DA0-001 Practice Test 37 — 15 Questions

Question 1

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.. Option A is correct because 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 2

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 3

A retail company has a dashboard that tracks daily sales across all stores. The dashboard is used by the CEO during monthly board meetings. Recently, the CEO complained that the dashboard is confusing and takes too long to interpret. The data analyst is asked to redesign the dashboard to improve usability. The analyst has access to all sales data. The CEO prefers a high-level overview with the ability to drill down into regions if needed. The analyst must decide on a course of action. What should the analyst do first?

Accepted Answer

Interview the CEO to determine key performance indicators and preferred layout.. The correct first step is to interview the CEO to determine key performance indicators and preferred layout. This ensures the dashboard is designed around the specific high-level metrics the CEO needs for monthly board meetings, with the ability to drill down into regions. Starting with stakeholder requirements prevents wasted effort on irrelevant visualizations or formats.

Answer

Revamp the dashboard with as many visualizations as possible.

Answer

Conduct a survey of all employees to understand their needs.

Answer

Create a static PDF report instead of a dashboard.

Question 4

A healthcare data analyst is presenting findings on patient readmission rates to a group of hospital administrators. The analysis reveals a 15% increase in readmissions over the past quarter for patients aged 65+ from a specific zip code. However, the administrators are skeptical because previous quarterly reports showed no such trend, and they suspect data quality issues. The analyst must communicate this insight effectively while maintaining credibility. Which of the following approaches should the analyst take?

Accepted Answer

Acknowledge the discrepancy and explain possible reasons such as changes in data collection methods or patient population. Option D is correct because it demonstrates the core competency of 'Communicating Data Insights' by acknowledging the discrepancy between the current finding and previous reports, which builds trust with skeptical stakeholders. By explaining possible reasons such as changes in data collection methods or patient population, the analyst maintains credibility and invites collaborative investigation into data quality issues, rather than dismissing concerns or hiding details.

Answer

Emphasize the statistical significance of the finding and ignore previous reports

Answer

Present the data without any explanation and let them draw conclusions

Answer

Remove the demographic detail to avoid controversy

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.. Option B is correct because 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 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. Option B is correct because 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 7

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 8

A financial services company is migrating its customer data from a legacy on-premises relational database to a cloud-based data warehouse. The legacy database uses a denormalized schema with a single table 'customer_master' that contains all customer attributes, including repeated groups for multiple accounts per customer (account1_type, account1_balance, account2_type, account2_balance, etc.). The data warehouse team wants to implement a normalized star schema with separate dimension and fact tables. During the ETL process, the team encounters an error: 'Data truncation: string data right truncation' when loading account_type values into the dim_account table. The account_type column in dim_account is defined as VARCHAR(10), but the source data contains account types like 'SavingsPlus' (11 characters) and 'CheckingPremium' (15 characters). The team must resolve this issue without losing data. Which course of action should the team take?

Accepted Answer

Increase the VARCHAR length of dim_account.account_type to accommodate the longest account type.. Option D is correct because increasing the VARCHAR length of dim_account.account_type to accommodate the longest account type (e.g., VARCHAR(15) for 'CheckingPremium') resolves the data truncation error without data loss. This aligns with the star schema design principle of preserving source data integrity while ensuring the column definition matches the actual data length. The team must avoid truncation or NULL insertion to maintain accurate dimensional attributes for analytics.

Answer

Truncate the account_type values to 10 characters during ETL.

Answer

Change the data type of dim_account.account_type to TEXT.

Answer

Ignore the error and continue loading with NULL values for truncated rows.

Question 9

A large e-commerce company has a data warehouse that stores daily sales transactions. The data analytics team creates a weekly report for the executive team that includes total revenue, number of orders, and average order value. Recently, the report showed a sudden 20% drop in total revenue compared to the previous week. Upon investigation, the data analyst discovers that a major promotional event ended the previous week, and the current week's data includes a new product launch with lower prices. The analyst needs to present this data to the executives in a way that accurately reflects the business situation. The analyst must also provide recommendations for actions. Which of the following is the best course of action?

Accepted Answer

Present the data with annotations explaining the promotion end and new product launch, and recommend analyzing customer retention and cross-selling opportunities.. Option C is correct because it adheres to the principle of transparent data communication: the analyst must present the full dataset with contextual annotations (promotion end, new product launch) to avoid misleading executives. This approach enables informed decision-making, and the recommendation to analyze customer retention and cross-selling opportunities directly addresses the underlying business dynamics—understanding whether the revenue drop is temporary or signals a shift in customer behavior. In a data warehouse context, this ensures that the weekly report reflects the true state of the business rather than a manipulated or incomplete view.

Answer

Remove the new product launch data from the report to show a clearer comparison.

Answer

Present only the data from the new product launch to show growth potential.

Answer

Present the drop as-is and recommend cutting costs to compensate for the revenue decline.

Question 10

A marketing company is building a customer segmentation model. The data team has access to two sources: a CRM database with customer demographics and purchase history, and a third-party data provider that offers social media activity scores. The CRM data is updated daily, while the third-party data is refreshed weekly on Sundays. The analyst needs to create a unified dataset for the model training scheduled for Wednesday morning. The analyst runs a SQL query to join the two tables on CustomerID, but the resulting dataset has far fewer rows than expected. Upon investigation, the analyst finds that many customers in the CRM do not have matching records in the third-party data. Additionally, some customers in the third-party data have multiple entries due to unresolved duplicates. The analyst must produce the most complete dataset possible while maintaining data quality. Which course of action should the analyst take?

