[NEW] Databricks Data Engineer Associate

Detailed Exam Domain CoverageTo pass the Databricks Certified Data Engineer Associate exam, you need to master the platform's core architecture and programmatic paradigms. This practice test bank maps directly to the official curriculum across these five core domains:Databricks Intelligence Platform (10%)Workspace architecture, clusters, and multi-cloud environmentsCore platform capabilities (Notebooks, repos, data storage concepts)Development and Ingestion (30%)Building scalable ETL pipelines using Spark SQL and PySparkIncremental data extraction and ingestion using Auto Loader and COPY INTORegistering and deploying custom User-Defined Functions (UDFs)Data Processing Transformations (31%)Advanced data transformation techniques (deduplication, filtering, joins)Handling complex nested data types (Arrays, Maps, Structs)Leveraging Delta Lake features, ACID transactions, and history trackingProductionizing Data Pipelines (18%)Orchestrating multi-task jobs via Databricks WorkflowsConfiguring cron schedules, triggers, and task dependenciesDeploying Delta Live Tables (DLT) for automated streaming pipelinesData Governance Quality (11%)Managing securable objects using Unity Catalog (Catalogs, Schemas, Tables, Views)Enforcing data quality constraints and monitoring pipeline integrityApplying security and compliance features (row-level security, column masking)Course DescriptionPassing the Databricks Certified Data Engineer Associate exam requires more than just memorizing definitions. The actual exam tests your ability to read code snippets, troubleshoot pipeline failures, choose the right optimization strategy, and understand how Delta Lake behaves under the hood. I designed this course to bridge the gap between basic platform knowledge and the scenario-based problem-solving you will face on exam day.I spent months crafting an original, comprehensive question bank that mirrors the distribution, phrasing, and technical rigor of the actual Databricks certification. Every single question comes paired with a deep-dive explanation. I do not just tell you which answer is correct; I systematically break down why the correct choice is optimal and why the other alternatives are incorrect or sub-optimal in production environments. This method ensures you learn how to identify architectural traps and eliminate distractors quickly during the timed test.Instead of generic theoretical questions, you will encounter real-world engineering challenges: choosing between Auto Loader and COPY INTO for specific cloud storage structures, debugging PySpark transformations, optimizing Delta tables with Z-ORDER, and managing securable objects via Unity Catalog. By practicing with questions built at this level of depth, you will walk into the testing center confident in your technical reasoning and fully prepared to pass on your first attempt.Practice Questions PreviewBelow are three sample questions that demonstrate the depth and structure of the questions found inside this course.Question 1: Data Ingestion ArchitectureA data engineering team needs to configure an ingestion pipeline that reads millions of JSON files continuously from an AWS S3 bucket. The incoming files have schemas that evolve over time as upstream systems add new fields. The pipeline must ingest these records incrementally without scanning the entire directory repeatedly and must automatically infer and update the target schema. Which approach best satisfies these requirements?A) Use spark.read.format("json").load("s3a://my-bucket/data/") scheduled on a 5-minute cron job.B) Use Delta Live Tables with a standard COPY INTO command inside a Python notebook.C) Use spark.readStream.format("cloudFiles").option("cloudFiles.format", "json").load("s3a://my-bucket/data/")D) Use a Spark SQL query executing SELECT * FROM json.s3a://my-bucket/data/`` with schema evolution enabled in Spark configurations.E) Set up an external Hive Metastore pointing to the S3 path and execute MSCK REPAIR TABLE before every read.F) Convert the JSON files to Parquet format using an AWS Lambda function before reading them into Databricks using a static DataFrame.Answer and Explanation Breakdown:Correct Answer: CExplanation for Option C (Correct): Option C correctly implements Databricks Auto Loader by using the cloudFiles format in a streaming DataFrame. Auto Loader is designed specifically for this scenario: it scales efficiently to millions of files by tracking new arrivals incrementally (using either directory listing or file notification modes), automatically infers the schema, and handles schema evolution out-of-the-box without requiring manual adjustments or full directory rescans.Explanation for Option A (Incorrect): This approach creates a static DataFrame that scans the entire S3 directory every 5 minutes. As the number of files grows into the millions, directory listing performance degrades exponentially, making this highly inefficient and expensive. It does not provide automated streaming or efficient incremental ingestion.Explanation for Option B (Incorrect): While COPY INTO is an incremental command, it is a SQL command meant for batch loading, not a continuous streaming format natively used within Delta Live Tables for real-time streaming ingestion. Auto Loader (cloudFiles) is the recommended best practice for DLT continuous ingestion when dealing with evolving schemas.Explanation for Option D (Incorrect): A direct ad-hoc SQL select statement over a raw path creates a static query. It cannot handle incremental tracking or automatic schema evolution on its own without re-evaluating the entire directory every single time the query runs.Explanation for Option E (Incorrect): MSCK REPAIR TABLE is used to update partition metadata in traditional Hive tables when directories change manually. It is slow, prone to performance bottlenecks over large storage structures, and does not handle schema evolution or stream-based incremental ingestion.Explanation for Option F (Incorrect): Introducing an external AWS Lambda