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Passing the Databricks Certified Data Engineer Associate Exam demonstrates that the candidate has the skills and knowledge necessary to design, build, and maintain data pipelines and data warehouses using Databricks. It is an important certification for data engineers who want to advance their careers and demonstrate their expertise in using Databricks to solve complex data engineering problems. With the explosive growth of data in today's business environment, the demand for skilled data engineers is higher than ever, and the Databricks Certified Data Engineer Associate Exam is an excellent way to stand out in a crowded job market.

Databricks-Certified-Data-Engineer-Associate exam is an essential certification for data engineers who work with Databricks. Databricks-Certified-Data-Engineer-Associate Exam measures the candidate's knowledge, skills, and abilities in Databricks architecture, data ingestion, data processing, data engineering, data storage, and data management. Databricks Certified Data Engineer Associate Exam certification is recognized globally and is a valuable asset for data engineers who want to advance their careers and demonstrate their proficiency in Databricks.

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Databricks Certified Data Engineer Associate exam is designed for data engineers who work with the Databricks platform and have experience in designing and implementing data pipelines. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including data ingestion, transformation, storage, and processing. It also covers advanced concepts such as Spark architecture, performance tuning, and optimization.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with data in a variety of types at once
B. An ability to work with an array of tables for procedural automation
C. An ability to work with complex, nested data ingested from JSON files
D. An ability to work with time-related data in specified intervals
E. An ability to work with data within certain partitions and windows

Answer: C

Explanation:
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

NEW QUESTION # 65
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?

A. datetime
B. Cron syntax
C. pyspark.sql.types.DateType
D. pyspark.sql.types.TimestampType
E. There is no way to represent and submit this information programmatically

Answer: B

NEW QUESTION # 66
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 autoscale their work as data size evolves
B. Both teams would use the same source of truth for their work
C. Both teams would respond more quickly to ad-hoc requests
D. Both teams would reorganize to report to the same department
E. Both teams would be able to collaborate on projects in real-time

Answer: B

Explanation:
Explanation
A data lakehouse is designed to unify the data engineering and data analysis architectures by integrating features of both data lakes and data warehouses. One of the key benefits of a data lakehouse is that it provides a common, centralized data repository (the "lake") that serves as a single source of truth for data storage and analysis. This allows both data engineering and data analysis teams to work with the same consistent data sets, reducing discrepancies and ensuring that the reports generated by both teams are based on the same underlying data.

NEW QUESTION # 67
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

A. Replayable Sources and Idempotent Sinks
B. Checkpointing and Idempotent Sinks
C. Structured Streaming cannot record the offset range of the data being processed in each trigger.
D. Checkpointing and Write-ahead Logs
E. Write-ahead Logs and Idempotent Sinks

Answer: D

Explanation:
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure. Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. Reference: Structured Streaming Programming Guide, Fault Tolerance Semantics

NEW QUESTION # 68
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 flagged as invalid in a field added to the target dataset.
B. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
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.

Answer: B

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.
Reference:
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 69
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