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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a professional certification program designed to assess the skills and knowledge of individuals working in the field of data engineering. Databricks Certified Data Engineer Associate Exam certification validates the ability of data engineers to design, build, and maintain data pipelines and data warehouses using Databricks technologies. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including data ingestion, data transformation, data modeling, data warehousing, and data quality.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q188-Q193):

NEW QUESTION # 188
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace "transactions" with the path to the location of the Delta table
B. Replace spark.read with spark.readStream
C. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
D. Replace predict with a stream-friendly prediction function
E. Replace format("delta") with format("stream")

Answer: B

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html
In the context of Databricks, when transitioning from batch processing to stream processing, one common change that needs to be made is replacing spark.read with spark.readStream. This modification is essential because spark.read is used for batch processing, while spark.readStream is used for stream processing. The rest of the code can often remain the same or require minimal changes. References: The information can be referenced from Databricks documentation on structured streaming: Structured Streaming Programming Guide.

NEW QUESTION # 189
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

A. The table's data was larger than 10 GB
B. The table's data was smaller than 10 GB
C. The table did not have a location
D. The table was external
E. The table was managed

Answer: D

Explanation:
Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location.hence when a external table is dropped you clear off only the metadata and the files (data) remain.

NEW QUESTION # 190
A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.
Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

A. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
B. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted
C. The Spark User interface has a debug tab that contains the variables that are used in this session.
D. The Data Engineer should add print statements to find out what the variable is.

Answer: A

NEW QUESTION # 191
A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several JSON files from containers on a storage account as soon as the file arrives within the storage account. Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

A. df = spark.readStream.format("json".load("input/path")
B. df = spark.readStream.format("cloud").option("json").load("/input/path")
C. df = spark.read.json("input/path")
D. df = spark.readStream.format("cloudFiles").option("cloudFiles.format", "json").load("/input/path")

Answer: D

Explanation:
To automatically ingest JSON files as soon as they arrive in cloud storage, the correct syntax is to use Auto Loader with spark.readStream.format("cloudFiles").option("cloudFiles.format",
"json").load("/input/path"). This enables continuous ingestion of new files into a streaming dataframe.

NEW QUESTION # 192
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could submit a feature request with Databricks to add this functionality.
B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
C. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
D. They could redesign the data model to separate the data used in the final query into a new table.
E. They could only run the entire program on Sundays.

Answer: C

Explanation:
This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. Reference: PySpark SQL Module, Python datetime Module, Databricks Jobs

NEW QUESTION # 193
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