DA0-001 Practice Questions 2 — 50 Questions

Question 1

A data engineer is configuring access to a data lake in Amazon S3. What does the JSON policy shown allow?

Accepted Answer

Read objects from the bucket. The JSON policy shown grants the `s3:GetObject` action, which allows reading objects from the specified S3 bucket. This is a standard AWS IAM policy that explicitly permits the `GetObject` API call, enabling the data engineer to retrieve objects from the data lake.

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Change user permissions

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Delete objects from the bucket

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Write objects to the bucket

Question 2

A data analyst needs to combine two datasets: one contains customer information (customer_id, name, address) and the other contains order information (order_id, customer_id, order_date). The analyst wants to include all customers, even those who have not placed orders. Which type of join should be used?

Accepted Answer

LEFT JOIN. A LEFT JOIN returns all rows from the left table (customers) and the matching rows from the right table (orders). If a customer has no orders, the order columns will contain NULLs. This satisfies the requirement to include all customers, even those without orders.

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FULL OUTER JOIN

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INNER JOIN

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RIGHT JOIN

Question 3

An organization needs to acquire data from a third-party vendor. The data will be used for regulatory reporting. Which of the following should be the primary consideration before acquiring the data?

Accepted Answer

Legal and compliance requirements. When acquiring data for regulatory reporting, legal and compliance requirements must be the primary consideration because the data must adhere to specific laws (e.g., GDPR, HIPAA, SOX) and industry regulations. Failing to ensure compliance can result in legal penalties, fines, or rejection of the report by regulatory bodies. This overrides technical or cost concerns, as non-compliant data is unusable for its intended purpose.

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Volume of data

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Data format

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Cost of the data

Question 4

A data analyst needs to extract data from an API that returns JSON. The analyst wants to convert the JSON output into a tabular format for analysis. Which function in a scripting language is commonly used for this purpose?

Accepted Answer

json_normalize(). Option D is correct because `json_normalize()` is a function in the pandas library specifically designed to flatten semi-structured JSON data (including nested lists and dictionaries) into a tabular DataFrame. This makes it the ideal tool for converting API responses with complex nesting into rows and columns for analysis, unlike simpler JSON parsing functions.

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json.loads()

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to_csv()

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read_json()

Question 5

A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positives correctly identified. With only 10% of actual churners detected, the model has a recall of 0.1, which directly highlights the failure to capture churners despite high overall accuracy.

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Accuracy

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F1 score

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Precision

Question 6

What is the primary purpose of the HAVING clause in the query shown?

Accepted Answer

Filter groups after aggregation. The HAVING clause is used to filter groups after the GROUP BY clause has aggregated the data. In SQL, WHERE filters individual rows before aggregation, while HAVING applies conditions to the results of aggregate functions like SUM, COUNT, or AVG. Option D is correct because the query uses HAVING to restrict which grouped results appear in the final output.

Answer

Sort the results in descending order

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Join two tables

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Filter rows before grouping

Question 7

A data analyst sees this error in the ETL logs. What is the most likely cause?

Accepted Answer

The materialized view log was updated after the last refresh. The error indicates that the materialized view's underlying data has changed since its last refresh, specifically because the materialized view log was updated. Materialized views rely on logs to track changes for fast refreshes; if the log is updated after the last refresh, the view's snapshot becomes stale and cannot be incrementally refreshed without a complete refresh. This is a common cause of refresh failures in Oracle databases.

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The source table was dropped

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The analyst does not have permission to refresh the view

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There is a network connection timeout

Question 8

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 9

Which TWO of the following are common assumptions of linear regression?

Accepted Answer

Linearity of the relationship. Linear regression assumes that the relationship between the independent and dependent variables is linear (option C). This means the model expects that a unit change in the predictor results in a constant change in the outcome, which is the core assumption for ordinary least squares (OLS) estimation to produce unbiased coefficients.

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Independence of observations

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No multicollinearity

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Normality of the dependent variable

Question 10

A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?

Accepted Answer

Mode imputation. Mode imputation is the most appropriate method for a categorical variable because it replaces missing values with the most frequently occurring category, preserving the distribution of the data. Unlike mean or median imputation, which are designed for numerical data, mode imputation maintains the categorical nature of the variable and avoids introducing invalid values. This approach is simple and effective when missing data is random and the category is well-represented.

