DA0-002 Data Acquisition and Preparation Practice Test 3 — 15 Questions

Question 1

A marketing team wants to collect data on competitor pricing for similar products. Which data source is most appropriate?

Accepted Answer

External public web scraping. External public web scraping is the most appropriate data source because competitor pricing is publicly available on websites, and web scraping allows automated extraction of this structured or unstructured data. This approach directly addresses the need for external competitive intelligence without relying on internal or customer-reported data.

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Customer surveys

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Internal ERP system

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Internal sales data

Question 2

During data acquisition, an analyst notices that the data from an external vendor has inconsistent date formats. What is the first step the analyst should take?

Accepted Answer

Perform data profiling. Data profiling is the initial step to understand the structure, quality, and issues in the data. Rejecting or transforming without profiling may lead to errors, and contacting the vendor is premature without understanding the scope.

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Contact the vendor to request corrected data

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Immediately transform dates to a standard format

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Reject the entire dataset

Question 3

A data engineer needs to acquire data from a legacy mainframe system that does not support modern APIs or direct database connectivity. Which approach is most feasible?

Accepted Answer

Use FTP to transfer flat files. FTP (File Transfer Protocol, RFC 959) is a widely supported, low-overhead method for transferring flat files (e.g., CSV, EBCDIC-encoded text) from legacy mainframe systems that lack modern APIs or direct database connectivity. Mainframes like IBM z/OS natively support FTP, allowing the data engineer to schedule periodic file exports without requiring system modernization or complex middleware.

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Re-platform the mainframe to a modern system

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Use a database gateway

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Manual data entry

Question 4

A small business wants to acquire customer feedback through a short questionnaire emailed after purchase. Which data acquisition method does this represent?

Accepted Answer

Survey. A survey is a structured data collection method where respondents answer predefined questions, typically via a form or questionnaire. In this scenario, the business is using a short questionnaire emailed after purchase to gather customer feedback, which directly aligns with the definition of a survey as a data acquisition method.

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Transaction log

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Interview

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Observation

Question 5

An organization is integrating data from multiple sources into a data warehouse. They need to handle differences in data granularity (e.g., daily vs. hourly sales data). Which technique is most appropriate?

Accepted Answer

Data aggregation. Data aggregation is the correct technique because it allows the organization to roll up hourly sales data to a daily granularity, ensuring consistency when integrating sources with different levels of detail. By applying aggregation functions (e.g., SUM, AVG) during the ETL process, the data warehouse can store all data at a common grain, which is essential for accurate reporting and analysis.

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Data normalization

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Data deduplication

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Data profiling

Question 6

A data analyst is using a public API to collect historical weather data. The API has a rate limit of 100 requests per minute, but the analyst needs to retrieve 10,000 records as quickly as possible. What strategy should be used?

Accepted Answer

Paginate with appropriate delays. Paginating with appropriate delays respects the API's rate limit of 100 requests per minute while maximizing throughput. By splitting the 10,000 records into pages (e.g., 100 records per page) and sending requests at a rate just under the limit (e.g., one request every 0.6 seconds), the analyst can retrieve all data in approximately 100 minutes without triggering HTTP 429 rate-limit errors.

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Increase the request rate

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Use multiple API keys

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Download a precompiled dataset

Question 7

Which TWO are common methods for acquiring internal data? (Choose two.)

Accepted Answer

Transaction logs. Transaction logs are a primary source of internal data because they record every interaction or event within a system, such as database changes, user access, or application errors. This data is generated and stored internally by the organization's own infrastructure, making it a classic example of internal data acquisition.

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Social media APIs

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Government databases

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Web scraping

Question 8

Which THREE are best practices for data profiling during acquisition? (Choose three.)

Accepted Answer

Check for completeness. Checking for completeness (Option B) is a best practice during data acquisition because it ensures that all required fields and records are present before further processing. Incomplete data can lead to incorrect analysis or failed transformations, so profiling for missing values or nulls is a fundamental validation step.

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Immediately normalize data

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Skip validation for trusted sources

Question 9

Refer to the exhibit. An analyst runs this query before acquiring data from a PostgreSQL database. What is the primary purpose of this query?

Accepted Answer

To check for data freshness. The query shown in the exhibit retrieves the `last_analyzed` column from PostgreSQL's system catalog, which records when table statistics were last updated. Analyzing this timestamp helps the analyst determine if the table's data is fresh enough for the intended analysis, thus checking data freshness before acquisition.

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To verify data types

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To find primary keys

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To identify duplicate tables

Question 10

Refer to the exhibit. An analyst sees this log during data acquisition. What action should be taken first?

Accepted Answer

Modify the ETL mapping for data types. The log shows a data type mismatch during ETL (Extract, Transform, Load) processing, where a date field is being read as a string. The correct first action is to modify the ETL mapping for data types to ensure the date string is properly cast or converted to the target date format, preventing data loss or corruption. This aligns with standard data acquisition best practices: adjust the transformation layer to handle source data anomalies before rejecting or altering the source.

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Reject the entire dataset

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Ignore warnings and continue

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Correct the date string in the source

Question 11

A data analyst is tasked with combining customer data from a CRM system and a billing system. The CRM uses a GUID for customer ID, while billing uses an integer. Which approach should the analyst use to ensure a reliable merge?

Accepted Answer

Standardize the customer ID format and use it as the join key.. Standardizing the customer ID format (e.g., converting the billing integer to a GUID or mapping both to a common string key) ensures a consistent join key across heterogeneous systems. This eliminates type mismatch errors and guarantees that each customer record can be matched reliably, as GUIDs are globally unique and integers are typically sequential, so direct comparison would fail without transformation.

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Use the customer name as the join key.

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Merge using a cross-join and then filter manually.

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Perform a fuzzy match on the customer address.

Question 12

A data team needs to extract data from a legacy system that only supports flat file exports. Which data acquisition method is most appropriate?

Accepted Answer

File transfer via SFTP. The legacy system only supports flat file exports, meaning it cannot provide direct database or API access. SFTP (SSH File Transfer Protocol) is the most appropriate method because it securely transfers flat files over a network, aligning with the system's export capabilities while ensuring data integrity and encryption during transit.

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Database replication

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API call

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Web scraping

Question 13

During a data mining project, an analyst discovers that a significant number of records have a negative value for the age field. What is the most appropriate first step?

Accepted Answer

Investigate the source system for data entry errors.. The first step in handling anomalous data like negative ages is to investigate the source system for data entry errors. This aligns with the data mining process, where understanding the root cause of data quality issues is critical before applying any imputation or removal techniques. Without investigation, you risk masking systemic problems that could affect all records.

Answer

Impute using regression.

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Replace negative age with the mean age.

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Remove all records with negative age.

Question 14

Refer to the exhibit. What does the query return?

Accepted Answer

Customers who have placed more than 5 orders.. The query uses a HAVING clause with COUNT(*) > 5, which filters groups (by customer ID) to only those with more than 5 orders. The GROUP BY customer ID ensures the count is per customer, so the result is customers who have placed more than 5 orders. Option C is correct because the condition is strictly greater than 5, not at least 5.

Answer

All orders grouped by customer ID.

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Customers who have placed at least 5 orders.

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All customers who have placed orders.

Question 15

Refer to the exhibit. What data quality issue is indicated?

Accepted Answer

Non-standardized data entry. The error shows that a non-numeric value 'N/A' is present in a numeric column, indicating non-standardized data entry. Duplication, inconsistency, or outliers are not directly shown.

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Data inconsistency

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Outlier

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Data duplication