DA0-001 Practice Test 36 — 15 Questions

Question 1

A healthcare analytics team is building a dashboard to monitor patient vitals. They receive data from two sources: Source 1 provides 'heart rate' as an integer (beats per minute), and Source 2 provides 'blood pressure' as a ratio (systolic/diastolic, e.g., 120/80). The team wants to create a combined metric called 'cardiac stress index' that uses both heart rate and systolic blood pressure. However, they notice that heart rate data occasionally contains negative values due to sensor errors. The data governance policy requires that all data be valid and meaningful. Which action best addresses the data quality issue while preserving the data types?

Accepted Answer

Remove all records with negative heart rate values as they are invalid. Option D is correct because negative heart rate values are physiologically impossible and violate the data governance policy requiring valid and meaningful data. Removing these records ensures the dashboard only contains accurate, actionable data without altering the original integer data type of heart rate, preserving its numerical integrity for the 'cardiac stress index' calculation.

Answer

Convert heart rate to absolute values (remove the negative sign)

Answer

Keep negative values but set them to NULL to indicate missing data

Answer

Change heart rate from integer to categorical (e.g., low, normal, high) to avoid negative issues

Question 2

A retail company with 500 stores across North America wants to visualize its sales performance. The dataset includes store ID, region (Northeast, Southeast, Midwest, West), product category (Electronics, Clothing, Home Goods), monthly sales (in dollars), and date (from January 2018 to December 2023). The data has missing values for about 5% of store-month combinations, and a few stores have reported sales that are 10 times higher than the average for their region due to grand opening events. The goal is to create a dashboard that shows monthly sales trends for each region and product category, and allows users to identify which categories are driving growth. Which approach should the analyst take?

Accepted Answer

Create a line chart with month on the x-axis, sales on the y-axis, and separate lines for each region and category; check for outliers and consider annotating them. Option A is correct because using a line chart with time on x-axis and grouping by region and category shows trends clearly. Handling outliers separately (e.g., annotation) preserves data integrity. Option B is wrong because combining all categories in a stacked bar makes it hard to see individual trends. Option C is wrong because removing outliers discards valid grand opening data. Option D is wrong because a scatter plot is not appropriate for time series.

Answer

Use a stacked bar chart showing total sales by month, with each bar segmented by region and category

Answer

Create a scatter plot of sales vs. month with dots colored by region

Answer

Remove all stores with outlier sales and then create a line chart of the cleansed data

Question 3

A finance department wants to compare actual expenses against budgeted amounts for each of 10 departments over the past 12 months. They need to show the variance (over/under budget) as well as the trend of expenses over time. The data includes monthly actual and budget figures for each department. The audience is the CFO who needs a quick overview. Which visualization approach should be used?

Accepted Answer

A dual-axis chart with bars for actual and budget by month for each department, and a line for variance. Option B is correct because a dual-axis chart (bars for budget vs actual per month, line for variance) shows both comparison and trend clearly. Option A is wrong because a single stacked bar for all departments obscures monthly trends. Option C is wrong because a table is not a quick-visual overview. Option D is wrong because a waterfall chart is for cumulative changes, not month-by-month trends.

Answer

A waterfall chart showing cumulative variance over the year

Answer

A table with conditional formatting to highlight over-budget cells

Answer

Stacked bar chart with one bar per month, segments showing each department's actual and budget

Question 4

A government agency's data analyst is commissioned to produce a report on public transportation usage trends. The report will be read by policymakers, transit planners, and the general public. The data includes ridership numbers, delay rates, and demographic breakdowns. The analyst needs to ensure the report is accessible and persuasive, especially to non-technical readers. The goal is to advocate for increased funding in underserved areas. The report must be data-driven but also tell a compelling story. What strategy should the analyst prioritize?

Accepted Answer

Create a narrative that highlights the impact of delayed trains on low-income commuters.. Option B is correct because it directly addresses the need to make data accessible and persuasive to non-technical readers by weaving a narrative around a specific, relatable impact (delayed trains on low-income commuters). This approach aligns with the goal of advocating for increased funding in underserved areas, as it humanizes the data and creates a compelling story that policymakers and the public can understand and act upon, without requiring technical expertise.

Answer

Provide raw data in appendices only.

Answer

Use complex statistical analysis to show significance of trends.

Answer

Focus solely on ridership numbers without context.

Question 5

A social media monitoring company collects public tweets using the Twitter API. The API has a tiered access: free tier allows 500,000 tweets per month, and paid tier allows 2 million tweets per month. The company needs to collect 1.5 million tweets per month for analysis. They are on a free tier but have been exceeding the limit, causing account suspension. They need a sustainable solution without significantly increasing costs. What should they do?

Accepted Answer

Use multiple developer accounts to stay within free limits. Using multiple developer accounts to distribute the collection load can allow access to more tweets while staying within each account's free limit. This avoids the cost of upgrading to a paid tier. Reducing collection to 500,000 tweets would cause loss of critical data. Requesting an academic exemption is unlikely because the company is commercial. Upgrading to paid tier increases costs significantly.

