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Study Databricks-Certified-Professional-Data-Engineer Test & Databricks-Certified-Professional-Data-Engineer Exam Papers

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
Which of the following scenarios is the best fit for the AUTO LOADER solution?

A. Efficiently process new data incrementally from cloud object storage
B. Efficiently move data incrementally from one delta table to another delta table
C. Incrementally process new data from relational databases like MySQL
D. Efficiently copy data from data lake location to another data lake location
E. Incrementally process new streaming data from Apache Kafa into delta lake

Answer: A

Explanation:
Explanation
The answer is, Efficiently process new data incrementally from cloud object storage.
Please note: AUTO LOADER only works on data/files located in cloud object storage like S3 or Azure Blob Storage it does not have the ability to read other data sources, although AU-TO LOADER is built on top of structured streaming it only supports files in the cloud object stor-age. If you want to use Apache Kafka then you can just use structured streaming.
Diagram Description automatically generated

Auto Loader and Cloud Storage Integration
Auto Loader supports a couple of ways to ingest data incrementally
1.Directory listing - List Directory and maintain the state in RocksDB, supports incremental file listing
2.File notification - Uses a trigger+queue to store the file notification which can be later used to retrieve the file, unlike Directory listing File notification can scale up to millions of files per day.
[OPTIONAL]
Auto Loader vs COPY INTO?
Auto Loader
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a new Structured Streaming source called cloudFiles. Given an input directory path on the cloud file storage, the cloudFiles source automatically processes new files as they arrive, with the option of also processing existing files in that directory.
When to use Auto Loader instead of the COPY INTO?
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*You do not plan to load subsets of previously uploaded files. With Auto Loader, it can be more difficult to reprocess subsets of files. However, you can use the COPY INTO SQL command to reload subsets of files while an Auto Loader stream is simultaneously running.
Refer to more documentation here,
https://docs.microsoft.com/en-us/azure/databricks/ingestion/auto-loader

NEW QUESTION # 68
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
B. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
C. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
D. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
E. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

Answer: C

Explanation:
This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Overview" section.

NEW QUESTION # 69
What are the different ways you can schedule a job in Databricks workspace?

A. Cron, File notification from Cloud object storage
B. On-Demand runs, File notification from Cloud object storage
C. Continuous, Incremental
D. Cron, On Demand runs
E. Once, Continuous

Answer: D

Explanation:
Explanation
The answer is, Cron, On-Demand runs
Supports running job immediately or using can be scheduled using CRON syntax

NEW QUESTION # 70
Kevin is the owner of the schema sales, Steve wanted to create new table in sales schema called regional_sales so Kevin grants the create table permissions to Steve. Steve creates the new table called regional_sales in sales schema, who is the owner of the table regional_sales

A. Kevin and Smith both are owners of table
B. By default ownership is assigned to DEFAULT_OWNER
C. Steve is the owner of the table
D. By default ownership is assigned DBO
E. Kevin is the owner of sales schema, all the tables in the schema will be owned by Kevin

Answer: C

Explanation:
Explanation
A user who creates the object becomes its owner, does not matter who is the owner of the parent object.

NEW QUESTION # 71
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
C. When a database is being created, make sure that the LOCATION keyword is used.
D. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
E. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

Answer: A

Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION '/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the '/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
Reference:
https://docs.databricks.com/delta/delta-batch.html#create-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-a-table

NEW QUESTION # 72
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