DA0-002 Practice Test 3 — 30 Questions

Question 1

A data analyst is preparing a dataset for analysis and needs to handle outliers. Which TWO of the following are common methods for treating outliers?

Accepted Answer

Removal. Capping (winsorizing) limits extreme values, and removal simply deletes outlier rows. Transformation (e.g., log) can also reduce impact but is not listed here; normalization and imputation are not primary outlier treatments.

Answer

Normalization

Answer

Imputation

Answer

Standardization

Question 2

You are a data analyst at a logistics company. The operations manager wants to reduce delivery delays. You have historical data including order date, delivery date, distance, weather conditions, and driver ID. Initial analysis shows that the average delivery time has increased over the past six months. You suspect that weather is a contributing factor, but you need to confirm. The company also wants to build a model to predict delivery times to better manage customer expectations. The data contains missing values for weather conditions in about 10% of records, and some driver IDs are incorrect. You have limited time and resources. What should you do first?

Accepted Answer

Start by cleaning the data: correct driver IDs and decide how to handle missing weather data, then perform exploratory data analysis. Data cleaning and exploratory data analysis (EDA) are foundational steps before any modeling or time series work. With missing weather data (10%) and incorrect driver IDs, proceeding without cleaning would introduce bias and errors. EDA will reveal patterns, correlations, and data quality issues, enabling informed decisions on imputation and feature engineering for the predictive model.

Answer

Immediately focus on time series analysis to look for patterns

Answer

Collect more data to fill missing values

Answer

Build a predictive model using all available data after imputing missing weather data

Question 3

A data analyst is performing a chi-square test of independence on a contingency table of customer satisfaction (satisfied vs. dissatisfied) and product type (A, B, C). The test yields a p-value of 0.04 with α = 0.05. What is the correct conclusion?

Accepted Answer

There is a significant association between satisfaction and product type.. Since p-value < α, we reject the null hypothesis of independence, meaning there is a significant association between satisfaction and product type.

Answer

There is no evidence of an association between satisfaction and product type.

Answer

The test is invalid because the expected counts are too low.

Answer

Satisfaction and product type are independent.

Question 4

A data analyst runs an A/B test on a new website layout. The test yields a p-value of 0.04 with the null hypothesis being no difference in conversion rates. The significance threshold is α=0.05. Which of the following is the correct conclusion?

Accepted Answer

Reject the null hypothesis; there is a statistically significant difference in conversion rates.. Since p-value (0.04) < α (0.05), we reject the null hypothesis and conclude there is a statistically significant difference. However, statistical significance does not guarantee practical significance.

Answer

The result is not significant; accept the alternative hypothesis.

Answer

Reject the null hypothesis; the new layout is proven to increase conversions.

Answer

Fail to reject the null hypothesis; there is no evidence of a difference.

Question 5

A company runs an A/B test to compare a new website layout (treatment) against the current layout (control). The conversion rate for the control is 5% and for the treatment is 5.5%. The p-value is 0.06 at α=0.05. Which THREE of the following conclusions are valid?

Accepted Answer

There is not enough evidence to conclude that the new layout is better.. The p-value > α, so fail to reject the null; the difference is not statistically significant. However, the observed lift is 0.5% (absolute). Sample size might be insufficient; statistical power could be low.

Answer

The test has sufficient power to detect the observed effect.

Answer

The new layout significantly increases conversion rate.

Question 6

Which data quality dimension is most concerned with whether data values fall within a defined domain or acceptable range?

Accepted Answer

Validity. Validity refers to whether data values conform to defined rules or constraints.

Answer

Completeness

Answer

Consistency

Answer

Accuracy

Question 7

A data analyst needs to identify outliers in a dataset. Which of the following is a common method based on the interquartile range (IQR)?

Accepted Answer

Values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. A common rule is to consider any data point below Q1 - 1.5*IQR or above Q3 + 1.5*IQR as an outlier.

