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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
A data engineer is investigating a Lakehouse ETL job on Databricks that has suddenly become four times slower.
They need to:
- Identify the specific Spark stages that regressed.
- Drill down to per-task metrics such as shuffle read/write size,
spill, and GC time.
- Correlate these with the executor resource usage during that run.
Which Databricks-native capability is the starting point for this detailed job-level root-cause analysis?

A. Ganglia metrics for the cluster, which provide full visibility into Spark stages, tasks, and shuffle metrics for the job run.
B. Cluster Metrics (Metrics tab on the compute) to view historical Spark and hardware metrics, without using the Spark UI.
C. Spark UI opened from the job run's compute, using the Jobs and Stages tabs to inspect task metrics.
D. The Jobs run details page timeline and graph views, without opening the Spark UI, to analyze all stage and task-level Spark metrics.

Answer: C

Explanation:
The Spark UI provides detailed visibility into job execution, including stages, tasks, and granular metrics such as shuffle I/O, spill, and GC time, making it the primary tool for deep root-cause analysis of performance regressions.

NEW QUESTION # 46
A data engineer is designing a Databricks Job that includes a Notebook task for data cleansing and a SQL Query task for generating a summary report. The summary report must only execute after the cleansing task completes successfully. How should the dependency be configured in the Lakeflow Jobs task graph?

A. Configure the cleansing task to use an 'On Failure' trigger to start the summary report.
B. Select the cleansing task as a 'Run if' dependency in the summary report task configuration.
C. Set both tasks to run on the same schedule with a five-minute start delay for the report.
D. Place both tasks in the same notebook and use a standard Python function call.

Answer: B

Explanation:
Configure the summary report task to depend on the cleansing task and use the default Run if: All succeeded condition. This ensures the SQL task runs only after the notebook task completes successfully.

NEW QUESTION # 47
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. Parquet files have a well-defined schema
B. Parquet files will become Delta tables
C. Parquet files can be partitioned
D. Parquet files have the ability to be optimized
E. CREATE TABLE AS SELECT statements cannot be used on files

Answer: A

Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.
References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV

NEW QUESTION # 48
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(processingTime="5 seconds")
B. trigger()
C. trigger("5 seconds")
D. trigger(once="5 seconds")
E. trigger(continuous="5 seconds")

Answer: A

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 49
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW 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 flagged as invalid in a field added to the target dataset.
B. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
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.
E. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Answer: D

Explanation:
Delta Live Tables expectations are optional clauses that apply data quality checks on each record passing through a query. An expectation consists of a description, a boolean statement, and an action to take when a record fails the expectation. The ON VIOLATION clause specifies the action to take, which can be one of the following: warn, drop, or fail. The drop action means that invalid records are dropped from the target dataset before data is written to the target. The failure is reported as a metric for the dataset, which can be viewed by querying the Delta Live Tables event log. The event log contains information such as the number of records that violate an expectation, the number of records dropped, and the number of records written to the target dataset. Reference:
Manage data quality with Delta Live Tables
Monitor Delta Live Tables pipelines
Delta Live Tables SQL language reference

NEW QUESTION # 50
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