DA0-001 Mining and Acquiring Data Practice Test 4 — 15 Questions

Question 1

A data analyst needs to identify duplicate customer records. Which TWO methods are commonly used? (Select two.)

Accepted Answer

Fuzzy matching using Levenshtein distance. Fuzzy matching using Levenshtein distance (Option A) is correct because it measures the edit distance between two strings, allowing identification of duplicates even when there are minor typographical differences, such as 'Jon Smith' vs. 'John Smith'. This is essential for deduplicating customer records where names, addresses, or other fields may have slight variations without being exact matches.

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Visual inspection of random sample

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Using a hash function on primary key

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Exact match on all fields

Question 2

After merging two datasets, an analyst finds that the resulting dataset has many null values in some columns. Which TWO steps should the analyst take to address this? (Select two.)

Accepted Answer

Impute nulls with the median.. Option B is correct because imputing nulls with the median is a standard technique for handling missing numerical data, especially when the distribution is skewed or contains outliers. The median is robust to extreme values and preserves the central tendency of the column, making it a safe choice for many analytical models. This approach avoids data loss while maintaining statistical integrity.

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Ignore nulls and proceed.

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Remove all rows with nulls.

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Replace nulls with a placeholder value like 'Unknown'.

Question 3

Which THREE data sources are suitable for web scraping? (Select three.)

Accepted Answer

HTML pages. HTML pages are suitable for web scraping because they contain structured or semi-structured data in markup format that can be parsed using libraries like BeautifulSoup or Scrapy. Web scrapers extract information from the DOM tree by targeting specific tags, classes, or attributes, making HTML a primary source for scraping.

Answer

CSV files

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Database connections

Question 4

A retail company wants to analyze customer purchase patterns to identify products frequently bought together. Which data mining technique is most appropriate?

Accepted Answer

Association rules. Association rules (market basket analysis) discover co-occurrence relationships. Regression predicts numeric values, clustering groups similar items, classification assigns categories.

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Classification

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Clustering

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Regression

Question 5

A data analyst is importing a CSV file that contains a mixture of numeric and text fields. What is the most common issue when importing?

Accepted Answer

Data types being incorrectly inferred. Data type inference often fails, causing numbers to be read as text or vice versa. File size limitations, missing headers, and duplicate rows are less common or not specific to mixed types.

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Duplicate rows

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Missing header row

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File size limitation

Question 6

During data acquisition, a data engineer uses a tool to extract data from a source system incrementally based on a timestamp column. Which method is being used?

Accepted Answer

Change data capture (CDC). Change data capture (CDC) captures modifications since the last extraction. Full extraction retrieves all data each time, snapshot extracts a point-in-time copy, and manual is not automated.

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Snapshot extraction

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Full extraction

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Manual extraction

Question 7

A data analyst discovers that a dataset contains multiple records for the same customer with different spellings (e.g., 'Jon' vs 'John'). Which data preparation step should be applied first?

Accepted Answer

Standardize text fields using a lookup table.. Option C is correct because the first step when dealing with inconsistent text values (like 'Jon' vs 'John') is to standardize the data using a lookup table or reference mapping. This ensures that all variations are normalized to a canonical form before any merging or deduplication is attempted, preventing data loss and preserving referential integrity.

Answer

Merge all records into one per customer.

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Remove duplicates based on exact match.

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Flag records for manual review.

Question 8

A financial institution is merging transaction data from two different systems. System A stores currency amounts as integers in cents, and System B stores as decimals in dollars. What is the best way to integrate the data?

Accepted Answer

Convert System A amounts to dollars by dividing by 100.. Option A is correct because converting System A's integer cents to dollars by dividing by 100 ensures both datasets share a consistent unit (dollars) and numeric data type (decimal). This direct transformation eliminates ambiguity in aggregation and reporting, as financial calculations require uniform precision and scale. Using a transformation layer or storing as strings would introduce unnecessary complexity or risk of rounding errors.

Answer

Keep both as is and use a transformation layer.

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Store all amounts as strings to preserve precision.

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Convert System B amounts to cents by multiplying by 100.

Question 9

A data team is integrating customer data from three sources. After joining, they find that the count of unique customers is lower than expected. What is the most likely cause?

