DA0-002 Practice Test 50 — 15 Questions

Question 1

A data analyst is designing a dashboard for a sales team. Which TWO of the following are key performance indicators (KPIs) rather than operational metrics?

Accepted Answer

Customer retention rate. Customer retention rate (B) is a key performance indicator (KPI) because it measures the long-term health of customer relationships and directly reflects strategic business goals, such as loyalty and recurring revenue. It is a high-level metric that tracks performance against strategic objectives, unlike operational metrics which focus on day-to-day activities.

Answer

Number of emails sent to prospects

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Number of sales calls made per day

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Average time to close a deal

Question 2

In a time series analysis, a retail analyst observes consistent peaks in sales every December and troughs every February. This pattern repeats annually. Which component of time series does this represent?

Accepted Answer

Seasonality. Seasonality refers to regular patterns that repeat over fixed periods, such as months or quarters.

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Irregular

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Trend

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Cyclical

Question 3

Which THREE of the following are examples of descriptive statistics? (Select THREE.)

Accepted Answer

Correlation coefficient. The correlation coefficient (A) is a descriptive statistic because it quantifies the strength and direction of a linear relationship between two variables using a single number (ranging from -1 to +1) without making inferences about a larger population. It simply describes the observed data's association, which is the core function of descriptive statistics.

Answer

P-value

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Regression coefficient

Question 4

A data analyst notices that the sales numbers in a report differ from the numbers in the finance department's spreadsheet. This discrepancy is most likely due to a lack of:

Accepted Answer

Single version of truth. A single version of truth means all departments use the same centralized data, avoiding inconsistencies.

Answer

Row-level security

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Data lineage

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Data dictionary

Question 5

A data analyst is cleaning a dataset with missing values. Which TWO of the following are acceptable methods for handling missing numerical data?

Accepted Answer

Mean imputation. Mean imputation and deletion (listwise) are common methods. Mode imputation is for categorical, and forward-fill is for time series; min-max is normalisation.

Answer

Min-max normalisation

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Forward-fill

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Mode imputation

Question 6

An organization is implementing a data warehouse to support business intelligence reporting. The data warehouse must ensure that transactions are processed reliably. Which property guarantees that each transaction is treated as a single, indivisible unit?

Accepted Answer

Atomicity. Atomicity (option C) is the correct property because it ensures that a transaction is treated as a single, indivisible unit of work. In the context of a data warehouse, this means that either all operations within the transaction are committed successfully, or none are applied, preventing partial updates that could corrupt the data. This is a core component of the ACID (Atomicity, Consistency, Isolation, Durability) model, which is fundamental to reliable transaction processing in databases like SQL Server, Oracle, or PostgreSQL.

Answer

Consistency

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Isolation

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Durability

Question 7

The exhibit shows a SQL query result intended for a bar chart of revenue by region. However, the chart shows only the top 10 regions, but the query returns all regions. What is the most likely cause?

Accepted Answer

The visualization tool has a default limit on the number of categories displayed. The SQL query itself returns all regions because there is no LIMIT clause. However, the visualization tool has a built-in default limit on the number of categories displayed, such as top 10, which truncates the data in the chart. This is the most likely cause, making Option B correct. Options A, C, and D are incorrect: the GROUP BY clause is correctly specified, a WHERE clause is not required to get all regions, and the ORDER BY clause may be applied for sorting but the tool's limit overrides the full result set.

Answer

The GROUP BY clause is incorrect

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The query is missing a WHERE clause

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The ORDER BY clause is ignored in the chart

Question 8

Refer to the exhibit. What is the impact of the validation result?

Accepted Answer

The staging table is missing the 'age' column, which may cause query errors. The validation result shows that the staging table has a different schema than the target table, specifically missing the 'age' column. This mismatch will cause query errors when attempting to insert or query data that references the 'age' column, as the staging table lacks the required column definition. The validation result explicitly flags this schema discrepancy, making option A correct.

Answer

The validation passed successfully

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Only duplicates were found

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The row counts match, so data is complete

Question 9

Which TWO of the following are valid techniques for validating the performance of a predictive model?

