Operational Procedures practice questions

A data analyst creates a dashboard for operational metrics. The operations team reports that the dashboard is confusing because it shows too many metrics on one screen. Which design principle should the analyst apply?

Trap 1: Increase white space

While white space improves readability, it does not reduce the number of metrics.

Trap 2: Use a single chart type

Different metrics may require different chart types for clarity.

Trap 3: Add more filters

Adding filters can make the dashboard more interactive but also more complex.

A
Apply progressive disclosure
Progressive disclosure shows a summary first and allows drilling into details.
B
Increase white space
Why wrong: While white space improves readability, it does not reduce the number of metrics.
C
Use a single chart type
Why wrong: Different metrics may require different chart types for clarity.
D
Add more filters
Why wrong: Adding filters can make the dashboard more interactive but also more complex.

Question 2hardmultiple choice

A data analyst is building a dashboard that will be used by both executives and operational managers. The executives need high-level summaries, while managers need granular details. Which dashboard design principle should the analyst apply?

Trap 1: Use a single chart that shows both summary and detail…

Combining both in one chart often results in clutter.

Trap 2: Display all available data on one page for transparency.

Information overload reduces usability.

Trap 3: Create two separate dashboards for each audience.

Separate dashboards may lead to data inconsistency and duplication.

A
Use a single chart that shows both summary and detail simultaneously.
Why wrong: Combining both in one chart often results in clutter.
B
Display all available data on one page for transparency.
Why wrong: Information overload reduces usability.
C
Design the dashboard with drill-down capabilities from summary to detail.
Drill-down allows executives to see overview and managers to access details on demand.
D
Create two separate dashboards for each audience.
Why wrong: Separate dashboards may lead to data inconsistency and duplication.

Question 3mediummultiple choice

A data engineer is designing an ETL pipeline to extract sales data from a legacy on-premise database and load it into a cloud data warehouse. The database is slow and queries during business hours affect performance. Which extraction strategy minimizes impact?

Trap 1: Query the database with SELECT * every hour

Frequent full queries overload the database and worsen performance impact.

Trap 2: Full table extraction nightly

Nightly full extraction still consumes significant resources and may not be incremental enough.

Trap 3: Use a database log shipping

Log shipping is for replication, not typically for ETL extraction, and may not be suitable for incremental loading.

A
Query the database with SELECT * every hour
Why wrong: Frequent full queries overload the database and worsen performance impact.
B
Incremental extraction using Change Data Capture (CDC)
CDC minimizes database load by extracting only changed data, reducing performance impact.
C
Full table extraction nightly
Why wrong: Nightly full extraction still consumes significant resources and may not be incremental enough.
D
Use a database log shipping
Why wrong: Log shipping is for replication, not typically for ETL extraction, and may not be suitable for incremental loading.

Question 4mediummultiple choice

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?

Trap 1: Extract directly from the primary database with high priority

High priority extraction can still degrade performance.

Trap 2: Run the extraction during peak hours to ensure data freshness

Running during peak hours maximizes impact on operations.

Trap 3: Schedule extraction at midnight when load is low

Even at low load, extracting from primary can still cause contention.

A
Extract directly from the primary database with high priority
Why wrong: High priority extraction can still degrade performance.
B
Run the extraction during peak hours to ensure data freshness
Why wrong: Running during peak hours maximizes impact on operations.
C
Schedule extraction at midnight when load is low
Why wrong: Even at low load, extracting from primary can still cause contention.
D
Use replication or a read replica to extract data
Read replicas are designed for such purposes and do not affect the primary.

Question 5hardmultiple choice

A data governance team is drafting a policy for handling personally identifiable information (PII). According to data governance best practices, which document should define the classification levels and handling procedures?

Trap 1: Data dictionary

A data dictionary defines metadata for data elements, not handling procedures.

Trap 2: Data quality report

A data quality report assesses quality dimensions, not classification.

Trap 3: Data flow diagram

A data flow diagram illustrates how data moves through systems.

A
Data dictionary
Why wrong: A data dictionary defines metadata for data elements, not handling procedures.
B
Data classification policy
A data classification policy categorizes data by sensitivity and outlines handling rules.
C
Data quality report
Why wrong: A data quality report assesses quality dimensions, not classification.
D
Data flow diagram
Why wrong: A data flow diagram illustrates how data moves through systems.

Question 6hardmulti select

Which TWO of the following are primary benefits of implementing a data governance program?

Trap 1: Faster data processing speed

Processing speed is more related to infrastructure than governance.

