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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	Databricks-Certified-Data-Engineer-Associate
Certificate Validity Period:	2 years
Exam Format:	Multiple Select, Multiple Choice
Exam Price:	$200 USD
Passing Score:	70%
Exam Duration:	90 minutes
Real Exam Qty:	60
Available Languages:	English
Related Certifications:	Databricks Certified Data Analyst Associate
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored or in-person testing center
Pre Condition:	Recommended: 6+ months of experience with Databricks and Apache Spark
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks Certified Data Engineer Associate exam measures an individual's ability to design and implement data pipelines, optimize and tune big data solutions, and manage data workflows using Databricks. Candidates are expected to have a solid understanding of data processing techniques, data storage, and data management concepts. Databricks Certified Data Engineer Associate Exam certification program offers a comprehensive assessment of an individual's skills in the field of big data engineering and provides a valuable credential for those looking to advance their careers in this field.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

A. Scheduled Workflows run continuously until manually stopped.
B. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.
C. Scheduled Workflows process data as it arrives at configured sources.
D. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.

Answer: B

NEW QUESTION # 45
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?

A. Job Cluster
B. All-Purpose Cluster
C. Dedicated Cluster
D. Databricks SQL Serverless

Answer: A

NEW QUESTION # 46
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 are added to the target dataset and recorded as invalid in the event log.
D. Records that violate the expectation cause the job to fail.
E. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.

Answer: E

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 # 47
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(continuous="5 seconds")
E. trigger(once="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). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 48
Which two components function in the DB platform architecture's control plane? (Choose two.)

A. Virtual Machines
B. Unity Catalog
C. Compute Orchestration
D. Compute
E. Serverless Compute

Answer: B,C

NEW QUESTION # 49
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