Databricks-Certified-Data-Engineer-Associate Latest Exam Format & Databricks-Certified-Data-Engineer-Associate Pass Exam

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

Section	Weight	Objectives
Topic 1: Apache Spark Data Processing Fundamentals	20-25%	- Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Apply transformations and actions on DataFrames - Create and use Spark DataFrames
Topic 2: Python for Data Engineering	10-15%	- Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing
Topic 3: Data Pipeline Architecture	15-20%	- Implement incremental data processing - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns
Topic 4: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits
Topic 5: Delta Lake Fundamentals	20-25%	- Create and manage Delta tables - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Write to and read from Delta tables
Topic 6: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Aggregate and group data - Join and union DataFrames - Write and execute Spark SQL queries

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q244-Q249):

NEW QUESTION # 244
A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution. Which compute option should the data engineer use?

A. Databricks Jobs
B. Databricks Runtime for ML
C. Databricks SQL Analytics
D. Serverless SQL Warehouse

Answer: D

Explanation:
A Serverless SQL Warehouse automatically scales to handle fluctuating workloads, requires no infrastructure management, and charges only for the compute used during query execution, making it cost-efficient for large datasets.

NEW QUESTION # 245
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

A. PII
B. There is no way to indicate whether a table contains PII.
C. "COMMENT PII"
D. TBLPROPERTIES PII
E. COMMENT "Contains PII"

Answer: E

Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

NEW QUESTION # 246
A data engineer needs to optimize the data layout and query performance for an e-commerce transactions Delta table. The table is partitioned by "purchase_date" a date column which helps with time-based queries but does not optimize searches on user statistics "customer_id", a high- cardinality column.
The table is usually queried with filters on "customer_id" within specific date ranges, but since this data is spread across multiple files in each partition, it results in full partition scans and increased runtime and costs.
How should the data engineer optimize the Data Layout for efficient reads?

A. Enable delta caching on the cluster so that frequent reads are cached for performance.
B. Alter the table to partition by "customer_id".
C. Alter the table implementing liquid clustering by "customer_id" and "purchase_date".
D. Alter table implementing liquid clustering on "customer_id" while keeping the existing partitioning.

Answer: C

Explanation:
Since queries filter by customer_id within purchase_date ranges, partitioning only by purchase_date causes inefficient scans across partitions. The correct optimization is to use Liquid Clustering on both customer_id and purchase_date, which dynamically reorganizes data to optimize for both dimensions and avoids the drawbacks of high-cardinality static partitioning.

NEW QUESTION # 247
What Databricks feature can be used to check the data sources and tables used in a workspace?

A. Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks,
B. Use the lineage feature to visualize a graph that highlights where the table is used only in reports.
C. Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.
D. Do not use the lineage feature as it only tracks activity from the last 3 months and will not provide full details on dependencies.

Answer: C

NEW QUESTION # 248
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?

A. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
B. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
C. Records that violate the expectation cause the job to fail.
D. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.

Answer: D

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 # 249
......

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