DA0-002 Practice Test 17 — 15 Questions

Question 1

A data analyst needs to visualize the relationship between two continuous variables, such as sales revenue and advertising spend, to identify potential correlation. Which chart type is most appropriate?

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

Accepted Answer

Scatter plot. A scatter plot is specifically designed to show the relationship between two continuous variables and can reveal correlations, clusters, or outliers. Option A is wrong because pie charts show proportions of a whole, not relationships between two continuous variables. Option B is wrong because line charts show trends over time, not correlation between two variables. Option C is wrong because bar charts compare discrete categories, not continuous relationships.

Answer

Pie chart

Answer

Line chart

Answer

Bar chart

Question 2

A data analyst is creating a dashboard to display monthly sales trends for the past two years. The dataset includes monthly sales figures with seasonal fluctuations. The analyst wants to highlight both the overall trend and the seasonal patterns effectively. Which TWO chart types are most appropriate for this purpose? (Select two.)

Accepted Answer

Line chart. A line chart (A) clearly shows the overall trend over time, while a bar chart (D) allows easy comparison of monthly values, revealing seasonal peaks and troughs. A stacked area chart (B) can show cumulative trends but makes individual monthly comparisons difficult. A scatter plot (C) is for correlations, not time series. A pie chart (E) is for parts of a whole and not suitable for trends.

Answer

Stacked area chart

Answer

Scatter plot

Answer

Pie chart

Question 3

A data analyst is preparing a presentation to share findings with non-technical stakeholders. Which TWO practices should the analyst follow to effectively communicate data insights? (Choose two.)

Accepted Answer

Tailor the message to the audience. The analyst should tailor the message to the audience (Option A) to ensure non-technical stakeholders can understand the insights, and focus on actionable insights (Option E) to provide clear recommendations. Option B is incorrect because raw data tables are overwhelming and not insightful for non-technical audiences. Option C is wrong because technical jargon can confuse stakeholders. Option D is incorrect because including all data anomalies and outliers can distract from key findings.

Answer

Provide raw data tables

Answer

Use technical jargon to demonstrate expertise

Answer

Include all data anomalies and outliers

Question 4

A logistics company receives GPS tracking data from fleet vehicles at 1-second intervals via a cellular network. The data is used to optimize routes and monitor driver behavior. Recently, the data acquisition system has been missing updates for some vehicles when they pass through tunnels or remote areas. The data team notices gaps during these periods. The company needs a solution to ensure near-real-time data continuity. What should they do?

Accepted Answer

Implement a store-and-forward mechanism that buffers data on the vehicle's onboard unit and uploads when connectivity resumes. A store-and-forward mechanism buffers GPS data locally on the vehicle's onboard unit during connectivity loss (e.g., in tunnels) and automatically uploads the backlog when cellular connectivity resumes. This ensures data continuity without requiring real-time transmission, directly addressing the intermittent connectivity issue while maintaining near-real-time updates.

Answer

Use a hybrid approach that combines cellular and Wi-Fi networks

Answer

Increase the frequency of data transmission to every 0.5 seconds

Answer

Switch to a satellite-based GPS system

Question 5

A logistics company tracks delivery times and customer satisfaction scores. The data analyst finds that delivery times have increased over the past quarter, correlating with a drop in satisfaction. The analyst needs to present this to the operations team, which is interested in root cause analysis. The team wants to identify whether the increase is due to specific regions, routes, or time periods. The analyst has access to granular data including timestamps, route IDs, and region codes. The presentation should lead to actionable insights for process improvement. What visualization should the analyst use as the primary chart?

Accepted Answer

A line chart showing delivery times and satisfaction over time.. A line chart with dual axes (or separate panels) can clearly show the trend of delivery times and satisfaction scores over the same time period, directly addressing the operations team's need to identify whether the increase is due to specific time periods. This visualization allows the team to correlate changes in delivery times with satisfaction drops over time, supporting root cause analysis by highlighting temporal patterns. The granular timestamp data makes a time-series line chart the most effective primary chart for revealing trends and potential seasonality.

