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

SectionWeightObjectives
Development and Ingestion30%- Notebook development fundamentals
  • 1. Using PySpark and Spark SQL
  • 2. Data exploration and validation
- Data ingestion patterns and methods
  • 1. Batch and streaming ingestion
  • 2. Delta Lake basics and usage
  • 3. Connecting to external data sources
Data Governance & Quality11%- Data quality and reliability
  • 1. Data validation and quality checks
  • 2. Schema enforcement and evolution
- Unity Catalog implementation
  • 1. Permissions and access control
  • 2. Data governance model
Data Processing & Transformations31%- Query optimization and performance
  • 1. Understanding query plans
  • 2. Optimization strategies
- Data transformation techniques
  • 1. DataFrame operations and transformations
  • 2. Complex data processing logic
  • 3. Aggregations, joins, and window functions
Productionizing Data Pipelines18%- Deployment and CI/CD
  • 1. Version control integration
  • 2. Databricks Asset Bundles
- Workflow orchestration
  • 1. Scheduling, triggers, and dependencies
  • 2. Lakeflow Jobs creation and management
- Monitoring and troubleshooting
  • 1. Pipeline reliability and recovery
  • 2. Logging and error handling
Databricks Intelligence Platform10%- 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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

Answer: C


NEW QUESTION # 15
A data engineering project involves processing large batches of data on a daily schedule using ETL. The jobs are resource-intensive and vary in size, requiring a scalable, cost-efficient compute solution that can automatically scale based on the workload.
Which compute approach will satisfy the needs described?

Answer: B


NEW QUESTION # 16
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

Answer: E

Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id,name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
References: The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.


NEW QUESTION # 17
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 code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

Answer: E


NEW QUESTION # 18
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?

Answer: A

Explanation:
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
References:
* 1: OPTIMIZE | Databricks on AWS
* 2: REDUCE | Databricks on AWS
* 3: repartition | Databricks on AWS
* 4: VACUUM | Databricks on AWS


NEW QUESTION # 19
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