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Drop rows with missing values

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Mean imputation

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Median imputation

Question 11

A data analyst needs to combine two datasets that have the same columns but different rows. Which operation should they use?

Accepted Answer

Append. Option B (Append) is correct because appending is the standard operation for combining two datasets with identical columns but different rows, stacking the rows from one dataset onto the other. In tools like SQL, this is achieved with the UNION or UNION ALL operator, and in Python pandas, it is done via the `append()` method or `pd.concat()` with axis=0. This operation preserves the column structure while extending the row count.

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Concatenate

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Merge

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Aggregate

Question 12

A data scientist trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?

Accepted Answer

Apply Ridge regularization. Ridge regularization (L2) reduces variance by adding a penalty term proportional to the square of the coefficients, which shrinks them toward zero without eliminating them. This directly addresses high variance (overfitting) by constraining the model's complexity, while low bias indicates the model fits the training data well. The regularization parameter λ controls the trade-off between bias and variance.

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Increase polynomial features

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Use a smaller training set

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Remove correlated features

Question 13

A retail company wants to predict future sales based on historical data. Which modeling approach is most appropriate if the data shows a clear seasonal pattern?

Accepted Answer

Time series analysis. Time series analysis is specifically designed to model data points indexed in time order, making it ideal for capturing and forecasting seasonal patterns. Unlike regression models, it accounts for autocorrelation, trends, and seasonality components, which are critical for accurate sales prediction from historical data.

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Linear regression

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K-means clustering

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Logistic regression

Question 14

A data analyst is performing a hypothesis test with a significance level of 0.05. The p-value obtained is 0.03. What should the analyst conclude?

Accepted Answer

Reject the null hypothesis. Since the p-value (0.03) is less than the significance level (0.05), the result is statistically significant. This means the observed data provides sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. The analyst should conclude that there is a statistically significant effect or difference.

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Fail to reject the null hypothesis

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Accept the null hypothesis

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The result is practically significant

Question 15

Which TWO visualization types are suitable for showing the distribution of a single continuous variable?

Accepted Answer

Box plot. A box plot is correct because it graphically depicts the distribution of a single continuous variable through its five-number summary (minimum, first quartile, median, third quartile, maximum), clearly showing spread, central tendency, and outliers. A histogram is correct because it bins the continuous variable into intervals and displays the frequency of data points within each bin, providing a direct view of the underlying probability distribution.

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Line chart

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Scatter plot

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Pie chart

Question 16

An analyst builds a dashboard with a gauge showing 'Current Inventory Level' as a percentage. Stakeholders find the gauge misleading because it always shows near 100% even when inventory is low. What is the most likely issue?

Accepted Answer

The maximum value of the gauge is set too high. When a gauge always shows near 100% despite low inventory, the most likely cause is that the maximum value of the gauge is set too high. For example, if the gauge's maximum is configured as 10,000 units but the actual inventory never exceeds 100 units, even a small absolute value will appear as a high percentage. This scaling mismatch makes the gauge misleading because it fails to reflect the true relative state of the inventory.

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The gauge updates too slowly

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The gauge uses green for all values

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The gauge needle is too small

Question 17

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.

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Pie chart

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Bar chart

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Heatmap

Question 18

Which THREE of the following are appropriate methods to handle outliers in a dataset?

Accepted Answer

Transforming the data using log transformation. Option A is correct because log transformation compresses the scale of data, reducing the impact of extreme values and making the distribution more symmetric. This is a standard technique for handling skewed data where outliers are present, as it preserves the relative order of observations while mitigating outlier influence.

Answer

Binning continuous variables

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Imputing outliers with the mean

Question 19

A data analyst is creating a dashboard to monitor server CPU utilization over the past 24 hours. Which chart type is most appropriate for showing the trend of CPU usage over time?

Accepted Answer

Line chart. A line chart is the most appropriate choice for displaying CPU utilization trends over a continuous 24-hour period because it connects data points in chronological order, making it easy to observe peaks, valleys, and overall patterns. The x-axis represents time (hours), and the y-axis represents CPU usage percentage, allowing the analyst to quickly identify when utilization spikes or drops. This aligns with the DA0-001 objective of selecting the correct visualization for time-series data.

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Scatter plot

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Pie chart

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Bar chart

Question 20

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.

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Bar chart

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Line chart

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Pie chart

DA0-001 Practice Questions 2 — 50 Questions

Exhibit

DA0-001 Practice Questions 2 — 50 Questions

Exhibit