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To take the Databricks Certified Professional Data Engineer certification exam, candidates must have a strong background in data engineering and experience working with Databricks. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and hands-on tasks that require candidates to demonstrate their ability to design, build, and manage data processing systems using Databricks. Databricks-Certified-Professional-Data-Engineer exam covers a wide range of topics, including data ingestion, data transformation, data storage, data processing, and data visualization. Upon successful completion of the exam, candidates will receive a Databricks Certified Professional Data Engineer certification, which demonstrates their expertise in using Databricks to build and maintain data processing systems.

Databricks is a leading company in the field of data engineering, providing a cloud-based platform for collaborative data analysis and processing. The company's platform is used by a wide range of companies and organizations, including Fortune 500 companies, government agencies, and academic institutions. Databricks offers a range of certifications to help professionals demonstrate their proficiency in using the platform, including the Databricks Certified Professional Data Engineer certification.

The Databricks Certified Professional Data Engineer Exam certification exam covers a range of topics, including data ingestion, transformation, and storage, ETL processes, data modeling, and machine learning. Candidates are tested on their ability to use Databricks tools and technologies to solve real-world data engineering problems. Databricks-Certified-Professional-Data-Engineer exam also evaluates the candidate's understanding of best practices for data engineering, including security, scalability, and cost optimization. By passing the Databricks Certified Professional Data Engineer exam, candidates can demonstrate their proficiency in Databricks data engineering technologies and enhance their job prospects in the field.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
B. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
C. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
D. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
E. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.

Answer: A

Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.

NEW QUESTION # 64
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
B. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
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 is implemented as a messaging bus and is derived from Apache Kafka.

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 # 65
You were asked to create a notebook that can take department as a parameter and process the data accordingly, which is the following statements result in storing the notebook parameter into a py-thon variable

A. department = notebook.param.get("department")
B. department = notebook.widget.get("department")
C. ASSIGN department == dbutils.widget.get("department")
D. department = dbutils.widget.get("department")
E. SET department = dbutils.widget.get("department")

Answer: D

Explanation:
Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html

NEW QUESTION # 66
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Delta log is scanned for min and max statistics for the latitude column
B. The Hive metastore is scanned for min and max statistics for the latitude column
C. The Parquet file footers are scanned for min and max statistics for the latitude column
D. All records are cached to attached storage and then the filter is applied
E. All records are cached to an operational database and then the filter is applied

Answer: A

Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column

NEW QUESTION # 67
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. The table reference in the metastore is updated and no data is changed.
B. The table name change is recorded in the Delta transaction log.
C. A new Delta transaction log Is created for the renamed table.
D. The table reference in the metastore is updated and all data files are moved.
E. All related files and metadata are dropped and recreated in a single ACID transaction.

Answer: A

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 # 68
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