DA0-002 Practice Test 7 — 10 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