Answer

Request an academic research exemption

Answer

Reduce the collection to exactly 500,000 tweets per month by sampling

Answer

Upgrade to the paid tier

Question 6

A retail company is acquiring sales data from 150 stores worldwide. Each store sends daily CSV files via email to a central email address. The data acquisition process is manual: an intern downloads each attachment and copies it into a shared folder. The shared folder is then accessed by an ETL tool that loads data into a data warehouse. Recently, the data warehouse has been missing records for several stores. The intern reports that some emails are not being received or are delayed. The company needs to improve the reliability and timeliness of data acquisition. Which course of action should be taken first?

Accepted Answer

Require stores to upload CSV files directly to a cloud-based storage bucket.. Option B is correct because requiring stores to upload CSV files directly to a cloud-based storage bucket eliminates dependency on email and manual intervention, improving reliability and timeliness. Option A is wrong because it still relies on email as the transport mechanism. Option C is wrong because implementing a web-based API might be too complex and time-consuming as a first step. Option D is wrong because it does not address the root cause of email unreliability.

Answer

Train the intern to check email more frequently and manually verify all attachments.

Answer

Replace the email method with a web-based API that stores push data in real-time.

Answer

Implement an automated email parser that downloads attachments and moves them to the shared folder.

Question 7

A financial analytics firm needs to acquire historical stock market tick data (millions of records per day) from a data vendor. The vendor provides data via FTP in binary format. The firm's existing infrastructure uses on-premise servers with limited storage and processing power. They need to stream the data into a cloud data lake for analysis. However, the binary format is proprietary and requires a licensed decoder. The budget is constrained. Which approach best meets the data acquisition requirements?

Accepted Answer

Purchase a license for the decoder and set up an ETL job to convert and upload. Purchasing the decoder license and setting up an ETL job to convert and upload to the cloud directly addresses the format issue and enables streaming to the data lake. Requesting CSV via email is impractical for millions of records. Negotiating for a JSON API is a good idea but may not be available or cost more than the decoder. Using a third-party service adds recurring costs and may introduce dependency.

Answer

Negotiate with the vendor to provide an API that outputs JSON

Answer

Request the vendor to send data in CSV format via email

Answer

Use a third-party cloud service that already decodes and normalizes the data for a subscription fee

Question 8

A data analyst at a retail company is building a dashboard for store managers to track sales performance. The data comes from three sources: point-of-sale (POS) systems, inventory, and customer loyalty. The POS table contains columns transaction_id, store_id, date, product_id, quantity, and price. The inventory table has product_id, store_id, stock_level, and reorder_point. The loyalty table has customer_id, transaction_id, and points_earned. The analyst creates a star schema with a sales_fact fact table containing all rows from POS, dimension tables for store, product, date, and customer. To calculate average transaction value, the analyst uses the formula SUM(quantity * price) / COUNT(*). Store managers report that the average transaction value appears too low, especially for stores with multiple registers. The analyst realizes that because each product sold in a transaction creates a separate row in sales_fact, a single transaction with multiple items contributes multiple rows. The current calculation divides by the number of rows rather than the number of distinct transactions. Which of the following is the best course of action to correct the average transaction value metric? (Choose one.)

Accepted Answer

Create a calculated field that sums sales per transaction (quantity * price) and then averages across distinct transaction IDs. Option A is correct because it calculates total sales per transaction (summing product-level rows) and then averages across distinct transactions, fixing the over-counting issue. Option B is too vague and does not specify how to aggregate. Option C is not required since the star schema is appropriate. Option D uses median, which does not address the counting issue.

Answer

Use the MEDIAN function instead of AVG

Answer

Aggregate the data at the transaction level before calculating the average

Answer

Use a different data model that denormalizes transaction totals into a new fact table

Question 9

An IT operations team monitors 200 servers. Each server reports CPU utilization (0-100%) every five minutes for the past year. The team wants to visualize the data to identify servers that are consistently over 80% utilization and detect any unusual spikes. They have a large dataset with 100,000+ records per server. The current visualization is a single scatter plot with CPU utilization on the y-axis, time on the x-axis, and each server as a different colored point. The chart is extremely cluttered, with points overlapping and colors indistinguishable. What should the team do to improve the visualization?

Accepted Answer

Use a heatmap showing CPU utilization over time per server, or create small multiple charts (one per server). Option D is correct because using heatmaps or small multiples (trellis charts) reduces clutter for high-density data. Option A is wrong because increasing marker size worsens overlap. Option B is wrong because a single line chart would be even more cluttered with 200 lines. Option C is wrong because trend lines remove detail needed for spike detection.

Answer

Switch to a line chart with each server as a separate line

Answer

Add a trend line to each server's data and remove the individual points

Answer

Increase the size of the data points to make them more visible

Question 10

A retail company has collected data on monthly advertising spend (in thousands of dollars) and corresponding sales (in thousands of dollars) over the past 12 months. The analyst creates a scatter plot to visualize the relationship between advertising spend and sales. The plot shows a cluster of points with a positive trend, but there is one extreme outlier where spend was $100,000 but sales were only $20,000. Upon investigation, the analyst discovers that the outlier is due to a data entry error: the sales figure should have been $200,000. The analyst wants to present the overall trend accurately in a meeting. Which course of action should the analyst take first?

