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Databricks Certified Data Engineer Professional Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
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?

A. date = spark.conf.get("date")
B. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
C. import sys
date = sys.argv[1]
D. input_dict = input()
date= input_dict["date"]
E. date = dbutils.notebooks.getParam("date")

Answer: B

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 parameter. 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.

NEW QUESTION # 48
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
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Choose the response that correctly fills in the blank within the code block to complete this task.

A. window("event_time", "5 minutes").alias("time")
B. to_interval("event_time", "5 minutes").alias("time")
C. "event_time"
D. lag("event_time", "10 minutes").alias("time")
E. window("event_time", "10 minutes").alias("time")

Answer: A

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".

NEW QUESTION # 49
Which of the following is true of Delta Lake and the Lakehouse?

A. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
B. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
C. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
D. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
E. Z-order can only be applied to numeric values stored in Delta Lake tables

Answer: A

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

NEW QUESTION # 50
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
B. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
C. All records from all columns will be displayed with the values in user_ltv.
D. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
E. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

Answer: A

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

NEW QUESTION # 51
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. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
B. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
C. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
D. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
E. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

Answer: A

Explanation:
Databricks jobs create will create a new job with the same name each time it is run.
In order to overwrite the extsting job you need to run databricks jobs reset

NEW QUESTION # 52
......

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