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Databricks Certified Professional Data Engineer Exam Sample Questions (Q159-Q164):

NEW QUESTION # 159
How do you check the location of an existing schema in Delta Lake?

A. Check unity catalog UI
B. Run SQL command SHOW LOCATION schema_name
C. Run SQL command DESCRIBE SCHEMA EXTENDED schema_name
E Schemas are internally in-store external hive meta stores like MySQL or SQL Server
D. Use Data explorer

Answer: C

Explanation:
Explanation
Here is an example of how it looks
Graphical user interface, text, application, email Description automatically generated

NEW QUESTION # 160
At the end of the inventory process, a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data in-crementally, schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically.
Below is the auto loader to command to load the data, fill in the blanks for successful execution of below code.
1.spark.readStream
2..format("cloudfiles")
3..option("_______","csv)
4..option("_______", 'dbfs:/location/checkpoint/')
5..load(data_source)
6..writeStream
7..option("_______",' dbfs:/location/checkpoint/')
8..option("_______", "true")
9..table(table_name))

A. cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, append
B. cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, mergeSchema
C. format, checkpointlocation, schemalocation, overwrite
D. cloudfiles.format, checkpointlocation, cloudfiles.schemalocation, overwrite
E. cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, overwrite

Answer: B

Explanation:
Explanation
The answer is cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, mergeSchema.
Here is the end to end syntax of streaming ELT, below link contains complete options Auto Loader options | Databricks on AWS
1.spark.readStream
2..format("cloudfiles") # Returns a stream data source, reads data as it arrives based on the trigger.
3..option("cloudfiles.format","csv") # Format of the incoming files
4..option("cloudfiles.schemalocation", "dbfs:/location/checkpoint/") The location to store the inferred schema and subsequent changes
5..load(data_source)
6..writeStream
7..option("checkpointlocation","dbfs:/location/checkpoint/") # The location of the stream's checkpoint
8..option("mergeSchema", "true") # Infer the schema across multiple files and to merge the schema of each file. Enabled by default for Auto Loader when inferring the schema.
9..table(table_name)) # target table

NEW QUESTION # 161
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A. return spark.readStream.load("bronze")
B. return spark.readStream.table("bronze")
C.
D.
E. return spark.read.option("readChangeFeed", "true").table ("bronze")

Answer: D

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 162
The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

A. Df.apply(model, columns). Select (''customer_id, prediction''
B. Df. Select (''customer_id''.
Model (''columns) alias (''predictions'')
C. Model, predict (df, columns)
D. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')

Answer: C

Explanation:
Given the information that the model is registered with MLflow and assumingpredictis the method used to apply the model to a set of columns, we use themodel.predict()function to apply the model to the DataFrame dfusing the specifiedcolumns. Themodel.predict()function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictionscolumn.
When working with PySpark, this predictions column needs to be selected alongside thecustomer_idto create a new DataFrame with the schemacustomer_id LONG, predictions DOUBLE.
References:
* MLflow documentation on using Python function models: https://www.mlflow.org/docs/latest/models.
html#python-function-python
* PySpark MLlib documentation on model prediction:https://spark.apache.org/docs/latest/ml-pipeline.
html#pipeline

NEW QUESTION # 163
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. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
C. One new job named " Ingest new data " will be defined in the workspace, but it will not be executed.
D. Three new jobs named " Ingest new data " will be defined in the workspace, but no jobs will be executed.
E. Three new jobs named " Ingest new data " will be defined in the workspace, and they will each run once daily.

Answer: D

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 and Logging" section; [Databricks Documentation], under "Jobs API - Create" section.

NEW QUESTION # 164
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