Databricks Databricks-Certified-Professional-Data-Engineer Practice Exams - Databricks-Certified-Professional-Data-Engineer Instant Access

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Spark APIs in Python
  • 2. Built-in and user-defined functions
  • 3. Performance optimization techniques
- Spark DataFrames and Spark SQL
  • 1. Window functions
  • 2. DataFrame operations and transformations
  • 3. Spark SQL queries and functions
Delta Lake20-25%- Delta Lake fundamentals
  • 1. Time travel and data versioning
  • 2. ACID transactions
  • 3. Optimize and Z-order
- Delta Lake operations
  • 1. Schema evolution and enforcement
  • 2. Delta Live Tables
  • 3. Merge, update, delete operations
Data Ingestion15-20%- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
- Batch ingestion methods
  • 1. Integration with external systems
  • 2. Spark APIs for ingestion
  • 3. DBR autoloader
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Jobs and job scheduling
  • 2. Monitoring and alerting
  • 3. Task dependencies and orchestration
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Bronze, silver, gold data layers
  • 2. Data governance fundamentals
  • 3. Differences between data lake, data warehouse, and lakehouse

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

Answer: A

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint


NEW QUESTION # 85
A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

Answer: A

Explanation:
In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins.
:
Structured Streaming Programming Guide: Join Operations
Databricks Documentation on Stream-Static Joins: Databricks Stream-Static Joins


NEW QUESTION # 86
The business intelligence team has a dashboard configured to track various summary metrics for retail stories.
This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

Answer: D


NEW QUESTION # 87
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

Answer: D

Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in theraw_iottable while recomputing downstream tables when a pipeline update is run, the propertypipelines.reset.allowedshould be set tofalse.
This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to theraw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
References:
* Databricks documentation on DLT pipelines: https://docs.databricks.com/data-engineering/delta-live- tables/delta-live-tables-overview.html


NEW QUESTION # 88
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

Answer: E

Explanation:
Explanation
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter1. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.
The other options are not correct, because:
Option A is incorrect because spark.conf.get("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.
Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API. The input() function is used to get user input from the standard input stream, not from the API request3.
Option C is incorrect because sys.argv1 is not a valid way to get a parameter passed through the Databricks Jobs API. The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.
Option D is incorrect because dbutils.notebooks.getParam("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.
References: Widgets, Spark Configuration, input(), sys.argv, Notebooks


NEW QUESTION # 89
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