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[General] Customizable Databricks Databricks-Certified-Professional-Data-Engineer Practice

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【General】 Customizable Databricks Databricks-Certified-Professional-Data-Engineer Practice

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q114-Q119):NEW QUESTION # 114
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.

Which command should be removed from the notebook before scheduling it as a job?
  • A. Cmd 6
  • B. Cmd 3
  • C. Cmd 2
  • D. Cmd 4
  • E. Cmd 5
Answer: A
Explanation:
Cmd 6 is the command that should be removed from the notebook before scheduling it as a job. This command is selecting all the columns from the finalDF dataframe and displaying them in the notebook. This is not necessary for the job, as the finalDF dataframe is already written to a table in Cmd 7. Displaying the dataframe in the notebook will only consume resources and time, and it will not affect the output of the job.
Therefore, Cmd 6 is redundant and should be removed.
The other commands are essential for the job, as they perform the following tasks:
* Cmd 1: Reads the raw_data table into a Spark dataframe called rawDF.
* Cmd 2: Prints the schema of the rawDF dataframe, which is useful for debugging and understanding the data structure.
* Cmd 3: Selects all the columns from the rawDF dataframe, as well as the nested columns from the values struct column, and creates a new dataframe called flattenedDF.
* Cmd 4: Drops the values column from the flattenedDF dataframe, as it is no longer needed after flattening, and creates a new dataframe called finalDF.
* Cmd 5: Explains the physical plan of the finalDF dataframe, which is useful for optimizing and tuning the performance of the job.
* Cmd 7: Writes the finalDF dataframe to a table called flat_data, using the append mode to add new data to the existing table.

NEW QUESTION # 115
What is the purpose of the bronze layer in a Multi-hop Medallion architecture?
  • A. Reduces data storage by compressing the data
  • B. Contain aggregated data that is to be consumed into Silver
  • C. Data quality checks, corrupt data quarantined
  • D. Powers ML applications
  • E. Copy of raw data, easy to query and ingest data for downstream processes.
Answer: E
Explanation:
Explanation
The answer is, copy of raw data, easy to query and ingest data for downstream processes, Medallion Architecture - Databricks Here are the typical role of Bronze Layer in a medallion architecture.
Bronze Layer:
1. Raw copy of ingested data
2. Replaces traditional data lake
3. Provides efficient storage and querying of full, unprocessed history of data
4. No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.


NEW QUESTION # 116
You are currently working on a notebook that will populate a reporting table for downstream process consumption, this process needs to run on a schedule every hour, what type of cluster are you going to use to set up this job?
  • A. Since it's just a single job and we need to run every hour, we can use an all-purpose cluster
  • B. The job cluster is best suited for this purpose.
  • C. Use delta live table pipeline to run in continuous mode
  • D. Use Azure VM to read and write delta tables in Python
Answer: B
Explanation:
Explanation
The answer is, The Job cluster is best suited for this purpose.
Since you don't need to interact with the notebook during the execution especially when it's a scheduled job, job cluster makes sense. Using an all-purpose cluster can be twice as expensive as a job cluster.
FYI,
When you run a job scheduler with option of creating a new cluster when the job is complete it terminates the cluster. You cannot restart a job cluster.

NEW QUESTION # 117
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?
  • A. /jobs/runs/get
  • B. /jobs/runs/list
  • C. /jobs/list
  • D. /jobs/runs/get-output
  • E. /jobs/get
Answer: E
Explanation:
Explanation
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.

NEW QUESTION # 118
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?
  • A. The total count of rows will be returned from cached results unless REFRESH is run
  • B. The total count of records is calculated from the parquet file metadata
  • C. The total count of records is calculated from the Delta transaction logs
  • D. The total count of rows is calculated by scanning all data files
  • E. The total count of records is calculated from the Hive metastore
Answer: C
Explanation:
https://delta.io/blog/2023-04-19 ... %20can%20get%20the%
20number,a%20given%20Delta%20table%20version.

NEW QUESTION # 119
......
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