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Test Databricks Databricks-Certified-Professional-Data-Engineer Duration - Databricks-Certified-Professional-Data-Engineer Dumps Torrent

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Preparing for the Databricks Certified Professional Data Engineer exam is crucial for anyone looking to advance their career in data engineering. Databricks offers several resources to help candidates prepare for the exam, including online training courses, study materials, and practice exams. By earning this certification, data engineers can demonstrate their proficiency in using Databricks to build scalable and efficient data pipelines, which can lead to new career opportunities and higher salaries.

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Databricks Certified Professional Data Engineer exam consists of a set of performance-based tasks that test the candidate's ability to apply their knowledge and skills to real-world scenarios. Databricks-Certified-Professional-Data-Engineer exam is conducted online and can be taken from anywhere in the world. Databricks-Certified-Professional-Data-Engineer Exam is timed and candidates have to complete the tasks within the given time frame. Databricks-Certified-Professional-Data-Engineer exam is designed in such a way that it assesses the candidate's ability to work with Databricks Unified Analytics Platform and solve complex data engineering problems.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q172-Q177):

NEW QUESTION # 172
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION "dbfs:/mnt/sales/*.csv"

A. USING CSV
TYPE ( "true","|")
B. FORMAT CSV
OPTIONS ( "true","|")
C. USING CSV
OPTIONS ( header ="true", delimiter = "|")
(Correct)
D. FORMAT CSV
FORMAT TYPE ( header ="true", delimiter = "|")
E. FORMAT CSV
TYPE ( header ="true", delimiter = "|")

Answer: C

Explanation:
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE table_name (col_name1 col_typ1,..)
USING data_source
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"

NEW QUESTION # 173
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
B. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
C. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
D. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
E. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.

Answer: A

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.

NEW QUESTION # 174
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
B. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
D. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

NEW QUESTION # 175
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
B. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
C. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
D. Convert the list of configuration values to a dictionary of table settings, using table names as keys.

Answer: D

Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names. Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

NEW QUESTION # 176
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. All related files and metadata are dropped and recreated in a single ACID transaction.
B. The table reference in the metastore is updated and no data is changed.
C. The table name change is recorded in the Delta transaction log.
D. A new Delta transaction log Is created for the renamed table.
E. The table reference in the metastore is updated and all data files are moved.

Answer: B

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.

NEW QUESTION # 177
......

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