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

Question 1: Which statement about Photon in Databricks SQL Warehouses is TRUE?

Photon is only available on Azure Databricks, not AWS or GCP
Photon requires a separate license and is not included in SQL Warehouse tiers
Photon replaces the JVM-based Spark engine entirely at the cluster level
Photon is a C++-based vectorized query engine that accelerates SQL and DataFrame operations (Correct answer)

Correct answer: Photon is a C++-based vectorized query engine that accelerates SQL and DataFrame operations

Photon is a Databricks-native C++ vectorized engine that runs alongside Spark JVM to accelerate SQL and DataFrame workloads.

Question 2: A Bronze table is growing rapidly and query performance is degrading. Which operation should be applied WITHOUT moving data out of the Bronze layer?

Migrating Bronze data to a Silver schema
Converting Bronze to CSV format for faster reads
Aggregating records into a Gold table
Running OPTIMIZE and Z-ORDER on the Bronze Delta table (Correct answer)

Correct answer: Running OPTIMIZE and Z-ORDER on the Bronze Delta table

OPTIMIZE compacts small files and Z-ORDER co-locates related data, improving scan performance on Delta tables within any layer.

Question 3: A data analyst notices that a Databricks SQL query uses more DBU than expected. Which tool in the Databricks SQL UI shows the per-operator resource consumption of a completed query?

SQL Warehouse metrics tab
EXPLAIN ANALYZE output
Query Profile (Spark UI embedded view) (Correct answer)
Cost Explorer dashboard

Correct answer: Query Profile (Spark UI embedded view)

The Query Profile in Databricks SQL provides a visual, operator-level breakdown of time and data processed for a completed query.

Question 4: A Unity Catalog admin needs to configure access to an S3 bucket for external tables. Which two objects must be created?

Storage credential and volume
Service principal and storage credential
Instance profile and external location
Storage credential and external location (Correct answer)

Correct answer: Storage credential and external location

A storage credential (IAM role or service principal) and an external location (path + credential binding) are both required to access external cloud storage in Unity Catalog.

Question 5: A LFC professional encounters an unfamiliar situation while performing mlflow model registry duties. What is the most appropriate first action?

Proceed based on general assumptions to avoid delays
Apply a solution from an unrelated field without verification
Skip the task entirely and move to the next assignment
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 mlflow model registry, 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: Which lakehouse feature enables schema enforcement?

Schema enforcement (Correct answer)
Schema on read
Data lakes
Data catalogs

Correct answer: Schema enforcement

Schema enforcement is a crucial lakehouse feature, often provided by formats like Delta Lake, that ensures all new data written to a table conforms to its predefined schema. This mechanism prevents the ingestion of malformed or inconsistent data, thereby maintaining high data quality and reliability for downstream analytics and applications.

Question 7: A data engineer wants to read only the new rows added to a Delta table since the last pipeline run. Which Databricks SQL / Delta Lake feature best supports this pattern?

SHALLOW CLONE of the target table
CHANGE DATA FEED (CDF) with table_changes() (Correct answer)
MERGE INTO with a timestamp filter
COPY INTO with VALIDATE mode

Correct answer: CHANGE DATA FEED (CDF) with table_changes()

Change Data Feed exposes a table_changes() function that returns only inserted, updated, or deleted rows since a given version or timestamp.

Question 8: Which of the following scenarios VIOLATES best practices for Medallion Architecture?

Storing raw JSON blobs in Bronze without parsing
Allowing business analysts to modify Gold tables directly (Correct answer)
Creating multiple Gold tables for different business domains
Writing cleansed records to Silver using a MERGE statement

Correct answer: Allowing business analysts to modify Gold tables directly

Gold tables should be populated by controlled pipelines; allowing ad-hoc analyst writes breaks data integrity and reproducibility.

Question 9: What is the purpose of Z-ordering (data skipping) in Delta Lake's storage layer?

To encrypt data files at rest using AES-256
To convert JSON ingestion files into columnar Parquet format
To replicate data across multiple cloud regions automatically
To co-locate related data within Parquet files so queries scan fewer files (Correct answer)

Correct answer: To co-locate related data within Parquet files so queries scan fewer files

Z-ordering multidimensionally sorts data so that frequently filtered columns are co-located, enabling Delta Lake to skip irrelevant files during query execution.

