DA0-001 Practice Test 27 — 15 Questions

Question 1

A data team is preparing a quarterly business review for the CEO. The report must include both high-level summaries and the ability for the CEO to drill down into specific departments. Which reporting technique best meets this requirement?

Accepted Answer

An interactive dashboard with drill-down capabilities.. An interactive dashboard with drill-down capabilities (Option B) is the correct choice because it directly addresses the requirement for both high-level summaries and the ability to explore specific departments. Dashboards allow the CEO to view aggregated KPIs at a glance and then click through to detailed views for each department, providing a seamless, user-driven exploration experience without switching between separate reports or slides.

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A slide deck with one slide per department.

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A static PDF with a summary page and appendices.

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A data dump in Excel with filters.

Question 2

A data analyst is comparing characteristics of structured and unstructured data. Which TWO of the following are characteristics of structured data? (Choose two.)

Accepted Answer

Data is stored in predefined schemas. Structured data is organized into predefined schemas, such as tables with rows and columns, which enforce a consistent data format and relationships. This rigid structure allows structured data to be easily queried using SQL, as SQL is designed to operate on relational database management systems (RDBMS) that rely on these schemas. Option C is correct because a predefined schema is a defining characteristic of structured data, enabling efficient storage, retrieval, and integrity constraints.

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Data is typically stored as raw text

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Data lacks a fixed format

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Data often requires NoSQL databases for storage

Question 3

A data analytics team has created a report for stakeholders. The report includes complex statistical terms and raw data tables. Stakeholders are confused and ask for clarification. Which of the following should the team do to improve communication?

Accepted Answer

Simplify the report by using clear visualizations and plain language summaries.. Option D is correct because effective data communication requires tailoring the message to the audience. By replacing complex statistical terms and raw data tables with clear visualizations and plain language summaries, the team makes insights accessible to stakeholders who may lack technical expertise, directly addressing the confusion.

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Schedule a follow-up meeting to explain each term individually.

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Provide a glossary of statistical terms and keep the report as is.

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Remove all data and only give conclusions.

Question 4

Refer to the exhibit. A data architect is designing a data dictionary for a relational database. Based on the exhibit, which data concept is being illustrated?

Accepted Answer

Data constraints. The exhibit shows a table definition with column attributes such as NOT NULL, UNIQUE, and PRIMARY KEY, which are data constraints that enforce rules on the data values. Data constraints ensure data integrity by restricting what data can be stored in a column, such as preventing null values or duplicate entries. This directly aligns with the concept of data constraints, making option A correct.

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Data aggregation

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Data normalization

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Data cardinality

Question 5

A data scientist is building a predictive model to forecast monthly sales. The data shows a linear trend with no seasonality. Which regression technique is most appropriate?

Accepted Answer

Linear regression. Linear regression is the most appropriate technique because the data shows a linear trend with no seasonality, making a straight-line model the simplest and most effective fit. It directly models the relationship between the independent variable (e.g., time) and the dependent variable (monthly sales) using a linear equation, minimizing the sum of squared residuals.

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Polynomial regression

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Logistic regression

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Ridge regression

Question 6

A data analyst needs to create a visual that shows the distribution of customer ages across different regions. Which chart type is most appropriate?

Accepted Answer

Stacked bar chart. A stacked bar chart is most appropriate because it allows the analyst to compare the distribution of customer ages (typically grouped into bins) across multiple regions simultaneously. Each bar represents a region, and the segments within the bar show the proportion or count of each age group, making it easy to see both the overall distribution and regional differences.

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Line chart

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Scatter plot

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Pie chart

Question 7

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

Accepted Answer

Correlation does not imply causation. Option C is correct because 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.

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Correlation measures both linear and nonlinear relationships

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Causation can always be inferred from a controlled experiment without randomization

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

Question 8

A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?

Accepted Answer

Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).. The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.

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Add more predictor variables to increase the training R-squared further.

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Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.

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Increase the number of hidden layers in the model to capture more complexity.

Question 9

The exhibit shows an SQL query executed on an 'orders' table that contains 'order_id', 'customer_id', and 'order_date'. What is the purpose of this query?

Accepted Answer

Identify customers who placed more than 5 orders in 2023. The query groups orders by customer_id and filters using a HAVING clause with COUNT(*) > 5, which counts the number of orders per customer. The WHERE clause restricts orders to those placed in 2023, so the result identifies customers who placed more than 5 orders in that year. This matches option D exactly.

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Count total orders per customer regardless of date

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Calculate average order count per customer for 2023

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Find products with more than 5 orders in 2023

Question 10

A data analyst is analyzing survey responses where respondents rated satisfaction on a scale of 1-5. The analyst wants to visualize the distribution of responses. Which chart type is most appropriate?

Accepted Answer

Histogram. A histogram is the most appropriate chart for visualizing the distribution of a single discrete variable, such as satisfaction ratings on a 1-5 scale. It groups the responses into bins (each rating value) and displays the frequency of each bin using bars, clearly showing the shape, central tendency, and spread of the data.

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Box plot

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Scatter plot

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Line chart

Question 11

After building a binary classification model, the data analyst obtains the following confusion matrix: True Positives=80, True Negatives=100, False Positives=20, False Negatives=30. What is the F1 score?

Accepted Answer

0.76. The F1 score is the harmonic mean of precision and recall. Precision = TP/(TP+FP) = 80/(80+20) = 0.80. Recall = TP/(TP+FN) = 80/(80+30) ≈ 0.7273. F1 = 2 * (0.80 * 0.7273) / (0.80 + 0.7273) ≈ 0.7619, which rounds to 0.76. Option A is correct.

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0.73

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0.80

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0.69

Question 12

A data analyst is performing data cleaning. Which THREE steps are part of this process? (Choose three.)

Accepted Answer

Correcting inconsistent data. Correcting inconsistent data (Option A) is a core data cleaning step because it ensures that values follow a consistent format, such as standardizing date formats (e.g., 'MM/DD/YYYY' vs 'DD-MM-YYYY') or fixing capitalization (e.g., 'USA' vs 'usa'). This process directly addresses data quality issues that arise from human entry errors or system differences, making the dataset reliable for analysis.

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Normalization

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Feature engineering

Question 13

A data analyst is building a binary classification model to predict customer churn. The dataset is imbalanced, with only 10% churners. The analyst wants to evaluate model performance with a focus on correctly identifying churners. Which metric is most appropriate?

Accepted Answer

Recall (sensitivity). Recall (sensitivity) is the most appropriate metric because it measures the proportion of actual churners correctly identified by the model. Since the dataset is imbalanced (only 10% churners) and the analyst's focus is on correctly identifying churners, recall directly addresses the cost of missing positive cases (false negatives). Accuracy would be misleading due to class imbalance, while precision and F1-score prioritize different trade-offs.

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F1-score

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Precision

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Accuracy

Question 14

A marketing team wants to segment customers into distinct groups based on purchasing behavior. The data includes numeric features such as frequency, monetary value, and recency. Which unsupervised learning algorithm should be used?

Accepted Answer

K-means clustering. K-means clustering is the correct choice because it is an unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity. For segmenting customers by purchasing behavior (frequency, monetary value, recency), K-means groups customers with similar numeric patterns without requiring labeled outcomes, making it ideal for exploratory segmentation.

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Decision tree

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Linear regression

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Association rules

Question 15

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