DA0-001 Practice Test 4 — 10 Questions

Question 1

Match each database concept to its definition.

Question 2

Match each data sampling method to its description.

Question 3

Match each data security concept to its description.

Question 4

A data analyst needs to visualize the relationship between two continuous variables: advertising spend (in dollars) and monthly sales (in units). Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart for visualizing the relationship between two continuous variables, such as advertising spend (dollars) and monthly sales (units). It displays individual data points on an x-y axis, allowing the analyst to observe correlation, clustering, or outliers between the two numeric fields. This aligns with the DA0-001 objective of selecting visualizations based on data type and analytical goal.

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Pie chart

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Bar chart

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Line chart

Question 5

A data analyst needs to present findings to a non-technical executive audience. Which visualization type is most appropriate to communicate a clear comparison of sales performance across multiple regions for the current quarter?

Accepted Answer

Bar chart. A bar chart is the most appropriate choice because it excels at comparing discrete categories (regions) using a common baseline, making it easy for a non-technical audience to quickly see which regions performed best or worst in the current quarter. The vertical or horizontal bars provide a clear, direct visual comparison of sales performance without requiring interpretation of trends or correlations.

Answer

Scatter plot

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Line chart

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Heatmap

Question 6

A data analyst is merging two datasets from different departments. The analyst notices that the 'CustomerID' field in the first dataset is stored as an integer, while in the second dataset it is stored as a string with leading zeros. Which TWO steps should the analyst take to ensure successful data integration?

Accepted Answer

Use a left join and treat the CustomerID as a string after conversion.. Option C is correct because converting the integer CustomerID to a string ensures both datasets have a compatible data type for the join. This approach preserves the leading zeros in the second dataset, which are semantically significant (e.g., '00123' vs. 123). A left join is appropriate to retain all records from the primary dataset while matching on the converted key.

Answer

Perform the merge directly without transformation since databases handle type conversions automatically.

Answer

Strip all non-numeric characters from the string CustomerID before joining.

Answer

Convert the string CustomerID to an integer by removing leading zeros.

Question 7

Based on the exhibit, what is the most likely cause of the import failure?

Accepted Answer

A data quality issue: the date field contains an invalid date.. Option D is correct because the exhibit shows a date field containing '2023-02-30', which is an invalid date (February never has 30 days). This data quality issue causes the import to fail, as the system likely validates date values against calendar rules before inserting them into the target table. The error is not due to file emptiness, non-numeric characters, or corruption, but specifically a semantic data integrity violation.

Answer

The file is empty or contains only headers.

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The price field includes non-numeric characters that cannot be parsed.

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The source file is corrupted or in an unsupported format.

Question 8

A healthcare analytics team is building a classification model to predict patient readmission within 30 days. The dataset contains 10,000 records with 30 features, including demographics, vital signs, lab results, and medication history. The target variable is imbalanced: 85% no readmission, 15% readmission. The team used logistic regression with default settings and achieved an accuracy of 85%, but the model predicted 'no readmission' for all patients. The lead analyst suspects the model is not learning due to class imbalance. The team has time to implement one corrective action before the next model review. Which action should the team take?

Accepted Answer

Apply SMOTE to oversample the readmission class. Option B is correct because SMOTE (Synthetic Minority Oversampling Technique) directly addresses the class imbalance by generating synthetic samples for the minority class (readmission). This forces the logistic regression model to learn decision boundaries that separate the two classes, rather than defaulting to the majority class prediction. With 85% majority and 15% minority, accuracy alone is misleading, and SMOTE is a proven technique to improve recall for the minority class.

Answer

Remove features with low variance to reduce noise

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Use accuracy as the evaluation metric to monitor improvement

Answer

Switch to a random forest model with default settings

Question 9

A data analyst is creating a dashboard for a retail company. The dashboard should provide insights into sales performance across multiple dimensions. Which TWO chart types are best suited for showing the contribution of each product category to total sales?

Accepted Answer

Pie chart. A pie chart is ideal for showing the contribution of each product category to total sales because it visually represents parts of a whole, making it easy to compare proportions at a glance. The stacked bar chart also effectively shows category contributions within a total, allowing for both absolute and relative comparisons across different time periods or segments. Both chart types directly address the need to visualize proportional breakdowns of a single aggregate metric.

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Scatter plot

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Histogram

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Line chart

Question 10

A data analyst is troubleshooting a report that shows unusually high sales for a specific product. Upon investigation, the analyst finds that the product was returned by several customers, but the returns were recorded in a separate system and not reflected in the sales data. Which data integration concept was likely missing?

Accepted Answer

Data reconciliation. The core issue is that the sales data and returns data are inconsistent because they were not cross-verified. Data reconciliation is the process of comparing datasets to ensure they are in agreement and identifying discrepancies, such as returns not being reflected in sales figures. Without reconciliation, the analyst would not detect that the high sales number is inflated by unrecorded returns.

Answer

ETL (Extract, Transform, Load)

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Data profiling

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Data governance