Question 10: What is Delta Live Tables (DLT) in the Databricks ecosystem?

A SQL query optimization engine
A visualization tool for Delta Lake tables
A machine learning model deployment service
A declarative ETL framework for building reliable data pipelines (Correct answer)

Correct answer: A declarative ETL framework for building reliable data pipelines

Delta Live Tables is a declarative ETL framework that automates pipeline infrastructure management and enforces data quality constraints.

Question 11: What is 'Liquid Clustering' in Delta Lake and how does it differ from traditional partitioning?

A streaming mode that clusters microbatches by arrival time
A flexible, auto-tuned data layout strategy that replaces static partition columns with dynamic clustering keys (Correct answer)
A Unity Catalog feature that groups related tables into namespaces
A compression algorithm that reduces Parquet file sizes by 50%

Correct answer: A flexible, auto-tuned data layout strategy that replaces static partition columns with dynamic clustering keys

Liquid Clustering incrementally reorganizes data without requiring a full rewrite and adapts to changing query patterns, unlike static Hive-style partitioning.

Question 12: In Spark, which action triggers actual computation on a DataFrame's lazy evaluation chain?

df.count() (Correct answer)
df.withColumn()
df.filter()
df.select()

Correct answer: df.count()

count() is an action that triggers Spark to execute the entire DAG of transformations and return a result to the driver.

Question 13: When creating a Databricks SQL alert, what condition triggers the alert notification?

When a query result value meets a defined comparison condition (Correct answer)
When the SQL Warehouse is terminated unexpectedly
When a user edits the underlying dashboard
When the scheduled query's runtime exceeds a threshold

Correct answer: When a query result value meets a defined comparison condition

Databricks SQL alerts fire when a scheduled query returns a result that satisfies the configured value comparison (e.g., count > 100).

Question 14: What is the effect of enabling liquid clustering on a Delta table used frequently with Photon?

Liquid clustering only benefits standard Spark, not Photon
Liquid clustering continuously reorganizes data to improve file-level statistics and Photon's data skipping (Correct answer)
Liquid clustering forces full table scans to maintain cluster metadata
Liquid clustering disables Photon for that table

Correct answer: Liquid clustering continuously reorganizes data to improve file-level statistics and Photon's data skipping

Liquid clustering automatically maintains data co-location over time, improving the selectivity of Delta's file statistics that Photon uses for data skipping.

Question 15: Which statement correctly describes how Unity Catalog handles external tables compared to managed tables from a security standpoint?

External tables require row-level security; managed tables do not
External tables are automatically encrypted; managed tables are not
Unity Catalog cannot govern external tables
Dropping a managed table deletes the data, while dropping an external table leaves the underlying files intact (Correct answer)

Correct answer: Dropping a managed table deletes the data, while dropping an external table leaves the underlying files intact

Managed tables have their lifecycle fully controlled by Unity Catalog (data deleted on drop), whereas external tables only drop the metadata and leave cloud storage files untouched.

Question 16: Which Structured Streaming output sink writes results to an in-memory table that can be queried with SQL during development and testing?

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

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 17: What is the role of data versioning in lakehouse systems?

To increase file sizes
To encrypt all metadata
To slow down query speeds
To track and revert changes (Correct answer)

Correct answer: To track and revert changes

Data versioning in lakehouse systems provides a historical record of all changes made to the data over time. This capability is essential for auditing, compliance, and debugging, as it allows users to track data modifications, understand data lineage, and revert to previous data states if errors or inconsistencies occur.

Question 18: Which of the following is a fundamental principle of unity catalog & data governance as it applies to Lakehouse Fundamentals Certification?

Avoiding documentation to streamline workflow efficiency
Systematic evaluation and adherence to established industry standards (Correct answer)
Prioritizing speed of completion over accuracy and compliance
Relying solely on personal experience without reference to guidelines

Correct answer: Systematic evaluation and adherence to established industry standards

A fundamental principle of unity catalog & data governance in Lakehouse Fundamentals Certification is the systematic evaluation and adherence to established industry standards, which ensures consistency, quality, and regulatory compliance across all professional activities.

Question 19: Which Databricks SQL syntax correctly creates a view that is visible only within the current session?

