Databricks Accredited Lakehouse Platform Fundamentals (LFC) — Questions and Answers

Question 1: In a multi-cloud or hybrid Lakehouse deployment, what technology makes data portable across cloud providers without vendor lock-in?

Open table formats (Delta Lake, Apache Iceberg, Apache Hudi) stored on object storage (Correct answer)
Cloud-provider-managed ETL services that sync data nightly
Dedicated cross-cloud VPN tunnels that replicate data in real time
Proprietary binary formats specific to each cloud's native data warehouse

Correct answer: Open table formats (Delta Lake, Apache Iceberg, Apache Hudi) stored on object storage

Open table formats store data as standard Parquet files with open metadata specs, allowing any compatible engine on any cloud to read and write the same tables.

Question 2: Which Medallion layer is BEST suited for data scientists who need to explore raw, unmodified source data?

Silver layer
Platinum layer
Bronze layer (Correct answer)
Gold layer

Correct answer: Bronze layer

The Bronze layer stores raw, unmodified data as ingested from source systems, making it ideal for exploration of original records.

Question 3: When documenting activities related to medallion architecture layers, which practice is considered essential for LFC certification holders?

Recording only outcomes while omitting the methods and processes used
Keeping documentation in personal notes that are not accessible to other team members
Maintaining comprehensive records that include procedures, observations, results, and any anomalies (Correct answer)
Completing documentation only when requested by auditors or supervisors

Correct answer: Maintaining comprehensive records that include procedures, observations, results, and any anomalies

Comprehensive documentation that includes procedures, observations, results, and any anomalies is essential in medallion architecture layers. This supports quality assurance, enables peer review, and satisfies regulatory and audit requirements.

Question 4: What is the recommended approach when a source system changes its schema and sends new fields to the Bronze layer?

Reject the new records until the schema is manually updated
Route records with new fields directly to Gold
Allow schema evolution so Bronze absorbs the new fields without pipeline failure (Correct answer)
Truncate and reload the Bronze table with the new schema

Correct answer: Allow schema evolution so Bronze absorbs the new fields without pipeline failure

Bronze should support schema evolution (e.g., Delta Lake's schema evolution) to handle upstream changes without breaking ingestion.

Question 5: A LFC professional encounters an unfamiliar situation while performing databricks sql & warehousing duties. What is the most appropriate first action?

Skip the task entirely and move to the next assignment
Proceed based on general assumptions to avoid delays
Apply a solution from an unrelated field without verification
Consult relevant standards, guidelines, or a qualified supervisor before proceeding (Correct answer)

Correct answer: Consult relevant standards, guidelines, or a qualified supervisor before proceeding

When facing unfamiliar situations in databricks sql & warehousing, the most appropriate action is to consult relevant standards, guidelines, or a qualified supervisor. This ensures safety, accuracy, and compliance while building professional knowledge.

Question 6: A team needs to share a live Delta table with an external partner's Databricks workspace without copying data. Which Databricks feature enables this?

Deep Clone
Unity Catalog Federation
Databricks Repos sync
Delta Sharing (Correct answer)

Correct answer: Delta Sharing

Delta Sharing is an open protocol that allows sharing live Delta Lake tables with external recipients without duplicating the underlying data.

Question 7: Why might a Gold layer table in Medallion Architecture be pre-aggregated rather than computed at query time?

Because Delta Lake does not support aggregation functions
To avoid granting users access to raw data
To comply with data retention policies
To reduce query latency and compute cost for frequent BI workloads (Correct answer)

Correct answer: To reduce query latency and compute cost for frequent BI workloads

Pre-aggregating in Gold shifts heavy compute to the pipeline, so dashboards and reports execute quickly without re-scanning large datasets.

Question 8: Which Unity Catalog securable object stores files (images, CSVs, ML models) in cloud storage while enforcing access controls?

External Location
Volume (Correct answer)
Share
Credential

Correct answer: Volume

Volumes provide governed access to non-tabular files in cloud object storage, with Unity Catalog enforcing permissions just like tables.

Question 9: What is the primary ethical obligation of a LFC professional when a conflict of interest arises during mlflow model registry activities?

Resolve the conflict privately without informing stakeholders
Ignore the conflict if it does not directly affect the current task
Proceed while favoring the outcome that benefits the professional personally
Disclose the conflict to all relevant parties and recuse from the decision if necessary (Correct answer)

Correct answer: Disclose the conflict to all relevant parties and recuse from the decision if necessary

The primary ethical obligation when a conflict of interest arises in mlflow model registry is to disclose it to all relevant parties and, if necessary, recuse from the decision. This maintains professional integrity and stakeholder trust.

Question 10: Which Structured Streaming output sink writes results to an in-memory table that can be queried with SQL during development and testing?

File sink
Foreach sink
Console sink
Memory sink (Correct answer)

Correct answer: Memory sink

The memory sink writes streaming results to an in-memory table registered under a given query name, making it easy to query interactively during development.

Question 11: Delta Sharing is an open protocol for sharing data. Which of the following best describes what a 'recipient' is in Delta Sharing?

A Delta table configured for external access
An entity (person or organization) authorized to access shared data (Correct answer)
A Databricks workspace that owns the shared data
A Unity Catalog metastore in the receiving region

Correct answer: An entity (person or organization) authorized to access shared data

In Delta Sharing, a recipient is an authenticated entity — which can be inside or outside Databricks — that has been granted access to one or more shares.

Question 12: In a Databricks SQL Warehouse, what is the 'auto stop' (auto-termination) feature designed to do?

Stop the warehouse immediately after each query completes
Prevent the warehouse from running queries longer than a set duration
Terminate the warehouse after a configurable idle period to save costs (Correct answer)
Automatically restart the warehouse when a query fails

Correct answer: Terminate the warehouse after a configurable idle period to save costs

Auto-termination shuts down a SQL Warehouse after it has been idle for a user-defined number of minutes, reducing unnecessary DBU consumption.

Question 13: Which data format characteristic most benefits Photon's vectorized execution?

CSV with mixed data types
JSON semi-structured format
Columnar storage with contiguous memory layout (Correct answer)
Row-based storage with wide tables

Correct answer: Columnar storage with contiguous memory layout

Photon's vectorized execution processes batches of column values stored contiguously in memory, maximizing CPU cache efficiency.

Question 14: A data team discovers that a MERGE operation accidentally deleted 10,000 rows from a production Delta table 2 hours ago. What is the fastest recovery path?

Manually re-insert rows from a separate audit table
Re-run the entire ingestion pipeline from source
Restore from an external backup system
Use RESTORE TABLE to roll back to the version before the faulty MERGE (Correct answer)

Correct answer: Use RESTORE TABLE to roll back to the version before the faulty MERGE

RESTORE TABLE [table] TO VERSION AS OF [n] instantly rolls back a Delta table to a prior snapshot, making it the fastest recovery option.

Question 15: Which Python decorator is used to define a materialized Delta table in a DLT pipeline?

@dlt.dataset
@dlt.table (Correct answer)
@dlt.view
@dlt.stream

Correct answer: @dlt.table

The @dlt.table decorator defines a materialized table whose results are stored persistently as a Delta table.

Databricks Accredited Lakehouse Platform Fundamentals (LFC)

The Databricks Lakehouse Fundamentals Certification validates knowledge of the Databricks Lakehouse Platform, including its architecture, core components (Apache Spark, Delta Lake, Databricks SQL, MLflow), and how it supports data engineering, data science, and analytics use cases.

Questions

20 min

Time Limit

70%

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