DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER CURRENT EXAM CONTENT, VALID DUMPS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER SHEET

Databricks-Certified-Professional-Data-Engineer Current Exam Content, Valid Dumps Databricks-Certified-Professional-Data-Engineer Sheet

Databricks-Certified-Professional-Data-Engineer Current Exam Content, Valid Dumps Databricks-Certified-Professional-Data-Engineer Sheet

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Databricks Certified Professional Data Engineer exam is a comprehensive assessment that covers a wide range of topics related to data engineering using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to design, build, and optimize data pipelines using Databricks. Databricks-Certified-Professional-Data-Engineer exam is available online and can be taken from anywhere in the world, making it a convenient option for data professionals who want to validate their expertise in Databricks. Upon successful completion of the exam, candidates will receive a Databricks Certified Professional Data Engineer certification, which will demonstrate their proficiency in data engineering using Databricks.

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Databricks Certified Professional Data Engineer certification is designed for data engineers who are responsible for building and maintaining data pipelines and data lakes on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification exam covers a wide range of topics, including data engineering concepts, data modeling, data ingestion, data transformation, data processing, and data warehousing. Databricks-Certified-Professional-Data-Engineer Exam is designed to assess a candidate's ability to design, build, and maintain scalable and reliable data pipelines on the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
You are currently asked to work on building a data pipeline, you have noticed that you are currently working on a very large scale ETL many data dependencies, which of the following tools can be used to address this problem?

  • A. STRUCTURED STREAMING with MULTI HOP
  • B. SQL Endpoints
  • C. JOBS and TASKS
  • D. DELTA LIVE TABLES
  • E. AUTO LOADER

Answer: D

Explanation:
Explanation
The answer is, DELTA LIVE TABLES
DLT simplifies data dependencies by building DAG-based joins between live tables. Here is a view of how the dag looks with data dependencies without additional meta data,
1.create or replace live view customers
2.select * from customers;
3.
4.create or replace live view sales_orders_raw
5.select * from sales_orders;
6.
7.create or replace live view sales_orders_cleaned
8.as
9.select sales.* from
10.live.sales_orders_raw s
11. join live.customers c
12.on c.customer_id = s.customer_id
13.where c.city = 'LA';
14.
15.create or replace live table sales_orders_in_la
16.selects from sales_orders_cleaned;
Above code creates below dag

Documentation on DELTA LIVE TABLES,
https://databricks.com/product/delta-live-tables
https://databricks.com/blog/2022/04/05/announcing-generally-availability-of-databricks-delta-live-tables-dlt.htm DELTA LIVE TABLES, addresses below challenges when building ETL processes
1.Complexities of large scale ETL
a.Hard to build and maintain dependencies
b.Difficult to switch between batch and stream
2.Data quality and governance
a.Difficult to monitor and enforce data quality
b.Impossible to trace data lineage
3.Difficult pipeline operations
a.Poor observability at granular data level
b.Error handling and recovery is laborious


NEW QUESTION # 83
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

  • A. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
  • B. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
  • C. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
  • D. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
  • E. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.

Answer: C

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.


NEW QUESTION # 84
What is the best way to query external csv files located on DBFS Storage to inspect the data using SQL?

  • A. SELECT * FROM CSV. 'dbfs:/location/csv_files/'
  • B. SELECT * FROM 'dbfs:/location/csv_files/' FORMAT = 'CSV'
  • C. SELECT CSV. * from 'dbfs:/location/csv_files/'
  • D. SELECT * FROM 'dbfs:/location/csv_files/' USING CSV
  • E. You can not query external files directly, us COPY INTO to load the data into a table first

Answer: A

Explanation:
Explanation
Answer is, SELECT * FROM CSV. 'dbfs:/location/csv_files/'
you can query external files stored on the storage using below syntax
SELECT * FROM format.`/Location`
format - CSV, JSON, PARQUET, TEXT


NEW QUESTION # 85
The viewupdatesrepresents an incremental batch of all newly ingested data to be inserted or updated in the customerstable.
The following logic is used to process these records.

Which statement describes this implementation?

  • A. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
  • B. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
  • C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
  • D. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
  • E. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

Answer: D

Explanation:
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case,the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.


NEW QUESTION # 86
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

  • A. Data plane
  • B. Databricks Filesystem
  • C. Control plane
  • D. JDBC data source
  • E. Databricks web application

Answer: A

Explanation:
Explanation
See the Databricks high-level architecture


NEW QUESTION # 87
......

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