Trap 2: Increased data volume

Governance does not aim to increase volume; it manages existing data.

Trap 3: Lower storage costs

Governance may lead to better storage utilization but cost reduction is not a primary benefit.

A
Faster data processing speed
Why wrong: Processing speed is more related to infrastructure than governance.
B
Increased data volume
Why wrong: Governance does not aim to increase volume; it manages existing data.
C
Improved data quality and consistency
Governance establishes standards that enhance quality and consistency.
D
Lower storage costs
Why wrong: Governance may lead to better storage utilization but cost reduction is not a primary benefit.
E
Reduced data redundancy
Governance helps eliminate duplicate data across systems.

Question 7mediummultiple choice

To consolidate data from multiple operational databases into a central repository for reporting, a company decides to transform data before loading it into the target system. Which data integration approach is being used?

Trap 1: Data virtualization

Data virtualization provides a virtual view without moving data.

Trap 2: Change data capture

Change data capture identifies changes but does not define the transformation timing.

Trap 3: ELT (Extract, Load, Transform)

In ELT, data is loaded first and transformed later in the target system.

A
ETL (Extract, Transform, Load)
ETL transforms data during the integration process before loading into the target.
B
Data virtualization
Why wrong: Data virtualization provides a virtual view without moving data.
C
Change data capture
Why wrong: Change data capture identifies changes but does not define the transformation timing.
D
ELT (Extract, Load, Transform)
Why wrong: In ELT, data is loaded first and transformed later in the target system.

Question 8hardmultiple choice

A large financial institution is implementing a data governance framework to comply with new regulations requiring strict control over sensitive customer data. The data governance committee has identified several domains, including customer master data, transaction data, and risk assessment data. They need to decide on a master data management (MDM) approach that ensures a single, authoritative source of customer information across all systems. However, the current environment has multiple legacy systems with conflicting customer records. The committee is concerned about downtime and business disruption during the transition. Which MDM approach best balances data consistency with minimal operational impact?

Trap 1: Centralized MDM, which replaces all systems with one central master…

Incorrect: Centralized MDM is highly disruptive and requires full migration.

Trap 2: Consolidation MDM, which merges all customer data into a single…

Incorrect: Consolidation requires data migration and can cause downtime.

Trap 3: Coexistence MDM, which maintains multiple masters with…

Incorrect: Coexistence does not enforce a single source of truth.

A
Registry MDM, which creates a virtual view by linking records without moving data.
Correct: Registry avoids disruption by leaving data in place and creating a unified index.
B
Centralized MDM, which replaces all systems with one central master system.
Why wrong: Incorrect: Centralized MDM is highly disruptive and requires full migration.
C
Consolidation MDM, which merges all customer data into a single physical hub.
Why wrong: Incorrect: Consolidation requires data migration and can cause downtime.
D
Coexistence MDM, which maintains multiple masters with synchronization.
Why wrong: Incorrect: Coexistence does not enforce a single source of truth.

Question 9mediummultiple choice

A retail company is migrating its on-premises data warehouse to a cloud data warehouse. The current ETL process extracts data from a transactional database (SQL Server) and a web analytics system (JSON logs). The ETL runs nightly and takes 6 hours. The business requires that the new cloud warehouse support real-time reporting with data latency of less than 15 minutes. The data engineer proposes using change data capture (CDC) from the SQL Server database and streaming the JSON logs via a message queue. However, management is concerned about cost and complexity. The engineer must design a solution that meets the latency requirement while minimizing operational overhead. Which approach should the engineer recommend?

Trap 1: Export the SQL Server data to flat files every 15 minutes and use a…

This approach is fragile and adds complexity without significant benefit.

Trap 2: Continue with nightly batch loads but increase the frequency to…

Hourly batches still exceed 15-minute latency requirement.

Trap 3: Use a data virtualization tool to query the source systems directly…

Data virtualization may not meet performance requirements for real-time reporting.

A
Export the SQL Server data to flat files every 15 minutes and use a cloud storage trigger to load
Why wrong: This approach is fragile and adds complexity without significant benefit.
B
Continue with nightly batch loads but increase the frequency to every hour
Why wrong: Hourly batches still exceed 15-minute latency requirement.
C
Implement CDC for the SQL Server database and stream the JSON logs via a message queue to the cloud warehouse
CDC provides real-time changes; streaming handles JSON logs with low latency.
D
Use a data virtualization tool to query the source systems directly without moving data
Why wrong: Data virtualization may not meet performance requirements for real-time reporting.