function adds unnecessary architectural complexity and cloud costs. Databricks can process raw JSON files natively and efficiently at scale using Auto Loader, making an external pre-conversion pipeline completely redundant.Question 2: Delta Lake OptimizationYou notice that a critical downstream dashboard query is running slowly when filtering a massive Delta Lake table by both customer_id and transaction_date. The table undergoes frequent append operations throughout the day. Which command should you run periodically to optimize read performance for this multi-column filtering pattern?A) ANALYZE TABLE my_table COMPUTE STATISTICS FOR ALL COLUMNS;B) OPTIMIZE my_table ZORDER BY (customer_id, transaction_date);C) ALTER TABLE my_table SET TBLPROPERTIES ('delta.autoOptimize.optimizeWrite' = 'true');D) VACUUM my_table RETAIN 0 HOURS;E) REFRESH TABLE my_table;F) OPTIMIZE my_table WHERE transaction_date current_date() - 30;Answer and Explanation Breakdown:Correct Answer: BExplanation for Option B (Correct): The OPTIMIZE command with ZORDER BY co-locates column information in the same set of files. By clustering data along both customer_id and transaction_date, Databricks can skip files that do not match the query filters (Data Skipping), drastically reducing I/O and accelerating query response times for multi-column patterns.Explanation for Option A (Incorrect): Computing statistics helps the Spark cost-based optimizer choose efficient join strategies, but it does not physically reorganize or compact the layout of files on storage to improve physical data skipping during file reads.Explanation for Option C (Incorrect): Auto Optimize manages file sizes during write actions (compacting into 128MB files), but it does not automatically perform multi-dimensional clustering (Z-ORDER). You must still execute OPTIMIZE with ZORDER BY to handle the specific multi-column filtering requirement.Explanation for Option D (Incorrect): VACUUM removes data files that are no longer referenced by the latest version of the Delta table log. It saves storage space but does not optimize or reorganize the active files used by current queries. Furthermore, setting retention to 0 hours requires overriding safety protocols and risks corrupting concurrent reads.Explanation for Option E (Incorrect): REFRESH TABLE invalidates the cached metadata of a table in the Spark session. It ensures that the session recognizes updates, but it does not change the physical organization of data files to optimize read speeds.Explanation for Option F (Incorrect): While this filters the optimization space to the last 30 days, it lacks the mandatory ZORDER BY clause required to organize the files by both the specific columns (customer_id and transaction_date) needed for the dashboard's query pattern.Question 3: Unity Catalog GovernanceA data engineer needs to grant a specific analyst group read-only access to a table named trusted_transactions housed within the finance schema of a catalog named production. Following the principle of least privilege, which SQL statement sequence must be executed?A) GRANT SELECT ON TABLE production. finance.trusted_transactions TO analyst_group;B) GRANT USAGE ON CATALOG production TO analyst_group; GRANT USAGE ON SCHEMA production. finance TO analyst_group; GRANT SELECT ON TABLE production.f inance.trusted_transactions TO analyst_group;C) GRANT ALL PRIVILEGES ON TABLE production. finance.trusted_transactions TO analyst_group;D) GRANT USE CATALOG ON CATALOG production TO analyst_group; GRANT USE SCHEMA ON SCHEMA production. finance TO analyst_group;E) GRANT SELECT ON SCHEMA production. finance TO analyst_group;F) GRANT OWNERSHIP ON TABLE production. finance.trusted_transactions TO analyst_group;Answer and Explanation Breakdown:Correct Answer: BExplanation for Option B (Correct): Unity Catalog uses a explicit hierarchical security model. To query an object inside a catalog and schema, a principal must possess the USAGE privilege on both the parent catalog and parent schema, alongside the specific privilege (like SELECT) on the target object itself. Without USAGE on the parent containers, the analyst group cannot traverse the metadata hierarchy to access the table.Explanation for Option A (Incorrect): While this statement attempts to grant access to the table, it will fail in practice because the analysts will be blocked at the catalog level. They require explicit USAGE rights on both the production catalog and the finance schema to actually see or read the table.Explanation for Option C (Incorrect): This violates the principle of least privilege by granting full administrative powers (such as modifying schemas or deleting the table) instead of restricting the group purely to read-only (SELECT) access.Explanation for Option D (Incorrect): Unity Catalog SQL syntax uses USAGE rather than USE CATALOG or USE SCHEMA to establish traversal permissions. Even if the keywords were valid, this sequence fails to grant the actual SELECT permission on the table itself.Explanation for Option E (Incorrect): Granting a privilege on an entire schema applies broadly. While it can grant read access to everything via inheritance in some configurations, it violates the strict instruction to grant access explicitly to the specific table trusted_transactions, thereby over-provisioning access to other potentially sensitive tables within the finance schema.Explanation for Option F (Incorrect): Granting OWNERSHIP gives the analyst group full control over the table, including the ability to drop it or alter its structure. This completely violates read-only and least-privilege requirements.Welcome to the Mock Exam Practice Tests Academy to help you prepare for your Databricks Certified Data Engineer Associate exam.You can retake the exams as many times as you wantThis is a huge original question bankYou get support from instructors if you have questionsEach question has a detailed explanationMobile-compatible with the Udemy appWe hope that by now you're convinced! And there are a lot more questions inside the course.