Answer

A scatter plot of delivery time vs. satisfaction.

Answer

A histogram of delivery times.

Answer

A pie chart showing proportion of late deliveries.

Question 6

A retail company's data analyst developed a dashboard for store managers to monitor daily sales performance. The dashboard includes numerous metrics such as sales by hour, product category, employee, and customer demographics, along with trend lines and forecast graphs. Despite the comprehensive data, store managers are ignoring the dashboard because they find it cluttered and confusing. They prefer to rely on their intuition and verbal updates from shift leads. The analyst needs to improve communication of data insights to ensure the dashboard is used effectively. Which of the following actions should the analyst take FIRST?

Accepted Answer

Simplify the dashboard by focusing on key metrics and using clear visual hierarchy. The core issue is that the dashboard is cluttered and confusing, which directly undermines its usability. Option B addresses this by simplifying the dashboard to focus on key metrics and using a clear visual hierarchy, which is the foundational step in effective data communication. Without first reducing cognitive load, no amount of training or additional data will make the dashboard useful for time-constrained store managers.

Answer

Send the raw data in a spreadsheet instead

Answer

Schedule a training session to explain all metrics

Answer

Add more data points to provide a comprehensive view

Question 7

A manufacturing company has two primary data systems: an ERP system that stores production orders with fields like OrderID, ProductID, Quantity, and ProductionDate, and a CRM system that stores customer sales with fields like SaleID, CustomerID, ProductID, SaleDate, and Amount. The data analyst needs to create a unified view of product performance by joining these tables. However, the ProductID field in the ERP uses a 5-character alphanumeric code (e.g., 'P1234'), while the CRM uses a 6-character code (e.g., 'PR1234'). Additionally, some products have multiple entries due to slight variations in naming. The analyst wants to ensure accurate matching without losing data. Which action should the analyst take first to address the data inconsistency?

Accepted Answer

Create a mapping table that standardizes ProductID formats between ERP and CRM.. Creating a mapping table allows the analyst to explicitly define the relationship between the 5-character ERP ProductID and the 6-character CRM ProductID, ensuring accurate joins without data loss. This approach standardizes the inconsistent formats and handles variations by providing a controlled, deterministic lookup, which is essential for maintaining referential integrity in a unified view.

Answer

Perform data profiling to identify all unique ProductID values and their frequencies.

Answer

Aggregate data by product name and ignore ProductID mismatches.

Answer

Use a fuzzy matching algorithm to join on similar ProductID strings.

Question 8

A healthcare analytics team is building a predictive model to identify patients at high risk of readmission within 30 days of discharge. The dataset includes 50,000 patient records with 200 features, including demographics, vital signs, lab results, and historical admissions. The target variable is binary (readmitted or not). The team uses a logistic regression model and achieves an AUC of 0.72 on the test set. However, the model's calibration is poor: for patients predicted to have a 70% risk, the actual readmission rate is only 40%. The team wants to improve calibration without significantly reducing discrimination (AUC). The data scientist suggests applying Platt scaling. However, the team lead is concerned that Platt scaling may reduce the model's ability to rank patients correctly. Which of the following is the best course of action?

Accepted Answer

Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.. Platt scaling is a post-processing technique that fits a logistic regression model on the predicted probabilities from the original model using a held-out validation set. This recalibrates the probabilities without altering the ranking of patients (the AUC remains unchanged), directly addressing the poor calibration while preserving discrimination. Option C correctly describes this procedure.

Answer

Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.

Answer

Ignore calibration because AUC is the only metric that matters for readmission risk models.

Answer

Switch to a random forest model, which inherently produces better-calibrated probabilities.

