When you sit a Databricks certification exam, knowing exactly what kinds of questions to expect beforehand is a huge advantage. This article walks through sample questions modeled on the real format of all 7 exams, each with a detailed answer explanation.
Get familiar with all three question types — Single Choice, Multiple Response, and code-reading — to sharpen your exam-day preparation. Start by working through the questions to gauge your current level and identify weak spots.
All 7 Databricks exams share the same three question formats. Let's review the characteristics and strategy for each.
The dominant format, accounting for about 70% of the exam. You pick one correct answer from four options. Question stems appear in Japanese on Japanese-language exams, but code snippets remain in English. One or two options are typically obviously wrong, so elimination is highly effective.
Questions with two or more correct answers. The number to select is always specified — "Select 2", "Select 3", and so on. There is no partial credit; you only earn the point when every selection is correct. A solid strategy is to lock in the answers you are certain about first, then weigh the remaining options against each other.
You are shown a PySpark, Spark SQL, or Delta Lake snippet and asked to pick the correct output or behavior. The share of code questions varies by exam, as shown below.
| Exam | Share of Code Questions | Code Topics Covered |
|---|---|---|
| Spark Developer Associate | ~30-35% | PySpark DataFrame API, Spark SQL, Structured Streaming |
| Data Engineer Associate (DEA) | ~10-15% | Delta Lake DML, Auto Loader configuration, DLT pipelines |
| Data Engineer Professional (DEP) | ~15-20% | Advanced Spark SQL, Delta Lake optimization, streaming |
| Machine Learning Associate (MLA) | ~10-15% | MLflow API, Feature Store, scikit-learn integration |
| GenAI Engineer Associate | ~10% | LangChain, Vector Search API, embedding configuration |
| Data Analyst Associate (DAA) | ~5-10% | SQL window functions, CTE queries |
| Machine Learning Professional (MLP) | ~15-20% | Pandas UDFs, distributed training code, MLOps pipelines |
DEA tests fundamentals of Delta Lake, ELT, and data pipelines. Auto Loader and MERGE INTO are recurring topics.
DEA - Auto Loader
問題 1
When you use Auto Loader to ingest JSON files from cloud storage with schema inference enabled, which statement about schema evolution is correct?
正解: B
Auto Loader's schema inference (cloudFiles.inferColumnTypes = true) stores schema information as JSON in the directory specified by schemaLocation. When a new column is detected, the default behavior (outside of rescuedDataColumn) stops the stream and surfaces the schema change for user confirmation. Option A — "automatically updates the schema" — only applies when mergeSchema=true is explicitly set. Option C is wrong because Auto Loader supports JSON, CSV, Parquet, Avro, and more. Option D is wrong because type inference does take place.
DEA - MERGE INTO
問題 2
When you write a MERGE INTO statement against a Delta Lake table to update existing records and insert new ones, what is the correct way to add an extra condition to the WHEN MATCHED clause?
正解: A
To add an extra condition to a WHEN MATCHED clause in MERGE INTO, use the "WHEN MATCHED AND <condition> THEN ..." syntax. This lets you further narrow the matched rows before applying UPDATE or DELETE. A WHERE clause (options B and D) is not valid inside a WHEN clause of a MERGE statement. The IF keyword (option C) also does not exist in MERGE syntax. UPDATE SET * is shorthand for updating every column in the target with the matching columns from the source.
MLA tests knowledge of MLflow, Feature Store, and AutoML. MLflow experiment tracking and feature engineering are central topics.
MLA - Feature Store
問題 3
When you create a feature table with Feature Engineering in Unity Catalog (formerly Feature Store), what is the correct way to set up automatic sync to an online store?
正解: C
With Feature Engineering in Unity Catalog (formerly Feature Store), you publish a feature table to an online store (DynamoDB, Cosmos DB, and so on) using the fe.publish_table() method. You provide the online store name, endpoint, and authentication credentials at publish time. Option A is wrong because there is no online_store_spec parameter on create_table(). Option B is wrong because TBLPROPERTIES does not configure online store sync. Option D is wrong because CREATE ONLINE TABLE is not Databricks SQL syntax.
Spark Developer is defined by its DataFrame API code questions, and it also expects you to understand how the Catalyst Optimizer behaves.
Spark - Catalyst Optimizer
問題 4
For the following PySpark code, which statement about the execution plan after Catalyst Optimizer is correct? df = spark.read.parquet("/data/sales") result = df.filter(df.amount > 100).select("product_id", "amount").filter(df.region == "JP")
正解: B
During its logical optimization phase, Catalyst Optimizer combines multiple Filter conditions (CombineFilters) and uses Predicate Pushdown to push filters down to the data source. The Parquet format supports column-level reads (Column Pruning), so only product_id and amount from the select, plus region from the filter, are read off disk. Option A is wrong because the engine does not execute the operations in literal source order — Catalyst Optimizer produces an optimized plan. Option D is wrong because Parquet fully supports predicate pushdown.
GenAI Engineer is the newest exam, covering RAG, Vector Search, and LLM application development.
GenAI - Vector Search
問題 5
Which statement about the Delta Sync Index in Databricks Vector Search is correct?
正解: A
The Delta Sync Index uses Change Data Feed to automatically detect INSERT/UPDATE/DELETE operations on the source Delta Lake table and incrementally update the vector index. Each table update keeps the index in sync automatically, so no manual reindexing is required. Option B is wrong because latency comparisons with the Direct Vector Access Index are not uniform. Option C is wrong because the compute_embeddings option can delegate embedding computation to a Databricks embedding model. Option D is wrong because deleting the source table invalidates the index.
On Databricks Single Choice questions, one or two options are usually obviously wrong. The most effective strategy is to eliminate those first and decide between the remaining two. On code questions in particular, knock out options with syntax errors before anything else.
For code-reading questions, do not rush to take in the whole snippet at once. The most reliable approach is to trace the data line by line from the top. Pay especially close attention to how row and column counts change around groupBy and join operations.
On Databricks exams, several options can be technically correct. When that happens, your job is to pick the one that is "most appropriate" or "most aligned with best practices." Knowing the official documentation's recommendations and Databricks best practices is the key to scoring well on these questions.
Associate exams give you 45 questions in 90 minutes (about 2 minutes each); Professional exams give you 59 questions in 120 minutes (also about 2 minutes each). Flag and skip questions you are unsure about, and clear the ones you know first. Ideally, save 10-15 minutes at the end to review your answers.
What question formats appear on Databricks exams?
Databricks exams use three question formats. Single Choice questions make up roughly 70% of the exam, Multiple Response questions about 20%, and code-reading questions about 10%. There are no drag-and-drop or hands-on lab components. Code questions present PySpark, Spark SQL, or Delta Lake snippets and ask you to pick the correct output or behavior. On the Spark Developer exam, the share of code questions rises to roughly 30-35%.
Do Multiple Response questions tell you how many answers are correct?
Yes. Multiple Response questions explicitly state how many answers to choose ("Select 2", "Select 3", and so on). The UI will not let you select more than the specified number. There is no partial credit — you only get the point if every selection is correct. The most effective strategy is to eliminate obviously wrong options first, then compare the remaining candidates.
Is there a difficulty gap between the sample questions and the real exam?
The sample questions in the official Practice Exam are calibrated to match real exam difficulty. The catch is that Databricks only publishes one set per exam (about 45 questions), so the coverage of question patterns is limited. The real exam often includes scenario-based questions drawn from production work and applied questions that combine multiple concepts. Use the official sample questions to learn the format, then drill a wider range of patterns with a question bank.
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