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| Section | Objectives |
|---|---|
| Data Governance and Quality | - Data quality concepts and management - Data access control and governance - Unity Catalog basics |
| Data Processing and Transformations | - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) |
| Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
| Productionizing Data Pipelines | - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization |
| Data Ingestion and ELT Development | - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data |
>> Databricks-Certified-Data-Engineer-Associate Test Quiz <<
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NEW QUESTION # 201
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
Answer: C
Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/
NEW QUESTION # 202
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark Ul and can see the ERRORS as follows:
Which two corrective actions should the data engineer perform to resolve this issue?
Choose 2 answers - (Q) Narrow the filters in order to collect less data in the query
Answer: B,C
NEW QUESTION # 203
A data engineer inherits a Delta table with historical partitions by country that are badly skewed. Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve.
Which two actions should the data engineer take? (Choose 2)
Answer: C,E
Explanation:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.
Reference Source: Databricks Delta Lake Guide - "Use Liquid Clustering for Tables" and "OPTIMIZE Command for File Compaction and Data Layout."
NEW QUESTION # 204
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?
Answer: A
Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
:
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 205
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?
Answer: D
NEW QUESTION # 206
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