Accepted Answer

Remove the outlier and recreate the scatter plot.. Option A is correct. Since the outlier is confirmed to be a data entry error, it should be removed or corrected before creating a new scatter plot. Option B is incorrect: a line chart would not be appropriate because the data is not a time series (it's just ordered pairs, not sequential over time). Option C is incorrect: adding a trend line to the current plot would still be influenced by the erroneous outlier. Option D is incorrect: creating bins would hide the outlier but not address the data quality issue, and it may obscure the relationship.

Answer

Use a bar chart to show average sales per advertising spend bin.

Answer

Add a trend line using linear regression to the current scatter plot.

Answer

Change the chart type to a line chart.

Question 11

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?

Accepted Answer

Perform data profiling to analyze data distributions, data types, and quality issues in each source. Option C is correct because 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 12

A retail company is merging customer data from three separate systems: an e-commerce platform, a point-of-sale (POS) system, and a loyalty program. The e-commerce platform stores customer names in "FirstName LastName" format, the POS system stores names as "LastName, FirstName", and the loyalty program stores names in separate "first_name" and "last_name" fields. The data analyst needs to create a unified customer master table. After initial merging, there are 20% more records than expected, including duplicates with slight name variations (e.g., "John Smith" vs "John A. Smith"). To ensure accurate consolidation, which data concept should the analyst prioritize applying first?

Accepted Answer

Data standardization. Data standardization is the correct first step because it resolves the inconsistent name formats (e.g., 'FirstName LastName', 'LastName, FirstName', and separate fields) into a single, consistent representation. By applying a standardized format (e.g., 'FirstName LastName'), the analyst can then accurately identify and merge duplicates like 'John Smith' and 'John A. Smith' using fuzzy matching or exact matching on the standardized values. This ensures the unified customer master table has the correct number of records without the 20% inflation caused by formatting variations.

Answer

Data profiling

Answer

Data indexing

Answer

Data encryption

Question 13

A financial analyst is building a model to predict stock price movements. The data is time series with daily prices. The analyst wants to use a regression model but notices that the residuals are autocorrelated. What adjustment should be made?

Accepted Answer

Use a time series model like ARIMA instead. When residuals from a regression model on time series data exhibit autocorrelation, the standard ordinary least squares (OLS) assumptions are violated, leading to biased standard errors and unreliable inference. An ARIMA model is specifically designed to handle autocorrelated time series by explicitly modeling the autoregressive (AR) and moving average (MA) components, making it the correct adjustment to capture the temporal dependencies in stock price movements.

Answer

Use cross-validation to validate the model

Answer

Add more predictors to the regression model

Answer

Transform the data to remove autocorrelation (e.g., differencing)

Question 14

A telecommunications company is experiencing issues with its customer satisfaction survey data. The data is collected from multiple channels: phone, email, and web forms. Each channel uses a different scale for ratings: phone uses 1-10, email uses 1-5, and web uses 1-7. Additionally, some survey responses contain missing values for demographic fields. The data analyst needs to calculate an overall satisfaction score that is comparable across all channels. The company's leadership wants a single metric that minimizes distortion from the different scales. Which approach should the analyst use to standardize the ratings?

Accepted Answer

Normalize each rating to a 0-100 scale using min-max normalization.. Option A is correct because min-max normalization rescales each rating to a common 0-100 range using the formula (x - min) / (max - min) * 100. This preserves the relative distribution of responses within each channel while eliminating the effect of different scale lengths, making the scores directly comparable. It minimizes distortion better than simple multiplication or binary conversion, as it accounts for the full range of each original scale.

Answer

Calculate the average rating separately for each channel and then compare the averages.

Answer

Convert all ratings to a binary metric of satisfied (above midpoint) or unsatisfied.

Answer

Convert all ratings to a 1-10 scale by multiplying email ratings by 2 and web by 1.43.

Question 15

A retail company has merged with another firm and now needs to create a unified customer data warehouse. The existing systems use different data classification methods: System A stores customer income as a categorical range (e.g., '$0-$50k', '$50k-$100k', '$100k+') while System B stores exact income as a decimal number. A data analyst must combine these into a single table. The goal is to perform statistical analysis that includes calculating average income, but the categorical data from System A loses precision. The analyst proposes converting System B's exact values into the same ranges as System A to ensure consistency. However, the data governance team wants to preserve as much detail as possible. Which course of action should the analyst recommend?

Accepted Answer

Impute System A's categorical data with the midpoint of each range to create a continuous numeric field, then combine with System B's exact values. Option C is correct because imputing the midpoint of each income range converts System A's categorical data into a continuous numeric field, allowing it to be combined with System B's exact decimal values. This approach preserves the granularity of System B's data while enabling statistical calculations like average income across the unified dataset, balancing the data governance team's requirement for detail with the need for consistency.

Answer

Store both columns separately and treat them as independent attributes

Answer

Convert System B's exact income to ranges matching System A, then combine

Answer

Use only System B's data and discard System A because it is less precise