Accepted Answer

Inconsistent key definitions.. When joining customer data from multiple sources, inconsistent key definitions (e.g., one source uses integer IDs while another uses string IDs, or different formats like 'CUST-001' vs '1001') cause the join to fail to match records that actually represent the same customer. This results in fewer unique customers than expected because the join treats mismatched keys as different entities, effectively dropping or misaligning records. The data team likely used an inner join or a left join that only retains matches based on exact key equality, so any key inconsistency reduces the count of matched unique customers.

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Missing values in join keys.

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Data truncation during transfer.

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Duplicate entries across sources.

Question 10

A data analyst needs to merge two customer tables from different sources. One table uses 'CUST_ID' as the primary key, the other uses 'CustomerID'. To ensure accurate merging, the analyst should first:

Accepted Answer

Normalize the key column names to a common format. Normalizing key column names to a common format (Option B) is the correct first step because the merge operation requires a consistent join key. Without aligning 'CUST_ID' and 'CustomerID' to a single name and data type, the database or ETL tool will treat them as different columns, resulting in a cross join or an error. This step ensures referential integrity and enables an accurate inner or outer join based on the primary key.

Answer

Perform a fuzzy match on names

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Remove duplicate rows from both tables

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Aggregate data by region

Question 11

A company receives daily sales data in CSV format. The data includes a 'Date' column in MM/DD/YYYY format. To load this into a database that expects YYYY-MM-DD, the analyst should:

Accepted Answer

Use a data transformation tool to convert the date format during ETL. Option B (use a transformation tool) is the standard practice during ETL. Option A (manually edit) is inefficient and error-prone. Option C (change schema) may cause other issues. Option D (ignore) loses data integrity.

Answer

Manually edit the CSV files before loading

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Change the database schema to accept MM/DD/YYYY

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Ignore the date column and use a default date

Question 12

A data analyst is tasked with collecting data from a web API that returns JSON. The API requires an API key in the header. Which method should be used to authenticate?

Accepted Answer

Add the API key in the HTTP header as 'Authorization: Bearer '. The correct method is to include the API key in the HTTP header using the 'Authorization: Bearer ' format. This is the standard approach for token-based authentication in REST APIs, as defined by RFC 6750. It keeps the credential out of URLs and logs, and is the expected mechanism for API key authentication in modern web APIs.

Answer

Use a session cookie

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Store the API key in the database and reference it

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Include the API key in the URL query string

Question 13

An analyst needs to combine two datasets from different sources that share a common key but have different levels of granularity. Dataset A has daily sales per store, Dataset B has hourly foot traffic per store. The analyst wants to analyze correlation. Which approach is appropriate?

Accepted Answer

Aggregate Dataset B to daily level before merging. Aggregating Dataset B (hourly foot traffic) to the daily level ensures both datasets share the same granularity before merging on the common key (store and date). This allows a valid correlation analysis between daily sales and daily foot traffic without introducing artificial patterns or data duplication. Merging at mismatched granularities would violate the assumption that each row represents a comparable unit of observation.

Answer

Use an outer join and keep all rows

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Disaggregate Dataset A to hourly level by dividing daily sales by hours

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Join on store and date without aggregation

Question 14

A data team is designing an ETL process to extract data from an operational database daily. The database experiences heavy write loads during business hours. What is the best practice to minimize impact on operations?

Accepted Answer

Use replication or a read replica to extract data. Option B (use a read replica) is best because it offloads the extraction from the primary. Option A (extract from primary) impacts performance. Option C (schedule at midnight) still hits primary. Option D (run during peak) increases load.

Answer

Extract directly from the primary database with high priority

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Run the extraction during peak hours to ensure data freshness

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Schedule extraction at midnight when load is low

Question 15

A healthcare organization acquires data from multiple hospitals with different patient record systems. The data includes patient IDs but no common identifier across systems. Which technique should be used to link records?

Accepted Answer

Probabilistic record linkage using name, DOB, and ZIP. Option D (probabilistic linkage) is designed for such situations. Option A (random IDs) loses connections. Option B (merge without dedup) creates duplicates. Option C (manual matching) is not scalable.

Answer

Merge all records without deduplication

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Generate random unique IDs for each system

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Manually match records for all patients