DA0-002 Practice Test 3 — 25 Questions

Question 1

A data governance team is defining roles and responsibilities for data management. Which TWO of the following are common data governance roles? (Select TWO).

Accepted Answer

Data steward. Data owner and data steward are standard roles in data governance, responsible for data ownership and stewardship respectively.

Answer

Data scientist

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Database administrator

Answer

Data analyst

Question 2

A data analyst is preparing features for a machine learning model that uses distance-based algorithms (e.g., K-means, KNN). The dataset contains numerical features with different scales: age (0-100), income (20,000-200,000), and credit score (300-850). Which data transformation technique is most appropriate to ensure all features contribute equally to the distance calculations?

Accepted Answer

Min-max normalization. Min-max normalization rescales features to a fixed range (e.g., 0 to 1), making distances computed equally weighted. Standardization is better for algorithms assuming Gaussian distributions.

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Z-score standardization

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One-hot encoding

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Log transformation

Question 3

A data analyst needs to show the relationship between advertising spend (in dollars) and monthly sales revenue (in dollars) for the past 12 months. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is used to visualize the correlation between two continuous variables.

Answer

Line chart

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Bar chart

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Pie chart

Question 4

A data analyst needs to identify the most frequently occurring value in a dataset. Which measure of central tendency should they use?

Accepted Answer

Mode. The mode is the measure of central tendency that identifies the most frequently occurring value in a dataset. Unlike the mean or median, the mode directly counts the frequency of each distinct value and returns the value with the highest count, making it the correct choice for this specific requirement.

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Standard deviation

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Median

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Mean

Question 5

An e-commerce company is acquiring product data from multiple supplier APIs. The APIs return JSON with inconsistent field naming conventions. Which data acquisition technique should be applied?

Accepted Answer

Data mapping and transformation. Data mapping and transformation is the correct technique because the JSON responses from different supplier APIs use inconsistent field naming conventions (e.g., 'product_id' vs. 'ProductID'). This technique defines a schema to map source fields to a standardized target format, ensuring data consistency before loading into the company's system. Without transformation, downstream processes like analytics or inventory management would fail due to mismatched field names.

Answer

Data compression

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Data deduplication

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Data aggregation

Question 6

Which TWO chart types are best suited for visualizing the distribution of a single continuous variable? (Select two.)

Accepted Answer

Box plot. Histogram shows frequency bins; box plot shows quartiles and outliers.

Answer

Scatter plot

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Line chart

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Pie chart

Question 7

A data analyst discovers that the sales data for the current quarter shows a 15% increase in revenue. However, the analyst notes that the data does not include returns from the last week due to a system lag. How should the analyst communicate this uncertainty?

Accepted Answer

Include a caveat that returns from the last week are missing and revenue may be overstated.. When data has limitations, it's important to disclose them, such as missing returns, to avoid misleading conclusions.

Answer

Delay the report until returns are processed.

Answer

Report the 15% increase as final, because the returns are insignificant.

Answer

State that the revenue increase is exactly 15% and provide a confidence interval.

Question 8

Drag and drop the steps to perform a data audit in the correct order.

Accepted Answer

1. Inventory, 2. Quality Assessment, 3. Compliance Check, 4. Documentation, 5. Recommendations. Data audit begins with inventory, quality assessment, compliance check, documentation, and recommendations.

Answer

1. Quality Assessment, 2. Inventory, 3. Compliance Check, 4. Documentation, 5. Recommendations

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1. Inventory, 2. Compliance Check, 3. Quality Assessment, 4. Documentation, 5. Recommendations

Answer

1. Inventory, 2. Quality Assessment, 3. Documentation, 4. Compliance Check, 5. Recommendations

Question 9

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. 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

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Present the data without any explanation and let them draw conclusions

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Remove the demographic detail to avoid controversy

Question 10

An analyst is creating a report to show the relationship between advertising spend and website traffic over the past 12 months. The data has a few outliers due to special promotional events. Which chart type should the analyst use to clearly show the trend while minimizing the impact of outliers?

Accepted Answer

Scatter plot with a trend line. A scatter plot with a trend line (Option D) is the best choice because it plots each data point individually, allowing the analyst to see the overall relationship between advertising spend and website traffic while the trend line (often a linear regression line) smooths out the influence of outliers. This chart type minimizes the visual impact of extreme values by focusing on the central tendency and direction of the data, making it ideal for identifying trends over 12 months despite promotional event spikes.

Answer

Pie chart

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Bar chart

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Heatmap

Question 11

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

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Imputation

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Standardization

Question 12

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

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Collect more data to fill missing values

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

Question 13

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.

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

Answer

Satisfaction and product type are independent.

Question 14

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.

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

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

Question 15

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.

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The new layout significantly increases conversion rate.

Question 16

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.

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Completeness

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Consistency

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Accuracy

Question 17

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

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Values that are negative

Answer

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

Question 18

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

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

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Use accuracy as the evaluation metric

Question 19

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 20

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