CompTIA Data+ (DA0-002)Data MiningHard
A data governance committee is establishing policies for data acquisition from external vendors. They are particularly concerned about ensuring the data received is accurate, reliable, and adheres to privacy regulations. Which of the following aspects is MOST critical to address during the data acquisition phase to mitigate risks related to data quality and compliance?
- AEstablishing clear data contracts and SLAs with vendors.
- BDefining the schema for the target data warehouse.
- CImplementing advanced data visualization tools.
- DOptimizing the network bandwidth for data transfer.
Show answer & explanationAnswer & explanation
Correct answer: A. Establishing clear data contracts and SLAs with vendors.
Establishing clear data contracts and Service Level Agreements (SLAs) with external vendors during the acquisition phase is paramount. These documents define data format, quality standards, refresh rates, security protocols, and compliance requirements (e.g., GDPR, CCPA), directly addressing accuracy, reliability, and privacy risks from the source.
Why the other options are wrong
- B. Defining the target schema is part of data modeling for the destination system, which happens later in the process and doesn't directly control the quality or compliance of the *acquired* source data.
- C. Data visualization tools are used for analysis and reporting *after* data has been acquired and processed; they do not impact the quality or compliance during the acquisition phase.
- D. Optimizing network bandwidth is important for data transfer efficiency but does not inherently ensure data quality, accuracy, or compliance of the data content itself.
Data Contract
A formal agreement or specification that details the expected format, schema, quality, and behavior of data exchanged between systems or teams, especially with external data providers.
- Ensures data consistency and quality at the source.
- Defines data governance and compliance requirements.
- Acts as a foundational document for reliable data pipelines.
Memory trick: Acquiring data: secure the source, define the rules, then flow.