Databricks-Certified-Data-Engineer-Associate在線題庫 - Databricks-Certified-Data-Engineer-Associate考古題分享
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值得信賴的Databricks-Certified-Data-Engineer-Associate在線題庫和資格考試領導者和準確的Databricks-Certified-Data-Engineer-Associate:Databricks Certified Data Engineer Associate Exam
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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q172-Q177):
問題 #172
A data engineer is maintaining an ETL pipeline code with a GitHub repository linked to their Databricks account. The data engineer wants to deploy the ETL pipeline to production as a databricks workflow.
Which approach should the data engineer use?
- A. Manually create and manage the workflow in Ul
- B. Maintain workflow_conf ig. json and deploy it using Terraform
- C. Maintain workflow_config.j son and deploy it using Databricks CLI
- D. Databricks Asset Bundles (DAB) + GitHub Integration
答案:D
問題 #173
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 & 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 and review_period == "True":
答案:D
解題說明:
Explanation
This statement will check if the variable day_of_week is equal to 1 and if the variable review_period evaluates to a truthy value. The use of the double equal sign (==) in the comparison of day_of_week is important, as a single equal sign (=) would be used to assign a value to the variable instead of checking its value. The use of a single ampersand (&) instead of the keyword and is not valid syntax in Python. The use of quotes around True in options B and C will result in a string comparison, which will not evaluate to True even if the value of review_period is True.
問題 #174
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?
- A. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
- B. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
- C. Records that violate the expectation cause the job to fail.
- D. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
答案:D
解題說明:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]
問題 #175
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?
- A. Data Explorer
- B. Auto Loader
- C. Databricks SQL
- D. Unity Catalog
- E. Delta Lake
答案:B
問題 #176
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:
Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?
- A. Replace spark.read with spark.readStream
- B. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
- C. Replace "transactions" with the path to the location of the Delta table
- D. Replace format("delta") with format("stream")
- E. Replace predict with a stream-friendly prediction function
答案:A
問題 #177
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