Databricks Databricks-Certified-Professional-Data-Engineer Visual Cert Test | Databricks-Certified-Professional-Data-Engineer Well Prep

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Databricks Certified Professional Data Engineer exam is a comprehensive assessment of a candidate's ability to design, implement, and manage data pipelines on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification exam covers a wide range of topics, including data ingestion, data processing, data transformation, and data storage. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's knowledge of best practices for building efficient and scalable data pipelines that can handle large volumes of data.

Databricks Certified Professional Data Engineer Exam is a certification program designed for data professionals who want to demonstrate their expertise in building, deploying, and maintaining data engineering solutions using Databricks. Databricks-Certified-Professional-Data-Engineer Exam covers a wide range of topics related to data engineering and requires a thorough understanding of Databricks data engineering concepts and techniques. Databricks-Certified-Professional-Data-Engineer exam is challenging and requires the candidate to demonstrate their ability to perform specific tasks using Databricks.

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The Databricks Databricks-Certified-Professional-Data-Engineer exam is designed to assess the proficiency of the candidates in various areas related to data engineering on Databricks. Databricks-Certified-Professional-Data-Engineer exam focuses on topics such as data ingestion, data transformation, data modeling, data storage, and data processing. Databricks-Certified-Professional-Data-Engineer Exam Tests the candidates' knowledge of using Databricks to build data pipelines that can handle large volumes of data, process data in real-time, and integrate with other data sources.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format("cloudFiles")
2. .option("cloudFiles.format", "json")
3. .option("cloudFiles.schemaLocation", schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?

A. The data engineer needs to change the format("cloudFiles") line to format("autoLoader")
B. There is no change required. Databricks automatically uses Auto Loader for streaming reads
C. There is no change required. The inclusion of format("cloudFiles") enables the use of Auto Loader
D. The data engineer needs to add the .autoLoader line before the .load(sourcePath) line
E. There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader

Answer: C

NEW QUESTION # 34
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Perform an insert-only merge with a matching condition on a unique key.
B. Set the configuration delta.deduplicate = true.
C. VACUUM the Delta table after each batch completes.
D. Perform a full outer join on a unique key and overwrite existing data.
E. Rely on Delta Lake schema enforcement to prevent duplicate records.

Answer: A

NEW QUESTION # 35
You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

A. By default, overwrites the data and schema, you cannot perform time travel
B. Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions
C. Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions
D. Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.
E. Appends the data to the current version, you can time travel to previous versions

Answer: B

Explanation:
Explanation
The answer is, INSERT OVERWRITE Overwrites the current version of the data but preserves all historical versions of the data, you can time travel to previous versions.
1.INSERT OVERWRITE customer_sales
2.SELECT * FROM customers c
3.INNER JOIN sales s on s.customer_id = c.customer_id
Let's just assume that this is the second time you are running the above statement, you can still query the prior version of the data using time travel, and any DML/DDL except DROP TABLE creates new PARQUET files so you can still access the previous versions of data.
SQL Syntax for Time travel
SELECT * FROM table_name as of [version number]
with customer_sales example
SELECT * FROM customer_sales as of 1 -- previous version
SELECT * FROM customer_sales as of 2 -- current version
You see all historical changes on the table using DESCRIBE HISTORY table_name Note: the main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to update the schema when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command INSERT OVERWRITEwill fail.
Any DML/DDL operation(except DROP TABLE) on the Delta table preserves the historical ver-sion of the data.

NEW QUESTION # 36
Which statement describes Delta Lake Auto Compaction?

A. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
B. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
C. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
E. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

Answer: D

Explanation:
Explanation
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB.
Auto Compaction only compacts files that have not been compacted previously. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.

NEW QUESTION # 37
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at anypoint.
B. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
C. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
D. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.

Answer: A

Explanation:
In Databricks, catalogs, schemas (or databases), and tables are managed through the Unity Catalog or Hive Metastore, depending on the environment. Permissions and ownership within these structures are governed by access control lists (ACLs).
* Catalog and Schema Ownership:When a platform engineer creates a catalog (such as catalog_A) and schema (such as schema_A), they automatically become the owner of those entities. This ownership gives them control over granting permissions for those entities (i.e., granting the USE CATALOG and USE SCHEMA privileges to others). However, ownership of the catalog or schema doesnot automaticallyextend to ownership or permission of individual tables within that schema.
* Table Permissions:For tables within a schema, the permission model is more granular. The table creator (i.e., whoever creates the table) is automatically assigned as the owner of that table. In this case, the platform engineer owns the schema but does not automatically inherit permissions to any table created within the schema unless explicitly granted by the table's owner or unless they grant permissions to themselves.
* Why the Engineer Lacks Access:The platform engineer notices that they do not have access to the underlying tables in schema_A despite being the owner of the schema. This occurs because the schema's ownership does not cascade to the tables. The engineer must either:
* Grant permissions to themselves for the tables in schema_A, or
* Be granted permissions by whoever created the tables within the schema.
* Resolution:As the owner of the schema, the platform engineer can easily grant themselves the required permissions (such as SELECT, INSERT, etc.) for the tables in the schema. This explains why the owner of a schema may not automatically have access to the tables and must take explicit steps to acquire those permissions.
References
* Databricks Unity Catalog Documentation: Manage Permissions
* [Databricks Permissions and Ownership](https://docs.databricks.com/security/access-control
/workspace-acl.html#permissions

NEW QUESTION # 38
......

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Databricks Databricks-Certified-Professional-Data-Engineer Visual Cert Test | Databricks-Certified-Professional-Data-Engineer Well Prep

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q33-Q38):

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