DA0-001 Practice Test 6 — 10 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.

Answer

Change user permissions

Answer

Delete objects from the bucket

Answer

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.

Answer

FULL OUTER JOIN

Answer

INNER JOIN

Answer

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.

Answer

Volume of data

Answer

Data format

Answer

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.

Answer

json.loads()

Answer

to_csv()

Answer

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.

Answer

Accuracy

Answer

F1 score

Answer

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

Answer

Join two tables

Answer

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.

Answer

The source table was dropped

Answer

The analyst does not have permission to refresh the view

Answer

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.

Answer

Standard deviation

Answer

Median

Answer

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.

Answer

Independence of observations

Answer

No multicollinearity

Answer

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.

Answer

Drop rows with missing values

Answer

Mean imputation

Answer

Median imputation

DA0-001 Practice Test 6 — 10 Questions

Exhibit

DA0-001 Practice Test 6 — 10 Questions

Exhibit