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Preparing for the Databricks-Certified-Data-Engineer-Associate Exam requires a deep understanding of Databricks and its various components. Candidates should have experience with Spark SQL, Spark Streaming, and Spark MLlib, as well as a solid understanding of data modeling, data structures, and algorithms. They should also be familiar with common data storage and processing technologies, such as Hadoop, Kafka, and AWS S3.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
Which query is performing a streaming hop from raw data to a Bronze table?

Answer: C

NEW QUESTION # 102
Which query is performing a streaming hop from raw data to a Bronze table?

Answer: D

Explanation:
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:
Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.
Reference:
Databricks documentation on Structured Streaming: Structured Streaming in Databricks

NEW QUESTION # 103
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 code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="once")
B. processingTime(1)
C. trigger(parallelBatch=True)
D. trigger(continuous="once")
E. trigger(availableNow=True)

Answer: E

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.trigger.html

NEW QUESTION # 104
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. DELTA
B. sqlite
C. org.apache.spark.sql.sqlite
D. autoloader
E. org.apache.spark.sql.jdbc

Answer: B

Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver

NEW QUESTION # 105
Which of the following tools is used by Auto Loader process data incrementally?

A. Data Explorer
B. Unity Catalog
C. Checkpointing
D. Databricks SQL
E. Spark Structured Streaming

Answer: E

Explanation:
Auto Loader provides a Structured Streaming source called cloudFiles that can process new data files as they arrive in cloud storage without any additional setup. Auto Loader uses a scalable key-value store to track ingestion progress and ensure exactly-once semantics. Auto Loader can ingest various file formats and load them into Delta Lake tables. Auto Loader is recommended for incremental data ingestion with Delta Live Tables, which extends the functionality of Structured Streaming and allows you to write declarative Python or SQL code to deploy a production-quality data pipeline. Reference: What is Auto Loader?, What is Auto Loader? | Databricks on AWS, Solved: How does Auto Loader ingest data? - Databricks - 5629

NEW QUESTION # 106
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