Accepted Answer

Train-test split. The train-test split (Option C) is a fundamental technique for validating predictive model performance by partitioning the dataset into separate training and testing subsets, ensuring the model is evaluated on unseen data to gauge generalization. This method directly addresses overfitting and provides an unbiased estimate of model accuracy, making it a standard practice in supervised learning workflows.

Answer

Bootstrapping

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Feature scaling

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Increasing training data

Question 10

A company's sales dashboard shows that the current month's revenue is $1.2M, which is 10% below the target of $1.33M. The analyst wants to highlight this shortfall. Which method of data presentation is most effective?

Accepted Answer

Use a bullet chart showing actual vs. target.. A bullet chart is the most effective method because it is specifically designed to show performance against a target, combining a bar for the actual value ($1.2M) with a reference line or marker for the target ($1.33M). This allows the analyst to immediately visualize the 10% shortfall in a compact, high-density format without needing to compare separate numbers or interpret a trend. It directly addresses the goal of highlighting the variance, which is a core principle in data presentation for performance dashboards.

Answer

Show a trend line of the last 12 months.

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Provide a table of all monthly targets and actuals.

Answer

Display the actual revenue only.

Question 11

A data governance team is drafting a policy for handling personally identifiable information (PII). According to data governance best practices, which document should define the classification levels and handling procedures?

Accepted Answer

Data classification policy. The data classification policy is the authoritative document that defines classification levels (e.g., public, internal, confidential, restricted) and specifies handling procedures for each category, including PII. This aligns with data governance best practices, as it establishes the rules for labeling, storing, transmitting, and disposing of sensitive data. A data dictionary describes metadata and schema, not classification rules.

Answer

Data dictionary

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Data quality report

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Data flow diagram

Question 12

A data analyst creates a dashboard for operational metrics. The operations team reports that the dashboard is confusing because it shows too many metrics on one screen. Which design principle should the analyst apply?

Accepted Answer

Apply progressive disclosure. Progressive disclosure is a design principle that presents only the most critical information initially, with the option to reveal additional details as needed. This directly addresses the operations team's complaint of too many metrics on one screen by reducing cognitive load and allowing users to drill down into specific metrics when required. In dashboard design, this is often implemented through expandable sections, hover-over tooltips, or click-through layers.

Answer

Increase white space

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Use a single chart type

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Add more filters

Question 13

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 low. When a gauge always shows near 100% despite low inventory, the most likely cause is that the maximum value of the gauge is configured too low. This scaling mismatch makes even small inventory values appear as a high percentage, making the gauge misleading. Therefore, option A is correct: the maximum value is set too low, causing the percentage to be artificially high.

Answer

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 14

Refer to the exhibit. If the date column is stored as a string in 'MM/DD/YYYY' format, what will be the result?

Accepted Answer

Incorrect results because string comparison is lexicographic.. When dates are stored as strings in 'MM/DD/YYYY' format, string comparison is lexicographic (character-by-character). This means that '01/02/2023' (January 2) would be considered greater than '12/31/2022' because '0' > '1' at the first character, leading to incorrect chronological ordering. The comparison does not interpret the string as a date value.

Answer

NULL values

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Error because DATE type is expected.

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Correct results because string comparison works for dates.

Question 15

A marketing analyst wants to segment customers based on purchasing behavior and demographics. The dataset includes continuous variables (spending amount, frequency) and categorical variables (region, gender). The analyst decides to use k-means clustering. What should the analyst do to prepare the data?

Accepted Answer

Standardize continuous variables and one-hot encode categorical variables. K-means clustering relies on Euclidean distance, which is sensitive to the scale of features. Standardizing continuous variables (e.g., spending amount, frequency) ensures they contribute equally to distance calculations, while one-hot encoding categorical variables (e.g., region, gender) converts them into numerical form without implying ordinal relationships, allowing k-means to process mixed data types correctly.

Answer

Use raw data because k-means works with mixed types

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Apply PCA first to reduce dimensionality

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Remove categorical variables entirely