CREATE TEMPORARY VIEW vw AS SELECT ... (Correct answer)
CREATE TRANSIENT VIEW vw AS SELECT ...
CREATE SESSION VIEW vw AS SELECT ...
CREATE LOCAL VIEW vw AS SELECT ...

Correct answer: CREATE TEMPORARY VIEW vw AS SELECT ...

CREATE TEMPORARY VIEW creates a session-scoped view that is not persisted to the metastore and disappears when the session ends.

Question 20: Which cluster configuration setting directly controls whether Photon is enabled on a Databricks cluster?

spark.executor.photon.mode
spark.sql.vectorized.reader.enabled
databricks.runtime.photon=true
spark.databricks.photon.enabled (Correct answer)

Correct answer: spark.databricks.photon.enabled

The spark.databricks.photon.enabled configuration property enables or disables the Photon native execution engine on a cluster.

Question 21: What is the primary purpose of Photon in the Databricks Lakehouse platform?

To provide a drag-and-drop interface for building ETL pipelines
To manage cross-region replication of Delta tables
To accelerate SQL and DataFrame workloads using a native vectorized C++ execution engine (Correct answer)
To automatically tune hyperparameters for machine learning models

Correct answer: To accelerate SQL and DataFrame workloads using a native vectorized C++ execution engine

Photon is Databricks' native vectorized query engine written in C++ that speeds up SQL and DataFrame operations by processing data in batches of columns rather than row-by-row.

Question 22: In Unity Catalog, what is 'Delta Sharing' used for?

Broadcasting streaming data to multiple downstream Spark jobs
Synchronizing schema changes across multiple workspaces
Securely sharing live data with external organizations without copying it (Correct answer)
Replicating Delta tables between AWS regions for disaster recovery

Correct answer: Securely sharing live data with external organizations without copying it

Delta Sharing is an open protocol that lets organizations share read-only access to live Delta tables with external recipients without data duplication.

Question 23: In the context of Lakehouse architecture, what does 'data skipping' rely on to avoid reading unnecessary files?

Bloom filters embedded inside the Hive metastore
Column-level min/max statistics stored in the Delta Lake transaction log (Correct answer)
Row-level access control policies defined in Unity Catalog
Partition pruning based on the HDFS directory structure

Correct answer: Column-level min/max statistics stored in the Delta Lake transaction log

Delta Lake stores min/max statistics for each column per data file in the transaction log, allowing the query engine to skip files that cannot contain matching rows.

Question 24: In a Medallion Architecture, what is the primary transformation applied when moving data from Bronze to Silver?

Creating machine learning feature stores
Aggregating metrics into KPIs
Partitioning data by business domain
Deduplicating, cleansing, and conforming data (Correct answer)

Correct answer: Deduplicating, cleansing, and conforming data

The Bronze-to-Silver step focuses on deduplication, null handling, type casting, and conforming schemas to produce trusted, clean records.

Question 25: Which of the following is a fundamental principle of medallion architecture layers as it applies to Lakehouse Fundamentals Certification?

Relying solely on personal experience without reference to guidelines
Avoiding documentation to streamline workflow efficiency
Systematic evaluation and adherence to established industry standards (Correct answer)
Prioritizing speed of completion over accuracy and compliance

Correct answer: Systematic evaluation and adherence to established industry standards

A fundamental principle of medallion architecture layers in Lakehouse Fundamentals Certification is the systematic evaluation and adherence to established industry standards, which ensures consistency, quality, and regulatory compliance across all professional activities.

Question 26: In a streaming Medallion pipeline, data flows from Kafka into Bronze continuously. Which Spark API is typically used for this ingestion pattern?

Structured Streaming with Auto Loader or readStream (Correct answer)
Spark SQL batch INSERT
COPY INTO command in batch mode
Spark RDD textFile()

Correct answer: Structured Streaming with Auto Loader or readStream

Structured Streaming (with Auto Loader for file sources or readStream for Kafka) enables continuous, low-latency ingestion into Bronze Delta tables.

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

45 min

Time Limit

70%

To Pass

Checking for saved progress...

Exam Rules

You can skip questions and return to them later
Flag questions for review before submitting
No feedback shown until you submit the entire exam
Unanswered questions count as wrong — answer everything
10 pretest questions are mixed in and don't affect your score
Timer auto-submits when time runs out
Your progress is auto-saved every 30 seconds