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[General] 最真實的Databricks-Certified-Data-Engineer-Associate考古題

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【General】 最真實的Databricks-Certified-Data-Engineer-Associate考古題

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考試是一項90分鐘的在線考試，由60道多選題構成。考試涵蓋多個主題，如數據提取、數據轉換、數據建模、數據倉儲和數據分析。本考試旨在驗證候選人在生產環境中使用 Databricks 的能力。
最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q139-Q144):問題 #139
Identify how the count_if function and the count where x is null can be used Consider a table random_values with below data.
What would be the output of below query?
select count_if(col > 1) as count_a. count(*) as count_b.count(col1) as count_c from random_values col1
0
1
2
NULL -
2
3
  • A. 3 6 6
  • B. 4 6 6
  • C. 3 6 5
  • D. 4 6 5
答案：C

問題 #140
Which query is performing a streaming hop from raw data to a Bronze table?
  • A.
  • B.
  • C.
  • D.
答案：C
解題說明：
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:
* Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
* Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
* Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
* Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.
Referencesatabricks documentation on Structured Streaming: Structured Streaming in Databricks

問題 #141
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?
  • A. GRANT ALL PRIVILEGES ON TABLE team TO sales;
  • B. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
  • C. GRANT SELECT ON TABLE sales TO team;
  • D. GRANT USAGE ON TABLE sales TO team;
  • E. GRANT ALL PRIVILEGES ON TABLE sales TO team;
答案：E
解題說明：
To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON TABLE statement. This statement will grant all the possible privileges on the table, such as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on the table team to the user or group sales, which is the opposite of what the question asks. References: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a table using SQL - Azure Databricks, SQL Privileges - Databricks

問題 #142
What Databricks feature can be used to check the data sources and tables used in a workspace?
  • A. Use the lineage feature to visualize a graph that highlights where the table is used only in reports.
  • B. Do not use the lineage feature as it only tracks activity from the last 3 months and will not provide full details on dependencies.
  • C. Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.
  • D. Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks,
答案：C

問題 #143
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which change will need to be made to the pipeline when migrating to Delta Live Tables?
  • A. The pipeline will need to use a batch source in place of a streaming source.
  • B. The pipeline can have different notebook sources in SQL & Python.
  • C. The pipeline will need to be written entirely in Python.
  • D. The pipeline will need to be written entirely in SQL.
答案：B
解題說明：
When migrating to Delta Live Tables (DLT) with a data pipeline that involves different programming languages across various data layers, the migration does not require unifying the pipeline into a single language. Delta Live Tables support multi-language pipelines, allowing data engineers and data analysts to work in their preferred languages, such as Python for data engineering tasks (raw, bronze, and silver layers) and SQL for data analytics tasks (gold layer). This capability is particularly beneficial in collaborative settings and leverages the strengths of each language for different stages of data processing.
Reference:
Databricks documentation on Delta Live Tables: Delta Live Tables Guide

問題 #144
......
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