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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q12-Q17):
質問 # 12
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can turn on the Auto Stop feature for the SQL endpoint.
- B. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized." - C. They can increase the cluster size of the SQL endpoint.
- D. They can increase the maximum bound of the SQL endpoint's scaling range
- E. They can turn on the Serverless feature for the SQL endpoint.
正解：E
解説：
Explanation
Databricks SQL endpoints can run in two modes: Serverless and Dedicated. Serverless mode allows you to run queries without managing clusters, while Dedicated mode allows you to run queries on a specific cluster.
Serverless mode is faster and more cost-effective for ad-hoc queries, especially when the SQL endpoint is not running. Dedicated mode is more suitable for predictable and consistent performance, especially for long-running queries. By turning on the Serverless feature for the SQL endpoint, the data engineering team can reduce the time it takes to start the SQL endpoint and return results. The other options are not relevant or effective for this scenario. References: Databricks SQL endpoints, New Performance Improvements in Databricks SQL, Slowness when fetching results in Databricks SQL
質問 # 13
A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?
- A. if day_of_week == 1 and review_period:
- B. if day_of_week = 1 and review_period = "True":
- C. if day_of_week == 1 and review_period == "True":
- D. if day_of_week = 1 and review_period:
- E. if day_of_week = 1 & review_period: = "True":
正解：A
解説：
In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison.
Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. References: [Python Operators], [Python If ... Else]
質問 # 14
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
- A. dbutils.sql
- B. spark.delta.sql
- C. spark.sql
- D. spark.table
- E. spark.delta.table
正解：C
解説：
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sql
質問 # 15
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
- A.
- B.
- C.
- D.
- E.
正解：E
解説：
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/
質問 # 16
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?
- A. There was a type mismatch between the specific schema and the inferred schema
- B. Auto Loader cannot infer the schema of ingested data
- C. JSON data is a text-based format
- D. All of the fields had at least one null value
- E. Auto Loader only works with string data
正解：C
解説：
Explanation
JSON data is a text-based format that uses strings to represent all values. When Auto Loader infers the schema of JSON data, it assumes that all values are strings. This is because Auto Loader cannot determine the type of a value based on its string representation. https://docs.databricks.com/en/ingestion/auto-loader/schema.html Forexample, the following JSON string represents a value that is logically a boolean: JSON "true" Use code with caution. Learn more However, Auto Loader would infer that the type of this value is string. This is because Auto Loader cannot determine that the value is a boolean based on its string representation. In order to get Auto Loader to infer the correct types for columns, the data engineer can provide type inference or schema hints. Type inference hints can be used to specify the types of specific columns. Schema hints can be used to provide the entire schema of the data. Therefore, the correct answer is B. JSON data is a text-based format.
質問 # 17
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