Databricks Databricks-Certified-Professional-Data-Engineer認定資格試験問題集 & Databricks-Certified-Professional-Data-Engineer日本語版トレーリング

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Databricks認定プロフェッショナルデータエンジニア認定試験は、DataBricksを使用してデータパイプラインとワークフローを構築するスキルを実証する必要がある実践試験です。この試験は、DataBricks環境でデータソリューションを設計、実装、および管理することを候補者に要求するパフォーマンスベースのタスクのセットで構成されています。候補者には一連のデータエンジニアリングシナリオが与えられ、データビックを使用して各シナリオの要件を満たすソリューションを構築する必要があります。

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q71-Q76):

質問 # 71
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Query Detail screen, by interpreting the Physical Plan
B. In the Executor's log file, by gripping for "predicate push-down"
C. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
D. In the Storage Detail screen, by noting which RDDs are not stored on disk
E. In the Delta Lake transaction log. by noting the column statistics

正解：A

解説：
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under
"Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.

質問 # 72
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. All records are cached to attached storage and then the filter is applied
B. All records are cached to an operational database and then the filter is applied
C. The Delta log is scanned for min and max statistics for the latitude column
D. The Parquet file footers are scanned for min and max statistics for the latitude column
E. The Hive metastore is scanned for min and max statistics for the latitude column

正解：C

解説：
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

質問 # 73
You are trying to calculate total sales made by all the employees by parsing a complex struct data type that stores employee and sales data, how would you approach this in SQL Table definition, batchId INT, performance ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>, in-sertDate TIMESTAMP Sample data of performance column
1.[
2.{ "employeeId":1234
3."sales" : 10000},
4.
5.{ "employeeId":3232
6."sales" : 30000}
7.]
Calculate total sales made by all the employees?
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate

A. 1.select aggregate(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
B. 1.WITH CTE as (SELECT EXPLODE (performance) FROM table_name)
2.SELECT SUM (performance.sales) FROM CTE
C. 1.WITH CTE as (SELECT FLATTEN (performance) FROM table_name)
2.SELECT SUM (sales) FROM CTE
D. 1.select reduce(flatten(collect_list(performance:sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
E. SELECT SUM(SLICE (performance, sales)) FROM employee

正解：A

解説：
Explanation
The answer is
1.select aggregate(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
Nested Struct can be queried using the . notation performance.sales will give you access to all the sales values in the performance column.
Note: option D is wrong because it uses performance:sales not performance.sales. ":" this is only used when referring to JSON data but here we are dealing with a struct data type. for the exam please make sure to understand if you are dealing with JSON data or Struct data.

Other solutions:
we can also use reduce instead of aggregate
select reduce(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y) as total_sales from sales we can also use explode and sum instead of using any higher-order funtions.
1.with cte as (
2. select
3. explode(flatten(collect_list(performance.sales))) sales from sales
4.)
5.select
6. sum(sales) from cte
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate

質問 # 74
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint2.0/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. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
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.

正解：C

解説：
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.

質問 # 75
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, SIZE, ARRAY_FILTER
B. SUM, SIZE, ARRAY_CONTAINS
C. SUM, SIZE, SLICE
D. SUM, COUNT, SIZE
E. SUM, SIZE, FILTER

正解：E

解説：
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

質問 # 76
......

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