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Data Acquisition and Preparation

Master techniques for acquiring, exploring, and transforming data to prepare it for analysis.

Covers using data acquisition methods including data integration techniques and queries to gather and combine data from multiple sources. Includes performing data exploration to find missing values, duplication, redundancy, or outliers in datasets. Also covers applying data transformation techniques including data cleansing, merging, parsing, and formatting to ensure data quality and consistency before analysis.
5 minutes 5 Questions

Data Acquisition and Preparation forms the foundation of the analytics lifecycle, representing a critical domain within the CompTIA Data+ V2 certification objectives. This phase involves gathering raw data from various sources and transforming it into a clean, usable format for analysis. **Data Ac…

Concepts covered: Data integration techniques, ETL (Extract, Transform, Load) processes, ELT (Extract, Load, Transform) approach, SQL queries for data acquisition, API data collection methods, Data pipelines and workflows, Combining data from multiple sources, Data ingestion patterns, Identifying missing values, Detecting duplicate records, Data redundancy analysis, Outlier detection techniques, Exploratory Data Analysis (EDA), Data profiling and summarization, Understanding data distributions, Data cleansing techniques, Handling missing data, Data merging and joining, Data parsing and extraction, Data formatting and standardization, Data normalization and scaling, Data type conversion, String manipulation and text cleaning

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Data+ - Data Acquisition and Preparation Example Questions

Test your knowledge of Data Acquisition and Preparation

Question 1

A data engineer is performing data profiling on a healthcare dataset containing patient visit records spanning 15 years. They discover that the 'Diagnosis_Code' field has a cardinality of 847 unique values, the 'Visit_Date' field shows a bimodal distribution pattern, and the 'Insurance_Provider' field exhibits 23% null values concentrated in records from 2009-2012. When preparing a profiling summary for stakeholders, which interpretation best explains the combined significance of these three findings for downstream analytical reliability?

Correct Answer: The high cardinality suggests granular medical coding requiring domain expertise for aggregation, the bimodal temporal pattern indicates potential systematic changes in data collection or patient behavior, and the temporally clustered nulls point to a specific historical data quality issue that may require imputation strategies

The correct answer provides the most accurate interpretation of all three data profiling findings in a healthcare context.

High Cardinality (847 unique diagnosis codes): Medical diagnosis codes (like ICD-10) are standardized but highly granular classification systems. Having 847 unique values over 15 years of patient visits is reasonable and expected for healthcare data. This means analysts will need domain expertise to properly aggregate or categorize these codes for meaningful analysis - not that there's redundancy or free-text issues.

Bimodal Distribution in Visit_Date: A bimodal (two-peak) distribution in temporal data typically indicates two distinct patterns or periods of activity. This could reflect systematic changes such as shifts in data collection methods, healthcare policy changes, seasonal patterns, or changes in patient populations over the 15-year span. This is a signal worth investigating, not necessarily corruption or fiscal year partitioning.

23% Null Values Concentrated in 2009-2012: The fact that null values are clustered in a specific time period (rather than randomly distributed) strongly suggests a historical data quality issue during that era - perhaps a system migration, changes in data entry requirements, or insurance provider data integration problems during those years. This temporal clustering indicates the nulls are not random and may require specific imputation or handling strategies.

Why other answers are incorrect:

The answer suggesting proper normalization, seasonal trends, and archival policies makes unfounded positive assumptions. High cardinality doesn't confirm normalization, bimodal patterns aren't necessarily seasonal, and archival policies don't typically create null patterns in specific fields.

The answer suggesting redundancy, data corruption, and referential integrity violations is overly negative. High cardinality in diagnosis codes isn't redundancy, bimodal distributions aren't evidence of corruption, and temporal null clustering doesn't indicate referential integrity failures.

The answer mentioning free-text entries, fiscal year partitioning, and regulatory data masking makes incorrect assumptions. Standard medical coding systems produce high cardinality naturally, bimodal temporal patterns don't reflect fiscal partitioning, and data masking for compliance would typically affect more recent records under retention requirements, not older ones.

Question 2

A retail company is collecting inventory data from supplier APIs. When making GET requests to retrieve product catalogs, the API documentation specifies that responses include an 'ETag' header value. What is the primary purpose of using this ETag value in subsequent API requests?

Correct Answer: To enable conditional requests that only return data when the resource has been modified since the last retrieval

The correct answer is that ETags enable conditional requests that only return data when the resource has been modified since the last retrieval.

An ETag (Entity Tag) is an HTTP header that serves as a version identifier for a specific resource. When a client receives a response with an ETag, it can store this value and include it in subsequent requests using the 'If-None-Match' header. The server then compares the provided ETag with the current version of the resource. If they match (meaning the resource hasn't changed), the server returns a 304 Not Modified status instead of sending the entire resource again. This mechanism significantly reduces bandwidth usage and improves API efficiency, which is particularly valuable when dealing with large product catalogs that may not change frequently.

The second option is incorrect because ETags have no authentication function. Bearer tokens and authentication credentials are handled through separate Authorization headers and security mechanisms, not through ETags.

The third option is incorrect because ETags do not determine response formats. Content negotiation for formats like JSON or XML is handled through the Accept header, while the Content-Type header indicates the format of the response body. ETags serve an entirely different purpose related to resource versioning.

The fourth option is incorrect because ETags are not related to compression or transfer speed optimization. HTTP compression is handled through Content-Encoding headers and mechanisms like gzip or deflate. While ETags do relate to caching, their function is to validate cached content freshness rather than to compress data for faster transfers.

Question 3

A retail database contains a Products table with columns: product_id, product_name, category, price, and supplier_id. A purchasing manager wants to see all products sorted alphabetically by category first, and then by price from lowest to highest within each category. Which SQL clause combination achieves this sorting requirement?

Correct Answer: ORDER BY category ASC, price ASC

The correct answer is ORDER BY category ASC, price ASC.

This SQL clause correctly achieves the required sorting because:

  1. ORDER BY is the proper SQL keyword for sorting query results
  2. Listing 'category ASC' first sorts all products alphabetically (A to Z) by their category
  3. The comma followed by 'price ASC' creates a secondary sort, which arranges products from lowest to highest price within each category
  4. The ASC keyword specifies ascending order, which means A-Z for text and low-to-high for numbers

Why the other options are incorrect:

  • ORDER BY category DESC, price ASC would sort categories in reverse alphabetical order (Z to A), not alphabetically as required by the purchasing manager.

  • GROUP BY category, price ORDER BY ASC is syntactically incorrect. GROUP BY is used for aggregating data with functions like COUNT or SUM, not for sorting. Additionally, 'ORDER BY ASC' alone is invalid syntax because you must specify which column(s) to sort by.

  • SORT BY category ASC THEN BY price ASC uses invalid SQL syntax. Standard SQL uses ORDER BY, not SORT BY. The 'THEN BY' clause does not exist in standard SQL - multiple sort columns are separated by commas.

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