Landing data with Auto Loader, and handling schema enforcement and evolution
Auto Loader (cloudFiles) incrementally and efficiently processes new files landing in cloud storage, using either directory listing or file notification mode to discover files. It enforces a stored schema to prevent bad data from corrupting a table, and supports schema evolution to safely adapt when new columns appear, all while writing into Unity Catalog-governed managed or external tables.
Must-know
- Auto Loader uses cloudFiles as the streaming source format, with cloudFiles.format specifying the underlying file type (json, csv, parquet, etc.).
- Directory listing mode incrementally lists cloud storage directories to detect new files and requires no extra cloud service setup, making it the simpler default option.
- File notification mode uses cloud provider queue/notification services (e.g., subscribing to storage event notifications) to detect new files, scaling better for very large or high-volume directories but requiring additional cloud resource permissions.
- Auto Loader infers and stores schema in a schema location, and cloudFiles.inferColumnTypes plus a checkpoint/schema directory let it track schema across runs so it does not re-infer every batch.
- cloudFiles.schemaEvolutionMode controls behavior when new columns are detected: default mode fails the stream and requires a restart to pick up the new schema (addNewColumns), while other modes can rescue data, ignore new columns, or fail explicitly.
- Unrecognized or mismatched columns are captured in a rescued data column (by default _rescued_data) rather than silently dropped, preserving data integrity.
- Even though Auto Loader is a streaming source, it can be run in batch/triggered fashion using trigger(availableNow=True) (or Trigger.Once) to process all currently available new files and then stop, which is the pattern typically used to land data incrementally into Unity Catalog tables via writeStream.table() or equivalent.
A batch Auto Loader job ingests Parquet files nightly using Trigger.AvailableNow into a Unity Catalog table. The source directory receives thousands of new files per run, and the workspace has no cloud provider notification services (e.g., SQS/Event Grid) configured. Which file discovery mode should be used, and what is its main tradeoff?
What you have tried across Databricks DEA's objectives, not a readiness score.
Databricks Intelligence Platform
Data Ingestion and Loading
- Batch, streaming, and incremental loading patterns, and where the data comes from
- Loading files from cloud storage into governed tables with COPY INTO
- Landing data with Auto Loader, and handling schema enforcement and evolution
- Setting up Lakeflow Connect to ingest from enterprise sources reliably
- Pulling data through JDBC, ODBC, or REST clients and scheduling the job
- Choosing the right ingestion method for a given volume, frequency, and governance need
- Bringing semi-structured and unstructured data into governed Delta tables
Data Transformation and Modeling
- Cleaning bronze data into silver tables with PySpark and SQL
- Joining and combining DataFrames with the different join and union types
- Reshaping columns, rows, and arrays in a table
- Deduplicating and aggregating DataFrames
- Tuning Spark's core parameters and measuring what changed
- Building Gold-layer views and tables for BI and analytics
- Validating Silver and Gold datasets for quality
Working with Lakeflow Jobs
Implementing CI/CD
- Branching, committing, and opening pull requests from inside the Databricks workspace
- Promoting one codebase across dev, test, and prod with bundle variables and overrides
- Packaging and deploying jobs and pipelines with Automation Bundles
- Validating and managing bundle deployments from the Databricks CLI
Troubleshooting, Monitoring, and Optimization
- Spotting performance trends in a job's run history
- Reading job status, task graphs, and failure rates to monitor pipeline health
- Diagnosing skew, shuffle, and spill from Spark UI stage metrics
- What Liquid Clustering and predictive optimization actually do
- Diagnosing cluster startup failures, library conflicts, and out-of-memory errors
Governance and Security
Coverage checked against the published exam guide on Jul 26, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.