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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q139-Q144):

問題 #139
You are currently working on reloading customersales tables using the below query
1. INSERT OVERWRITE customersales
2. SELECT * FROM customers c
3. INNER JOIN salesmonthly s on s.customerid = c.customerid
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customersales table if you want to see the previous version of the data prior to running the above statement?

  • A. By default, overwrites the data and schema, you cannot perform time travel
  • B. Appends the data to the current version, you can time travel to previous versions
  • C. Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions
  • D. Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions
  • E. Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.

答案：D 解題說明：
Explanation
The answer is, INSERT OVERWRITE Overwrites the current version of the data but preserves all historical versions of the data, you can time travel to previous versions.
1.INSERT OVERWRITE customersales
2.SELECT * FROM customers c
3.INNER JOIN sales s on s.customerid = c.customerid
Let's just assume that this is the second time you are running the above statement, you can still query the prior version of the data using time travel, and any DML/DDL except DROP TABLE creates new PARQUET files so you can still access the previous versions of data.
SQL Syntax for Time travel
SELECT * FROM tablename as of [version number]
with customersales example
SELECT * FROM customersales as of 1 -- previous version
SELECT * FROM customersales as of 2 -- current version
You see all historical changes on the table using DESCRIBE HISTORY tablename Note: the main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to update the schema when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command INSERT OVERWRITEwill fail.
Any DML/DDL operation(except DROP TABLE) on the Delta table preserves the historical ver-sion of the data.
問題 #140
You were asked to identify number of times a temperature sensor exceed threshold temperature (100.00) by each device, each row contains 5 readings collected every 5 minutes, fill in the blank with the appropriate functions.
Schema: deviceId INT, deviceTemp ARRAY<double>, dateTimeCollected TIMESTAMP

SELECT deviceId, __ (__ (__(deviceTemp], i -> i > 100.00)))
FROM devices
GROUP BY deviceId

  • A. SUM, COUNT, SIZE
  • B. SUM, SIZE, ARRAY_FILTER
  • C. SUM, SIZE, SLICE
  • D. SUM, SIZE, FILTER
  • E. SUM, SIZE, ARRAY_CONTAINS

答案：D 解題說明：
Explanation
FILER function can be used to filter an array based on an expression
SIZE function can be used to get size of an array
SUM is used to calculate to total by device
Diagram Description automatically generated

問題 #141
While investigating a performance issue, you realized that you have too many small files for a given table, which command are you going to run to fix this issue

  • A. OPTIMIZE table_name
  • B. SHRINK table_name
  • C. VACUUM table_name
  • D. COMPACT table_name
  • E. MERGE table_name

答案：A 解題說明：
Explanation
The answer is OPTIMIZE table_name,
Optimize compacts small parquet files into a bigger file, by default the size of the files are determined based on the table size at the time of OPTIMIZE, the file size can also be set manually or adjusted based on the workload.
https://docs.databricks.com/delta/optimizations/file-mgmt.html
Tune file size based on Table size
To minimize the need for manual tuning, Databricks automatically tunes the file size of Delta tables based on the size of the table. Databricks will use smaller file sizes for smaller tables and larger file sizes for larger tables so that the number of files in the table does not grow too large.
Table Description automatically generated

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問題 #142
You are working on a dashboard that takes a long time to load in the browser, due to the fact that each visualization contains a lot of data to populate, which of the following approaches can be taken to address this issue?

  • A. Use Databricks SQL Query filter to limit the amount of data in each visualization
  • B. Increase size of the SQL endpoint cluster
  • C. Increase the scale of maximum range of SQL endpoint cluster
  • D. Use Delta cache to store the intermediate results
  • E. Remove data from Delta Lake

答案：A 解題說明：
Explanation
Note: The question may sound misleading but these are types of questions the exam tries to ask.
A query filter lets you interactively reduce the amount of data shown in a visualization, similar to query parameter but with a few key differences. A query filter limits data after it has been loaded into your browser.
This makes filters ideal for smaller datasets and environments where query executions are time-consuming, rate-limited, or costly.
This query filter is different from than filter that needs to be applied at the data level, this filter is at the visualization level so you can toggle how much data you want to see.
1.SELECT action AS action::filter, COUNT(0) AS "actions count"
2.FROM events
3.GROUP BY action
When queries have filters you can also apply filters at the dashboard level. Select the Use Dash-board Level Filters checkbox to apply the filter to all queries.
Dashboard filters
Query filters | Databricks on AWS
問題 #143*
What is the purpose of the bronze layer in a Multi-hop architecture?

  • A. Contains aggregated data that is to be consumed into Silver
  • B. Can be used to eliminate duplicate records
  • C. Perform data quality checks, corrupt data quarantined
  • D. Used as a data source for Machine learning applications.
  • E. Provides efficient storage and querying of full unprocessed history of data

答案：E 解題說明：
Explanation
The answer is Provides efficient storage and querying of full unprocessed history of data Medallion Architecture - Databricks Bronze Layer:
1.Raw copy of ingested data
2.Replaces traditional data lake
3.Provides efficient storage and querying of full, unprocessed history of data
4.No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.

問題 #144
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