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시험패스가능한Databricks-Certified-Professional-Data-Engineer유효한공부자료최신덤프

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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q130-Q135):

질문 # 130
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
B. Stop the existing pipeline; use the returned settings in a reset command
C. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
D. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git

정답：C

설명：
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use thedatabricks pipelines get --pipeline-idcommand to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing thepipeline_idto prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines createcommand to create a new pipeline with those settings.
References:
* Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines

질문 # 131
Given the following error traceback (from display(df.select(3*"heartrate"))) which shows AnalysisException: cannot resolve 'heartrateheartrateheartrate', which statement describes the error being raised?

A. There is a type error because a column object cannot be multiplied.
B. There is no column in the table named heartrateheartrateheartrate.
C. There is a syntax error because the heartrate column is not correctly identified as a column.
D. There is a type error because a DataFrame object cannot be multiplied.

정답：B

설명：
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "select() expects column names or Column expressions."
Exact extract: "Use col("name") (or df["name"]) to reference a column; Python string operations act on strings, not columns."

질문 # 132
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
B. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
C. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

정답：D

설명：
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
:
Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

질문 # 133
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No: the change data feed only tracks inserts and updates not deleted records.
B. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
C. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

정답：B

설명：
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

질문 # 134
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de- duplicated and validated, which statement describes what will occur when this code is executed?

A. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
B. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
C. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
D. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
E. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

정답：A

설명：
The provided PySpark code performs the following operations:
* Reads Data from silver_customer_sales Table:
* The code starts by accessing the silver_customer_sales table using the spark.table method.
* Groups Data by customer_id:
* The .groupBy("customer_id") function groups the data based on the customer_id column.
* Aggregates Data:
* The .agg() function computes several aggregate metrics for each customer_id:
* F.min("sale_date").alias("first_transaction_date"): Determines the earliest sale date for the customer.
* F.max("sale_date").alias("last_transaction_date"): Determines the latest sale date for the customer.
* F.mean("sale_total").alias("average_sales"): Calculates the average sale amount for the customer.
* F.countDistinct("order_id").alias("total_orders"): Counts the number of unique orders placed by the customer.
* F.sum("sale_total").alias("lifetime_value"): Calculates the total sales amount (lifetime value) for the customer.
* Writes Data to gold_customer_lifetime_sales_summary Table:
* The .write.mode("overwrite").table("gold_customer_lifetime_sales_summary") command writes the aggregated data to the gold_customer_lifetime_sales_summary table.
* The mode("overwrite") specifies that the existing data in the
gold_customer_lifetime_sales_summary table will be completely replaced by the new aggregated data.
Conclusion:
When this code is executed, it reads all records from the silver_customer_sales table, performs the specified aggregations grouped by customer_id, and then overwrites the entire gold_customer_lifetime_sales_summary table with the aggregated results. Therefore, option D accurately describes this process: "The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job." References:
* PySpark DataFrame groupBy
* PySpark Basics

질문 # 135
......

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