Answer

Values more than 2 standard deviations from the mean

Answer

Values that are negative

Answer

Values below the 5th percentile or above the 95th percentile

Question 8

A healthcare analytics team is analyzing patient readmission rates. They have a dataset with thousands of records including patient age, diagnosis, length of stay, number of prior admissions, and discharge date. The goal is to identify key factors influencing readmission and create a model to predict high-risk patients. The data is imbalanced: only 5% of patients are readmitted within 30 days. The team plans to use logistic regression. What is the most appropriate approach?

Accepted Answer

Apply oversampling techniques like SMOTE to the training set. With imbalanced data, logistic regression can be biased toward the majority class. Oversampling the minority class (e.g., SMOTE) helps the model learn patterns for readmission. Using accuracy as a metric would be misleading. Removing majority samples discards valuable data. Using data as-is often fails to predict the minority class.

Answer

Use the dataset as is because logistic regression handles imbalance

Answer

Remove most of the non-readmitted patients to balance the dataset

Answer

Use accuracy as the evaluation metric

Question 9

A research firm is acquiring data from public government databases via API. The API rate limits at 100 requests per minute. They need to download 10,000 records, but each request returns a maximum of 100 records. What is the most efficient approach to ensure complete acquisition without being blocked?

Accepted Answer

Use a retry logic with exponential backoff and pagination. Pagination with retry logic using exponential backoff allows the firm to send requests in a controlled manner, respecting the rate limit and handling potential failures. Sending all requests in parallel would likely exceed the rate limit and cause blocking. Downloading one record per second is too slow. Requesting a data dump via email is inefficient and may not be supported.

Answer

Request a data dump from the government via email

Answer

Download one record per second

Answer

Send all requests simultaneously in parallel

Question 10

An e-commerce company wants to analyze sales performance across product categories. The dataset includes transaction amounts and a column 'category' with values (Electronics, Clothing, Home). The analyst decides to use stratified sampling to ensure proportional representation. Which THREE steps are required to implement this? (Select THREE).

Accepted Answer

Calculate the proportion of each category in the population. Stratified sampling requires dividing the population into strata (categories), then randomly sampling from each stratum in proportion to its size. Combining strata or simple random sampling without stratification would not achieve proportional representation.

Answer

Select every 10th transaction from the entire dataset

Answer

Combine all categories into a single group and perform simple random sampling

Question 11

A data analyst creates a dashboard for executives to monitor quarterly sales. Which best practice ensures the dashboard is effective?

Accepted Answer

Place the most important metric in the top-left corner with simple charts.. Placing the most important metric in the top-left corner leverages the natural reading pattern (left-to-right, top-to-bottom) to immediately draw the executive's attention to the key insight. Using simple charts (e.g., bar or line charts) reduces cognitive load, enabling rapid comprehension of quarterly sales trends without distracting details. This aligns with dashboard design principles that prioritize clarity and actionability over data density.

Answer

Use a dark background with bright colors for contrast.

Answer

Include raw data tables for detailed analysis.

Answer

Use as many charts as possible to show all data.

Question 12

A retail company has a data warehouse that integrates sales data from multiple sources including online transactions, in-store POS, and third-party marketplaces. The data team recently updated the ETL pipeline to add a new data source: mobile app purchases. After the update, the daily sales report shows a 15% increase in total sales compared to the previous day, which is unexpected because the mobile app is new and only contributed 2% of sales in tests. The report is created by a SQL script that aggregates sales by date and runs every morning. The data team needs to identify the cause of the discrepancy. Which of the following should the team do first?

Accepted Answer

Verify that the date filter in the SQL script is correct and not including future dates.. The correct first step because an unexpected 15% increase in total sales, especially when the new mobile app source accounts for only 2% in tests, suggests a data integrity issue rather than a real sales surge. The most common cause is a date filter error in the SQL script, such as including future dates or applying an incorrect date range, which would inflate the daily total. Verifying the date filter is a quick, low-effort check that can confirm the data source before investigating more complex issues like double-counting (Option D). Options B and C are premature: comparing raw transaction counts is a secondary step after confirming the date filter, and assuming the increase is real (C) ignores the most likely data problem.

Answer

Compare the raw transaction counts from each source for that day.

Answer

Assume the increase is due to the mobile app and update the forecast.

Answer

Check if the ETL pipeline is double-counting transactions from the mobile app source.

