DA0-002 Practice Test 36 — 15 Questions

Question 1

A large retail company is integrating customer data from two separate CRM systems into a new data warehouse. System A stores customer IDs as integers (e.g., 12345), while System B stores them as alphanumeric strings (e.g., 'CUST-12345-X'). Additionally, some customers exist in both systems but with slight name variations (e.g., 'John Smith' vs 'Jon Smith'). The data warehouse requires a unified customer table with a single unique identifier for each customer. The analyst needs to design the data acquisition process. Which of the following is the most appropriate first step?

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

Accepted Answer

Perform data profiling to analyze data distributions, data types, and quality issues in each source. Data profiling is the foundational first step in any data integration project. It systematically assesses source data types, formats, completeness, and quality issues (e.g., integer vs. alphanumeric IDs, name variations) before designing transformation logic. Without profiling, subsequent steps like fuzzy matching or ID standardization risk being built on incorrect assumptions about the data.

Answer

Use a simple crosswalk table based on exact name matches to link records

Answer

Load all data from both systems into a staging table, then run a fuzzy matching algorithm to identify duplicates

Answer

Standardize all customer IDs to a common format (e.g., UUIDs) and then merge the tables

Question 2

A company has a dataset with 100 features. The data analyst wants to reduce dimensionality while preserving as much variance as possible. Which technique should be used?

Accepted Answer

PCA (Principal Component Analysis). PCA is the correct choice because it is an unsupervised linear dimensionality reduction technique that projects the data onto orthogonal components ordered by the variance they capture. By selecting the top principal components, the analyst can retain the maximum possible variance in the dataset while reducing the number of features from 100 to a smaller set, directly addressing the goal of preserving variance.

Answer

LDA (Linear Discriminant Analysis)

Answer

Autoencoders

Answer

t-SNE

Question 3

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.

Answer

Requires NoSQL databases for storage

Answer

Often contains natural language text

Answer

Cannot be queried using SQL

Question 4

Which TWO of the following are components of time series data?

Accepted Answer

Trend. Trend (C) is a core component of time series data because it represents the long-term, systematic increase or decrease in the series level over time, which is exactly what decomposition methods (e.g., additive or multiplicative decomposition) isolate. Seasonality (D) is also a core component because it captures regular, calendar-linked repeating patterns (e.g., daily, weekly, monthly, or quarterly cycles) that recur with a fixed period. By contrast, Mean (A), Variance (B), and Median (E) are descriptive statistics or summary measures of a distribution, not structural components of a time series; they describe the data's central tendency or spread rather than the temporal dynamics that decomposition separates.

Answer

Mean

Answer

Variance

Answer

Median

Question 5

In time series analysis, which component represents regular patterns that repeat over fixed periods, such as daily or yearly?

Accepted Answer

Seasonality. Seasonality refers to patterns that repeat at regular intervals.

Answer

Trend

Answer

Cyclical

Answer

Irregular

Question 6

After presenting a data analysis to stakeholders, they ask for additional details on a specific trend. The analyst needs to provide a follow-up report. What is the best approach?

Accepted Answer

Provide a detailed written report with charts and explanations.. The stakeholders requested additional details on a specific trend, so the follow-up should be focused, clear, and self-contained. A detailed written report with charts and explanations directly addresses their request by providing context, visual evidence, and interpretation they can review asynchronously. This approach also creates a reusable artifact that documents the analysis for future reference, which is a best practice in data analytics communication.

Answer

Create a new dashboard with all the data.

Answer

Send the raw data file to the stakeholders.

Answer

Hold another meeting to verbally explain the trend.

Question 7

A data analyst notices that a dataset of customer ages has several missing values. Which method for handling missing data is most appropriate if the data is missing completely at random and the analyst wants to preserve sample size?

Accepted Answer

Impute with the mean age. When data is missing completely at random (MCAR) and the analyst wants to preserve sample size, mean imputation is the standard approach — it replaces missing values with the average of the observed values, retaining all rows and avoiding the bias that deletion would introduce. For MCAR data, mean imputation produces unbiased estimates of the mean (though it reduces variance).

Answer

Forward-fill using the previous value

Answer

Replace missing values with zero

Answer

Delete all rows with missing data

Question 8

A data analyst receives a request from a sales manager for a one-time report on Q4 performance by product line. The report is not expected to be repeated. Which type of report should the analyst create?

Accepted Answer

Ad hoc report. An ad hoc report is a one-time, on-demand report created for a specific question and not scheduled or reused. The scenario explicitly states the report is not expected to be repeated, which is the defining characteristic of ad hoc reporting. Scheduled, operational, and self-service reports all imply recurring or ongoing use.

