100% Pass Snowflake - DAA-C01 - Newest SnowPro Advanced: Data Analyst Certification Exam Questions
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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q58-Q63):
NEW QUESTION # 58
A Data Analyst created two tables that are related by a foreign key, using the following commands:
Then the Analyst renamed the referenced table using the following command: ALTER TABLE mother RENAME TO father; What happens to the foreign key after the referenced table is renamed?
- A. The foreign key becomes invalid because the referenced table no longer exists.
- B. The foreign key will not be used during the query rewrite.
- C. The foreign key is updated to reference the renamed table.
- D. The foreign key is dropped because the reference is no longer valid.
Answer: C
Explanation:
In Snowflake, table relationships established via constraints are managed at the metadata level. When a table that is referenced by a foreign key is renamed, Snowflake automatically maintains the integrity of that relationship by updating the internal metadata pointers.
Unlike some legacy database systems where a rename might break hardcoded references, Snowflake's architecture ensures that the Foreign Key in the son table remains valid. After executing ALTER TABLE mother RENAME TO father;, the constraint son_fk will be automatically updated by the system to reference the new table name, father. If you were to run a SHOW TRANSACTIONS or GET_DDL command on the son table after the rename, you would see that the REFERENCES clause now points to father(id).
Evaluating the Options:
* Options A and B are incorrect because Snowflake is designed to prevent "orphaned" constraints during standard DDL operations like renaming. The relationship is preserved.
* Option C is incorrect because Snowflake's query optimizer can still utilize these constraints for query rewrite and join elimination optimizations, provided the constraints are defined, regardless of the table's current name.
* Option D is the 100% correct answer. This automatic update behavior is a core feature of Snowflake's metadata-driven approach to Data Transformation and Data Modeling, allowing for flexible schema evolution without breaking existing relational structures.
NEW QUESTION # 59
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?
- A. Presenting data visually increases complexity in analysis.
- B. Visualizations limit data exploration and analysis capabilities.
- C. They enhance data comprehension, aiding effective analysis.
- D. Visualizations don't impact data comprehension or analysis significantly.
Answer: C
Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.
NEW QUESTION # 60
How do exploratory ad-hoc analyses differ from routine analysis?
- A. Ad-hoc analyses deviate from established routines, exploring patterns and anomalies in data.
- B. Ad-hoc analyses focus on anomalies and established trends.
- C. Ad-hoc analyses rely heavily on predefined queries.
- D. They involve querying known patterns without exploring further.
Answer: A
Explanation:
Ad-hoc analyses deviate from established routines, exploring patterns and anomalies in data beyond predefined queries.
NEW QUESTION # 61
You have a large dataset of IoT sensor readings stored in compressed JSON files within an AWS S3 bucket. Each JSON file contains an array of sensor readings with the following structure:
You need to load this data into a Snowflake table named 'sensor data' with columns 'sensor id', 'timestamp', 'temperature', and 'humidity'. Which of the following Snowflake commands would be the MOST efficient and appropriate to ingest this data, assuming you have already created the table and a named stage pointing to the S3 bucket?
- A. Option C
- B. Option E
- C. Option B
- D. Option A
- E. Option D
Answer: D
Explanation:
Option A is the most efficient and direct way to load the data. = TRUE' correctly handles the array of JSON objects within each file. Option B would fail because it doesn't strip the outer array. Option C is incorrect as it treats the JSON as CSV. Option D, although functional, involves creating a temporary table and flattening, making it less efficient. Option E is invalid syntax, and unnecessarily complex.
NEW QUESTION # 62
In Snowflake, how does leveraging Time Travel contribute to handling data anomalies and ensuring data integrity?
- A. Provides real-time data updates
- B. Enables querying data as of specific timestamps
- C. Restricts data access for specific user roles
- D. Validates data types for consistency
Answer: B
Explanation:
Time Travel in Snowflake allows querying data as of specific timestamps, aiding in addressing data anomalies and ensuring data integrity by accessing historical data versions.
NEW QUESTION # 63
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