Accepted Answer

First deduplicate the third-party data by keeping the most recent record per CustomerID, then perform a LEFT JOIN from CRM to the deduplicated third-party data.. Option A is correct because it first resolves the duplicate issue in the third-party data by keeping the most recent record per CustomerID, ensuring each customer has a single, current social media score. Then, a LEFT JOIN from CRM to the deduplicated third-party data preserves all CRM customers, maximizing completeness while maintaining data quality. This approach aligns with the goal of producing the most complete dataset for model training, as the CRM is the primary source with daily updates.

Answer

Perform an INNER JOIN on CustomerID and then remove duplicates from the result.

Answer

Use only the third-party data because it provides the social media scores needed for segmentation.

Answer

Perform a LEFT JOIN from the third-party data to CRM, then aggregate duplicates by averaging scores.

Question 11

You are a data analyst for a logistics company. The company has a fleet of delivery trucks and tracks performance metrics including delivery time, fuel consumption, and distance traveled. Management wants a dashboard to monitor driver efficiency and identify underperforming drivers. You have access to a dataset with columns: DriverID, Date, RouteID, Distance (miles), FuelUsed (gallons), DeliveryTime (minutes). The dataset contains 10,000 records from the past year. You need to create a visualization that allows management to quickly compare the average fuel efficiency (miles per gallon) of drivers and also see how consistent each driver's efficiency is. Which of the following approaches is the best course of action?

Accepted Answer

Create a box plot grouped by DriverID showing the distribution of MPG for each driver.. A box plot grouped by DriverID is the best choice because it simultaneously shows the central tendency (median MPG) and the spread (interquartile range and outliers) of each driver's fuel efficiency. This allows management to quickly compare average efficiency across drivers while also assessing consistency—drivers with narrow boxes are more consistent, while those with wide boxes or many outliers are erratic. The other options either fail to show distribution (bar chart, line chart) or require manual interpretation of consistency (scatter plot).

Answer

Create a line chart with Date on the x-axis and MPG on the y-axis, with separate lines for each driver.

Answer

Create a scatter plot with Distance on the x-axis and FuelUsed on the y-axis, color-coded by DriverID.

Answer

Create a bar chart showing the average MPG for each driver.

Question 12

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.. Option A is correct because 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 13

A mid-sized e-commerce company stores customer data in a relational database. The database has a table named 'Customers' with columns: CustomerID (primary key), FirstName, LastName, Email, Phone, Address, City, State, ZipCode, and SignUpDate. The company is migrating to a new CRM system that requires a denormalized structure for performance reasons. The new system expects a single table 'CustomerDetails' with columns: CustomerID, FullName (concatenation of first and last name), ContactInfo (JSON object containing email, phone, and address), SignUpDate, and Region (derived from state). The data analyst must design an ETL process to transform the data. During a test run, the analyst notices that some records have missing Phone or Address values. Which of the following is the best approach to handle missing data in the ContactInfo JSON object?

Accepted Answer

Include the missing fields as null in the JSON object.. Option C is correct because representing missing fields as null in the JSON object preserves the data structure and allows downstream systems to explicitly handle null values. This approach maintains data integrity without discarding records or introducing ambiguous placeholder strings that could be misinterpreted as actual data.

Answer

Exclude any record with missing Phone or Address from the migration.

Answer

Set missing values to an empty string in the JSON object.

Answer

Replace missing values with 'N/A' string.

Question 14

A retail company is migrating its on-premises data warehouse to a cloud data warehouse. The current ETL process extracts data from a transactional database (SQL Server) and a web analytics system (JSON logs). The ETL runs nightly and takes 6 hours. The business requires that the new cloud warehouse support real-time reporting with data latency of less than 15 minutes. The data engineer proposes using change data capture (CDC) from the SQL Server database and streaming the JSON logs via a message queue. However, management is concerned about cost and complexity. The engineer must design a solution that meets the latency requirement while minimizing operational overhead. Which approach should the engineer recommend?

Accepted Answer

Implement CDC for the SQL Server database and stream the JSON logs via a message queue to the cloud warehouse. Option C is correct because CDC captures only changed rows from SQL Server, minimizing data volume and enabling near-real-time ingestion, while streaming JSON logs via a message queue (e.g., Apache Kafka or Amazon Kinesis) provides sub-15-minute latency. This combination meets the latency requirement without the overhead of full batch exports or complex virtualization, addressing management's cost and complexity concerns.

Answer

Export the SQL Server data to flat files every 15 minutes and use a cloud storage trigger to load

Answer

Continue with nightly batch loads but increase the frequency to every hour

Answer

Use a data virtualization tool to query the source systems directly without moving data

Question 15

A healthcare organization is building a data warehouse to support population health analytics. The data sources include: (1) an electronic health record (EHR) system with a relational database containing patient demographics, diagnoses, and medications; (2) a claims system that generates CSV files daily; (3) patient-generated health data from mobile apps via a REST API returning JSON. The data engineer needs to design a data acquisition process that runs nightly. The EHR system has a change tracking mechanism that logs changes with timestamps. The claims CSV files are appended daily. The API supports filtering by date. The data warehouse uses a star schema with fact and dimension tables. The engineer must ensure data consistency and minimize load times. Which approach should the engineer take?

Accepted Answer

Extract only new and changed EHR data using change tracking, extract the full claims CSV (since it's append-only), and extract API data filtered by the last extraction date. Option B is correct because it uses incremental extraction for the EHR system (via change tracking) and the API (via date filtering), while performing a full extraction of the claims CSV since it is append-only and small enough to reload nightly. This minimizes load times by avoiding full re-extraction of large, slowly changing datasets, and ensures data consistency by capturing only new or modified records. The star schema in the data warehouse is then populated efficiently from these targeted extracts.

Answer

Perform a full extraction of all data from all sources every night and load directly into the data warehouse

Answer

Use a staging area to land all raw data first, then transform and load

Answer

Extract the EHR data using change tracking, extract the full claims CSV, and extract the API data using a full dump