Answer

Scheduled report

Answer

Operational report

Answer

Self-service report

Question 9

A data analyst creates a line chart showing monthly sales over the past year. The chart uses a y-axis starting at $100,000 instead of zero. What is the most likely misinterpretation a viewer might have?

Accepted Answer

The differences between months are exaggerated, making small changes look large.. Starting the y-axis at $100,000 instead of zero truncates the baseline, which visually exaggerates the relative differences between monthly sales values. This is a common data visualization pitfall that can mislead viewers into perceiving small fluctuations as significant trends, violating the principle of using a zero baseline for bar and line charts to accurately represent proportional change.

Answer

The sales appear to be decreasing when they are actually increasing.

Answer

The chart is correctly scaled, so no misinterpretation occurs.

Answer

The sales appear to be increasing when they are actually decreasing.

Question 10

A stock analyst is analyzing monthly sales data for a retail company and observes a consistent pattern of high sales every December. This pattern is most likely an example of which time series component?

Accepted Answer

Seasonality. A consistent spike every December is a calendar-driven repeating pattern, which is the definition of seasonality in time series analysis. The fixed annual period (12 months) and predictable timing make this a classic seasonal component.

Answer

Irregular

Answer

Cyclical

Answer

Trend

Question 11

In a dataset of employee salaries, the analyst notices one value that is significantly higher than the rest. Using the IQR method, which values are typically considered outliers?

Accepted Answer

Values beyond Q1 - 1.5*IQR or Q3 + 1.5*IQR. Outliers are values less than Q1 - 1.5*IQR or greater than Q3 + 1.5*IQR.

Answer

Values beyond Q1 - 3*IQR or Q3 + 3*IQR

Answer

Values beyond mean ± 2 standard deviations

Answer

Values beyond min and max

Question 12

Match each data analysis tool to its primary function.

Accepted Answer

Excel: Spreadsheet analysis and data manipulation. Excel is for spreadsheets, SQL for databases, Python for programming/ML, and Tableau for visualization. Common confusions include assigning predictive modeling to Excel or dashboard creation to SQL.

Answer

Excel: Predictive modeling and statistical analysis

Answer

SQL: Creating interactive dashboards

Question 13

A data analyst is building a logistic regression model to predict whether a customer will churn (yes/no). Which TWO statements about logistic regression are correct?

Accepted Answer

The output is a probability between 0 and 1.. Option C is correct because logistic regression applies the sigmoid (logistic) function to a linear combination of predictors, producing an output that is a probability bounded between 0 and 1, which is exactly what is needed to model the churn probability (yes/no). Option E is correct because logistic regression assumes linearity on the logit scale: the log-odds of the outcome, ln(p/(1-p)), is modeled as a linear function of the predictor variables. Option A is incorrect because logistic regression is a classification method for binary outcomes, not a time series forecasting technique. Option B is incorrect because the dependent variable is binary/categorical (churn yes/no), not continuous. Option D is incorrect because logistic regression does not require normally distributed errors; that assumption belongs to linear regression, whereas logistic regression uses maximum likelihood estimation with a binomial error distribution.

Answer

It is used only for time series forecasting.

Answer

The dependent variable is continuous.

Answer

It requires normally distributed errors.

Question 14

A marketing analyst wants to segment customers based on purchasing behavior and demographics. The dataset includes continuous variables (spending amount, frequency) and categorical variables (region, gender). The analyst decides to use k-means clustering. What should the analyst do to prepare the data?

Accepted Answer

Standardize continuous variables and one-hot encode categorical variables. K-means clustering relies on Euclidean distance, which is sensitive to the scale of features. Standardizing continuous variables (e.g., spending amount, frequency) ensures they contribute equally to distance calculations, while one-hot encoding categorical variables (e.g., region, gender) converts them into numerical form without implying ordinal relationships, allowing k-means to process mixed data types correctly.

Answer

Use raw data because k-means works with mixed types

Answer

Apply PCA first to reduce dimensionality

Answer

Remove categorical variables entirely

Question 15

A data analyst is preparing a dataset for a machine learning model to predict customer churn. The dataset contains missing values in several columns. The analyst wants to handle missing data in a way that preserves as much information as possible and avoids introducing bias. Which TWO of the following techniques are appropriate? (Choose two.)

Accepted Answer

K-nearest neighbors (KNN) imputation. Multiple imputation by chained equations and KNN imputation are both advanced techniques that preserve data structure and reduce bias. MICE models each variable with missing data using other variables and creates multiple imputations to account for uncertainty. KNN imputation uses similarity between observations to estimate missing values. Both are superior to mean imputation, listwise deletion, or zero replacement, which either discard information or introduce bias.

Answer

Listwise deletion

Answer

Mean imputation for numerical features

Answer

Replacing missing values with zero