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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q138-Q143):

NEW QUESTION # 138
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

A. SELECT * FROM my_table WHERE age > 25;
B. DELETE FROM my_table WHERE age > 25;
C. DELETE FROM my_table WHERE age <= 25;
D. UPDATE my_table WHERE age > 25;
E. UPDATE my_table WHERE age <= 25;

Answer: B

NEW QUESTION # 139
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory.
The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

A. Auto Loader
B. Data Explorer
C. Databricks SQL
D. Unity Catalog
E. Delta Lake

Answer: A

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage.
Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 140
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

A. The OPTIMIZE command was nun on the table
B. The DELETE HISTORY command was run on the table
C. The VACUUM command was run on the table
D. The TIME TRAVEL command was run on the table
E. The HISTORY command was run on the table

Answer: C

NEW QUESTION # 141
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would respond more quickly to ad-hoc requests
B. Both teams would autoscale their work as data size evolves
C. Both teams would reorganize to report to the same department
D. Both teams would be able to collaborate on projects in real-time
E. Both teams would use the same source of truth for their work

Answer: E

Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. References: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM

NEW QUESTION # 142
A data engineer investigates a slow stage in the Databricks Spark UI. The engineer scrolls down to the Summary Metrics section for task durations, and they observe for that stage:
25th percentile duration: 40 seconds
Median duration: 45 seconds
75th percentile duration: 50 seconds
Max duration: 80 seconds
What is the interpretation of these metrics?

A. The stage likely suffers from data skew.
B. There is a GC issue.
C. The stage is healthy.
D. The cluster is under-provisioned.

Answer: A

Explanation:
The maximum task duration is 60% higher than the 75th percentile (80s versus 50s). Databricks identifies this pattern as a likely indication that a small number of tasks process disproportionately large partitions-data skew.

NEW QUESTION # 143
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