Question 13

A data analyst is cleaning text data in a SQL database. Which THREE string functions are commonly used to standardize and clean text? (Choose three.)

Accepted Answer

REPLACE. TRIM removes leading/trailing spaces, UPPER/LOWER standardize case, REPLACE substitutes substrings. CONCAT concatenates strings, LENGTH returns length, SUBSTRING extracts part of string.

Answer

LENGTH

Answer

CONCAT

Question 14

A data analyst is using pandas to clean a DataFrame that contains missing values in the 'age' and 'income' columns. Which THREE pandas methods are appropriate for handling missing data? (Select THREE).

Accepted Answer

dropna(). Common pandas methods for missing data include dropna (remove rows with NaN), fillna (replace NaN with a value), and apply with a custom function. Merge is for combining DataFrames; pivot_table is for reshaping.

Answer

pivot_table()

Answer

merge()

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. 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

Question 16

A data analyst creates a scatter plot showing the relationship between advertising spend and revenue. The plot shows a strong positive correlation. Which of the following should the analyst include in the report to ensure accurate communication?

Accepted Answer

Include a note that correlation does not imply causation.. Correlation does not imply causation. It is important for the analyst to include this caveat to prevent misinterpretation of the relationship. Option B is incorrect because a bar chart is not appropriate for showing the relationship between two continuous variables. Option C is incorrect because removing outliers without justification can distort the analysis; outliers should be handled transparently. Option D is incorrect because while a trend line could be added, it is not mandatory and the question specifically asks for ensuring accurate communication, which is primarily about the correlation-causation warning.

Answer

Replace the scatter plot with a bar chart.

Answer

Remove any outliers from the plot.

Answer

Add a trend line to the scatter plot.

Question 17

An analyst is presenting findings to stakeholders. Which TWO techniques effectively communicate uncertainty in data? (Choose TWO.)

Accepted Answer

Include confidence intervals.. Confidence intervals provide a range of values that likely contain the true population parameter, offering a clear measure of uncertainty around a point estimate. This technique is fundamental in inferential statistics and directly communicates the precision of the data, helping stakeholders understand the reliability of the findings.

Answer

Use only point estimates.

Answer

Remove all outliers from the data.

Answer

State exact numbers without ranges.

Question 18

A company uses a NoSQL document database to store product catalogs. Each product document includes fields like product_id, name, category, and price. The operations team frequently queries by product_id and by category. Which type of NoSQL database is being used, and what should be created to optimize queries by category?

Accepted Answer

Document database; create an index on category. The question explicitly states a document database is used, and the operations team frequently queries by category. In a document database like MongoDB, creating an index on the category field optimizes these queries by allowing the database to quickly locate documents without scanning every document in the collection. This is the standard approach for improving query performance on non-primary-key fields in document stores.

Answer

Key-value store; create a secondary index on category

Answer

Graph database; create a relationship between products

Answer

Wide-column store; create a column family for category

Question 19

A data analyst needs to present the correlation between advertising spend and website traffic. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart type for visualizing the correlation between two continuous variables, such as advertising spend and website traffic. It displays individual data points on a Cartesian plane, allowing the analyst to assess the strength, direction, and form of the relationship (e.g., linear, non-linear, or no correlation). This aligns with the DA0-001 objective of selecting the correct visualization for bivariate analysis.

Answer

Bar chart

Answer

Line chart

Answer

Pie chart

Question 20

A data engineer is designing a data warehouse for a multinational corporation. The company has sales data from different regions with varying currencies and date formats. To ensure consistency, which data concept should be applied to standardize the data before loading into the warehouse?

Accepted Answer

Data transformation. Data transformation is the correct concept because it involves converting data from source formats (e.g., different currencies and date formats) into a consistent, standardized format before loading into the data warehouse. This process includes applying conversion rules, such as using ISO 8601 for dates and a single base currency (e.g., USD) with exchange rate tables, ensuring uniformity across all regional data. Without transformation, the warehouse would contain incompatible data types, breaking referential integrity and analytical queries.

Answer

Data cleansing

Answer

Data profiling

Answer

Data masking