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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Connecting to external data sources 3. Delta Lake basics and usage - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation
Topic 2: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles - Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery - Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management
Topic 3: Data Governance & Quality	11%	- Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks - Unity Catalog implementation 1. Data governance model 2. Permissions and access control
Topic 4: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Compute options: clusters, SQL warehouses, serverless 2. Data layout and optimization: partitioning, file sizing, caching 3. Workspace navigation and management
Topic 5: Data Processing & Transformations	31%	- Data transformation techniques 1. DataFrame operations and transformations 2. Complex data processing logic 3. Aggregations, joins, and window functions - Query optimization and performance 1. Optimization strategies 2. Understanding query plans

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q287-Q292):

NEW QUESTION # 287
A data engineer has multiple Unity Catalog-managed Delta tables that require regular maintenance. Currently, the data engineer manually schedules OPTIMIZE and VACUUM jobs for each table, adjusting the frequency based on how often each table is queried and updated. The engineer needs to eliminate this manual maintenance overhead and have the platform automatically determine when and how to run these operations. Which action should the engineer take to meet the requirements?

A. Set the delta.autoOptimize.autoCompact and delta.autoOptimize.optimizeWrite table properties to trigger automatic VACUUM and file compaction.
B. Configure Auto Loader with schema evolution enabled to automatically trigger OPTIMIZE and VACUUM after each data ingestion batch completes.
C. Enable predictive optimization at the account, catalog, or schema level so the platform automatically identifies and runs OPTIMIZE and VACUUM on managed tables.
D. Enable Liquid Clustering with CLUSTER BY on each table to automatically compact and remove stale data files on a scheduled basis.

Answer: C

Explanation:
Predictive optimization automatically determines when Unity Catalog-managed tables need maintenance and runs operations such as OPTIMIZE and VACUUM. It can be enabled at the account, catalog, schema, or table level.

NEW QUESTION # 288
A data engineer is facing performance bottlenecks in an e-commerce transactions Delta table.
The table is a managed Unity Catalog table, and it uses partitioning and Z-ordering in its data layout scheme. The predictive optimization for Unity Catalog tables is also enabled. The table has a frequently changing query filter, and the data engineer does not observe a benefit of the Data Layout or the Predictive Optimization. How should the data engineer fix the data layout bottlenecks?

A. Enable Delta Caching so that query results can be read through caches.
B. Tweak the Z-Order columns and run OPTIMIZE manually.
C. Re-write the Data Layout with Liquid Clustering and cluster by the Z-Ordered columns.
D. Switch the Data layout from Partition+Z-Ordering to Automatic Liquid Clustering.

Answer: D

Explanation:
When queries use frequently changing filter conditions, partitioning and Z-ordering lose effectiveness because they rely on fixed columns. The correct solution is to switch to Automatic Liquid Clustering, which dynamically reorganizes data based on observed query patterns, removing the need for manual Z-ordering or static partitioning and resolving the data layout bottleneck.

NEW QUESTION # 289
A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

A. if day_of_week = 1 and review_period:
B. if day_of_week == 1 and review_period == "True":
C. if day_of_week == 1 and review_period:
D. if day_of_week = 1 and review_period = "True":
E. if day_of_week = 1 & review_period: = "True":

Answer: C

Explanation:
In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. Reference: [Python Operators], [Python If ... Else]

NEW QUESTION # 290
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger("5 seconds")
B. trigger(continuous="5 seconds")
C. trigger(processingTime="5 seconds")
D. trigger()
E. trigger(once="5 seconds")

Answer: C

Explanation:
Explanation
# ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream \
format("console") \
trigger(processingTime='2 seconds') \
start()
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers

NEW QUESTION # 291
A data engineer is writing Spark code to group sales data by region and calculate total revenue for each region. Which Spark DataFrame transformation performs grouping operations?

A. orderBy
B. select
C. groupBy
D. filter

Answer: C

NEW QUESTION # 292
......

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