ad hoc requests for the same type of analysis. Which TWO approaches could reduce the number of ad hoc requests?

Accepted Answer

Create a scheduled report that covers the common analysis. Building self-service capabilities and iterating on feedback to improve scheduled reports can address recurring needs and reduce ad hoc requests.

Answer

Increase data freshness to real-time

Answer

Add more security to the data

Answer

Ignore the requests until they become urgent

Question 11

Which TWO are common pitfalls when communicating data insights?

Accepted Answer

Using misleading scales on charts. Using misleading scales on charts (e.g., truncating the y-axis or using non-zero baselines) distorts the visual representation of data, leading to incorrect interpretations. This violates the principle of data integrity in data visualization, as it can exaggerate or minimize trends, making it a common pitfall when communicating data insights.

Answer

Explaining assumptions clearly

Answer

Including a clear call to action

Answer

Providing context for the data

Question 12

A data governance team is defining roles and responsibilities for data management. Which TWO of the following are common data governance roles? (Select TWO).

Accepted Answer

Data steward. Data owner and data steward are standard roles in data governance, responsible for data ownership and stewardship respectively.

Answer

Data scientist

Answer

Database administrator

Answer

Data analyst

Question 13

Which TWO chart types are best suited for visualizing the distribution of a single continuous variable? (Select two.)

Accepted Answer

Box plot. Histogram shows frequency bins; box plot shows quartiles and outliers.

Answer

Scatter plot

Answer

Line chart

Answer

Pie chart

Question 14

A data analyst is preparing a dataset for analysis and needs to handle outliers. Which TWO of the following are common methods for treating outliers?

Accepted Answer

Removal. Capping (winsorizing) limits extreme values, and removal simply deletes outlier rows. Transformation (e.g., log) can also reduce impact but is not listed here; normalization and imputation are not primary outlier treatments.

Answer

Normalization

Answer

Imputation

Answer

Standardization

Question 15

A company runs an A/B test to compare a new website layout (treatment) against the current layout (control). The conversion rate for the control is 5% and for the treatment is 5.5%. The p-value is 0.06 at α=0.05. Which THREE of the following conclusions are valid?

Accepted Answer

There is not enough evidence to conclude that the new layout is better.. The p-value > α, so fail to reject the null; the difference is not statistically significant. However, the observed lift is 0.5% (absolute). Sample size might be insufficient; statistical power could be low.

Answer

The test has sufficient power to detect the observed effect.

Answer

The new layout significantly increases conversion rate.

Question 16

An e-commerce company wants to analyze sales performance across product categories. The dataset includes transaction amounts and a column 'category' with values (Electronics, Clothing, Home). The analyst decides to use stratified sampling to ensure proportional representation. Which THREE steps are required to implement this? (Select THREE).

Accepted Answer

Calculate the proportion of each category in the population. Stratified sampling requires dividing the population into strata (categories), then randomly sampling from each stratum in proportion to its size. Combining strata or simple random sampling without stratification would not achieve proportional representation.

Answer

Select every 10th transaction from the entire dataset

Answer

Combine all categories into a single group and perform simple random sampling

Question 17

A data analyst is cleaning text data in a SQL database. Which THREE string functions are commonly used to standardize and clean text? (Choose three.)

Accepted Answer

REPLACE. TRIM removes leading/trailing spaces, UPPER/LOWER standardize case, REPLACE substitutes substrings. CONCAT concatenates strings, LENGTH returns length, SUBSTRING extracts part of string.

Answer

LENGTH

Answer

CONCAT

Question 18

A data analyst is using pandas to clean a DataFrame that contains missing values in the 'age' and 'income' columns. Which THREE pandas methods are appropriate for handling missing data? (Select THREE).

Accepted Answer

dropna(). Common pandas methods for missing data include dropna (remove rows with NaN), fillna (replace NaN with a value), and apply with a custom function. Merge is for combining DataFrames; pivot_table is for reshaping.

Answer

pivot_table()

Answer

merge()

Question 19

An analyst is presenting findings to stakeholders. Which TWO techniques effectively communicate uncertainty in data? (Choose TWO.)

Accepted Answer

Include confidence intervals.. Confidence intervals provide a range of values that likely contain the true population parameter, offering a clear measure of uncertainty around a point estimate. This technique is fundamental in inferential statistics and directly communicates the precision of the data, helping stakeholders understand the reliability of the findings.

Answer

Use only point estimates.

Answer

Remove all outliers from the data.

Answer

State exact numbers without ranges.

Question 20

Which TWO of the following chart types are appropriate for showing the distribution of a continuous variable? (Choose 2.)

Accepted Answer

Box plot. A box plot is correct because it visually summarizes the distribution of a continuous variable through its five-number summary (minimum, first quartile, median, third quartile, maximum), clearly showing spread, central tendency, and potential outliers. This makes it ideal for distribution analysis in data visualization contexts like the DA0-001 exam.

Answer

Bar chart

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Pie chart

Answer

Line chart