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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Related Certifications: | SnowPro Core Certification |
| Exam Format: | Scenario-based questions, Multiple choice, Multiple select |
| Available Languages: | English |
| Passing Score: | Not publicly disclosed (Snowflake uses scaled scoring) |
| Exam Price: | $375 USD |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | Approximately 65 questions |
| Exam Duration: | 115 minutes |
| Recommended Training: | SnowPro Advanced Data Engineer Exam Guide Snowflake University Training |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake DEA-C01 Sample Questions |
| Exam Way: | Online proctored or authorized testing center |
| Pre Condition: | Recommended: SnowPro Core Certification or equivalent Snowflake experience |
| Official Syllabus URL: | https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/ |
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NEW QUESTION # 124
Select the incorrect statement while working with warehouses?
Answer: D
Explanation:
Explanation
Resizing a warehouse doesn't have any impact on statements that are currently being executed by the warehouse. When resizing to a larger size, the new compute resources, once fully provisioned, are used only to execute statements that are already in the warehouse queue, as well as all future statements submitted to the warehouse.
NEW QUESTION # 125
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?
Answer: B,C
Explanation:
Explanation
SYSTEM$CLUSTERING_DEPTH:
Computes the average depth of the table according to the specified columns (or the clustering key defined for the table). The average depth of a populated table (i.e. a table containing data) is always 1 or more. The smaller the average depth, the better clustered the table is with regards to the speci-fied columns.
Calculate the clustering depth for a table using two columns in the table:
SELECT SYSTEM$CLUSTERING_DEPTH('TPCH_PRODUCT', '(C2, C9)');
SYSTEM$CLUSTERING_INFORMATION:
Returns clustering information, including average clustering depth, for a table based on one or more columns in the table.
SELECT SYSTEM$CLUSTERING_INFORMATION('SAMPLE_TABLE', '(col1, col3)');
NEW QUESTION # 126
A Data Engineer is writing a Python script using the Snowflake Connector for Python. The Engineer will use the snowflake. Connector.connect function to connect to Snowflake The requirementsare:
*Raise an exception if the specified database schema or warehouse does not exist
*improve download performance
Whichparameters of the connect function should be used? (Select TWO).
Answer: C,E
Explanation:
Explanation
The parameters of the connect function that should be used are client_prefetch_threads and validate_default_parameters. The client_prefetch_threads parameter controls the number of threads used to download query results from Snowflake. Increasing this parameter can improve download performance by parallelizing the download process. The validate_default_parameters parameter controls whether an exception should be raised if the specified database, schema, or warehouse does not exist or is not authorized. Setting this parameter to True can help catch errors early and avoid unexpected results.
NEW QUESTION # 127
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)
Answer: A,B,F
Explanation:
Explanation
The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries. The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.
NEW QUESTION # 128
Does sensitive data in Snowflake is modified in an existing table while applying Masking policies?
Answer: B
Explanation:
Explanation
Snowflake supports masking policies as a schema-level object to protect sensitive data from unau-thorized access while allowing authorized users to access sensitive data at query runtime. This means that sensitive data in Snowflake is not modified in an existing table (i.e. no static masking). Rather, when users execute a query in which a masking policy applies, the masking policy condi-tions determine whether unauthorized users see masked, partially masked, obfuscated, or tokenized data.
NEW QUESTION # 129
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