Question 9

You are a data analyst at a retail company. The marketing team has asked you to analyze the effectiveness of a recent email campaign. You have data on email open rates, click-through rates, and conversion rates. The campaign targeted 50,000 customers; 20,000 opened the email, 5,000 clicked a link, and 500 made a purchase. The marketing director wants to know the campaign's overall performance and whether the email content was engaging. You prepare a dashboard but notice that the click-through rate (CTR) is 25% and the conversion rate is 10%, which seem high. Upon reviewing the data, you discover that the click-through rate was calculated as (clicks / opens) * 100, and the conversion rate as (purchases / clicks) * 100. The director expects the conversion rate to be calculated as (purchases / opens) * 100. Which action should you take to ensure accurate communication of insights?

Accepted Answer

Present both conversion rates (purchases/opens and purchases/clicks) with clear labels and definitions.. The marketing director expects conversion rate as purchases/opens, but the analyst initially used purchases/clicks. Presenting both metrics with clear labels ensures transparency and avoids misinterpretation, allowing the director to see the full funnel performance. This aligns with best practices in data communication, where stakeholders may have different definitions of a metric.

Answer

Change the conversion rate calculation to purchases/opens without informing the director.

Answer

Recalculate the click-through rate as opens/sent to align with industry standards.

Answer

Present the dashboard as is, because the calculations are technically correct.

Question 10

A data analyst is working with a dataset containing customer information. The dataset includes a column 'full_name' which stores first and last names together. To perform analysis on first names separately, which data concept describes the process of splitting 'full_name' into 'first_name' and 'last_name'?

Accepted Answer

Data normalization. Data normalization is the process of organizing data to reduce redundancy and improve integrity, which includes splitting composite attributes like 'full_name' into atomic values ('first_name', 'last_name'). This aligns with the first normal form (1NF) principle in database design, where each column should contain indivisible values. The data analyst is decomposing a single field into multiple, more granular fields to enable separate analysis.

Answer

Data deduplication

Answer

Data summarization

Answer

Data aggregation

Question 11

A data team is creating a dashboard to monitor real-time sales. What design principle is critical?

Accepted Answer

Use auto-refresh and clear alert thresholds. For a real-time sales dashboard, the critical design principle is to ensure data freshness and immediate actionability. Option B is correct because auto-refresh keeps the dashboard current without manual intervention, and clear alert thresholds enable the team to instantly identify when sales metrics deviate from expected ranges, which is essential for real-time monitoring.

Answer

Provide downloadable raw data

Answer

Include all historical data

Answer

Minimize use of color

Question 12

A data analyst is examining sales data for a retail chain and notices that the mean monthly sales is $50,000 while the median is $35,000. Which of the following best describes the distribution of the sales data?

Accepted Answer

The distribution is right-skewed.. When the mean is greater than the median, the distribution is right-skewed (positively skewed) because the mean is pulled towards the higher values by outliers or a long right tail.

Answer

The distribution is bimodal.

Answer

The distribution is left-skewed.

Answer

The distribution is symmetrical.

Question 13

A data analyst is preparing a dataset for analysis and needs to ensure data quality. Which TWO of the following are dimensions of data quality?

Accepted Answer

Consistency. Accuracy and consistency are recognized dimensions of data quality in CompTIA Data+.

Answer

Volume

Answer

Velocity

Answer

Variety

Question 14

In a table 'sales_team' with columns 'salesperson', 'quarter', and 'revenue', an analyst wants to assign a rank to each salesperson within their quarter based on revenue, with the highest revenue getting rank 1. However, if two salespeople have the same revenue, they should receive the same rank, and the next rank should be the next consecutive integer (no gaps). Which window function should be used?

Accepted Answer

DENSE_RANK(). DENSE_RANK() assigns ranks with no gaps for ties, whereas RANK() leaves gaps.

Answer

RANK()

Answer

NTILE(4)

Answer

ROW_NUMBER()

Question 15

A data analyst is performing K-means clustering on customer data. Which THREE of the following are steps in the K-means algorithm?

Accepted Answer

Initialize k centroids randomly.. K-means involves initializing centroids, assigning points to nearest centroid, and updating centroids as the mean of assigned points.

Answer

Perform eigenvalue decomposition.

Answer

Calculate the correlation matrix.