Which of the following is a common use case for streaming data processing?

Backing up a database to an archive nightly

Loading a data warehouse for end-of-day reconciliation

Generating a monthly sales report from historical data

Detecting fraudulent credit card transactions as they occur

A financial regulator requires a bank to prove that a specific customer's account balance in a quarterly report was derived from the correct set of transactions and that no unauthorized adjustments were made. The bank's data pipeline ingests transactions daily via batch jobs and applies several transformation steps. Which approach best satisfies the regulator's requirement?

Storing raw transaction data in a separate data lake

Increasing the frequency of batch jobs to run every hour

Implementing end-to-end data lineage that captures source transactions, transformation logic, and job execution metadata

Encrypting all transaction data at rest and in transit

A company's nightly ETL job failed midway through the transformation step. The operations team needs to restart the job without re-processing the data that was already successfully extracted and loaded. Which feature of a well-designed data pipeline would support this?

Checkpointing and idempotent processing

A data engineering team is designing a pipeline that ingests customer data from a CRM, an email marketing platform, and a billing system. The pipeline must combine these sources, clean the data, and load it into a data warehouse. The team also needs to document which system each field originated from and what transformations were applied. Which pipeline component should be configured to capture this information?

The data validation rules engine

The data lineage metadata store

CompTIADataX

Domain 4Objective 3

Data Ingestion DY0-001 Practice Questions (Page 4)

Part of the Operations and processes domain, which accounts for 22% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~14–24 in this domain), expect 2–3 from this objective — we provide 26 practice questions to prepare you well beyond it. (estimate)

26questions here

6free pages

4concepts

22%of the exam

Questions 16–20

16foundation · easy
Which of the following is a common use case for streaming data processing?
Select an answer first
17application · medium
A data analyst discovers a discrepancy in a monthly sales report. The analyst needs to determine whether the issue originated in the source CRM system, during the extraction step, or during a transformation that joined data from multiple sources. Which capability is most directly needed to investigate this?
Select an answer first
18expert · hard
A financial regulator requires a bank to prove that a specific customer's account balance in a quarterly report was derived from the correct set of transactions and that no unauthorized adjustments were made. The bank's data pipeline ingests transactions daily via batch jobs and applies several transformation steps. Which approach best satisfies the regulator's requirement?
Select an answer first
19application · medium
A company's nightly ETL job failed midway through the transformation step. The operations team needs to restart the job without re-processing the data that was already successfully extracted and loaded. Which feature of a well-designed data pipeline would support this?
Select an answer first
20application · medium
A data engineering team is designing a pipeline that ingests customer data from a CRM, an email marketing platform, and a billing system. The pipeline must combine these sources, clean the data, and load it into a data warehouse. The team also needs to document which system each field originated from and what transformations were applied. Which pipeline component should be configured to capture this information?
Select an answer first

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Data Ingestion DY0-001 Practice Questions (Page 4)

Questions 16–20

Which of the following is a common use case for streaming data processing?

A company's nightly ETL job failed midway through the transformation step. The operations team needs to restart the job without re-processing the data that was already successfully extracted and loaded. Which feature of a well-designed data pipeline would support this?

Keep studying — Operations and processes