Snowflake DEA-C01 問題集

質問 1：
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?
A. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis
B. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
C. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
D. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A Data Engineer has created table t1 with datatype VARIANT:
create or replace table t1 (cl variant);
The Engineer has loaded the following JSON data set. which has information about 4 laptop models into the table:

The Engineer now wants to query that data set so that results are shown as normal structured data. The result should be 4 rows and 4 columns without the double quotes surrounding the data elements in the JSON data.
The result should be similar to the use case where the data was selected from a normal relational table z2 where t2 has string data type columns model__id. model, manufacturer, and =iccisi_r.an=. and is queried with the SQL clause select * from t2; Which select command will produce the correct results?
A.
B.
C.
D.
正解：B

質問 3：
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)
A. DataFraas.writeO
B. session.read.json{)
C. session.sql()
D. session,table()
E. session.jdbc_connection()
F. session.builder()
正解：B,C,D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?
A. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.
B. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage
C. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
D. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A Data Engineer needs to load JSON output from some software into Snowflake using Snowpipe.
Which recommendations apply to this scenario? (Select THREE)
A. Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute
B. Load a single huge array containing multiple records into a single table row
C. Ensure that data files are 100-250 MB (or larger) in size compressed
D. Load large files (1 GB or larger)
E. Extract semi-structured data elements containing null values into relational columns before loading
F. Verify each value of each unique element stores a single native data type (string or number)
正解：A,C,F
解説: (Topexam メンバーにのみ表示されます)

質問 6：
A stream called TRANSACTIONS_STM is created on top of a transactions table in a continuous pipeline running in Snowflake. After a couple of months, the TRANSACTIONS table is renamed transactiok3_raw to comply with new naming standards What will happen to the TRANSACTIONS _STM object?
A. TRANSACTIONS _STMwill keep working as expected
B. TRANSACTIONS _STMwill be automatically renamedTRANSACTIONS _RAW_STM.
C. TRANSACTIONS _STMwill be stale and will need to be re-created
D. Reading from the traksactioks_3T>: stream will succeed for some time after the expected STALE_TIME.
正解：C
解説: (Topexam メンバーにのみ表示されます)