DAA-C01考題免費下載 & DAA-C01 PDF題庫

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最新的 SnowPro Advanced DAA-C01 免費考試真題 (Q32-Q37):

問題 #32
When connecting BI tools to Snowflake for dashboard creation, what factors need to be considered for seamless integration? (Select all that apply)

答案：B,C,D

解題說明：
Seamless integration requires considering compatibility, encryption, and network latency between BI tools and Snowflake.


問題 #33
A Data Analyst needs to create a custom filter called state in a Snowflake dashboard using a SQL query.
Which query will include this custom filter?

答案：C

解題說明：
In Snowsight, Snowflake's modern web interface, Filters are used to make dashboards and worksheets interactive. When a Data Analyst creates a custom filter, they define a "SQL Keyword" that acts as a variable within their SQL code. This allows users to select a value from a dropdown or date picker in the UI, which then dynamically updates the query's results.
The standard syntax for referencing a filter variable in a Snowflake worksheet or dashboard is the colon prefix (:) followed by the keyword name. In this specific scenario, the keyword is state. Therefore, the query must use :state to tell Snowflake that this value should be supplied by the dashboard's filter UI rather than being a literal string or a column reference.
Evaluating the Options:
* Option A is the 100% correct syntax. It correctly uses the :state identifier to link the query logic to the dashboard filter.
* Option B is incorrect because the @ symbol is typically used in Snowflake to reference stages (e.g.,
@mystage), not dashboard filters.
* Option C is incorrect because state = state is a tautology that will always evaluate to true for non-null values; it does not reference an external filter.
* Option D is incorrect because 'state' is a string literal. The query would literally look for a state named
"state" rather than using the user's selection.
Using these filters effectively is a core requirement for the Data Presentation and Data Visualization domain, as it enables the creation of self-service analytics tools where non-technical users can explore data subsets without modifying SQL code.


問題 #34
Which Snowflake feature allows users to encapsulate a series of SQL statements into a reusable database object, facilitating modular code development?

答案：B

解題說明：
In Snowflake, a Stored Procedure is the primary object used for procedural logic and the encapsulation of multiple SQL statements. Unlike standard functions that are typically restricted to returning a single value or a table based on an input, Stored Procedures are designed to perform administrative tasks, wrap complex business logic, and execute a sequence of operations-such as DDL (Data Definition Language) and DML (Data Manipulation Language) commands-within a single callable object.
Modular code development is facilitated by Stored Procedures because they allow analysts and developers to write logic once and reuse it across various workflows. For example, a procedure can be written to truncate a staging table, call a COPY INTO command, and then execute several INSERT statements into a final fact table. This "wrapper" approach ensures consistency, as any change to the logic only needs to be updated in the procedure definition rather than in every script or task that uses it.
Evaluating the Options:
* Option B is incorrect because a Materialized View is a pre-computed result set used to improve query performance; it does not "encapsulate a series of statements" for procedural execution.
* Option C is incorrect because while User-Defined Functions (UDFs) provide reusability, they are generally intended for calculations or data transformations that return a result. They are much more restricted than procedures; for instance, a UDF cannot execute DDL commands like CREATE TABLE.
* Option D is incorrect because a Common Table Expression (CTE) is a temporary named result set defined within the execution scope of a single SELECT, INSERT, UPDATE, or DELETE statement. It is not a persistent database object and cannot be reused across different sessions or scripts.
* Option A is the correct answer. Snowflake Stored Procedures can be written in multiple languages (JavaScript, Snowflake Scripting/SQL, Python, Java, or Scala), providing the flexibility needed for sophisticated, modular automation in a data pipeline.


問題 #35
A Data Analyst has a Parquet file stored in an Amazon S3 staging area. Which query will copy the data from the staged Parquet file into separate columns in the target table?

答案：C

解題說明：
In the Snowflake ecosystem, Parquet is treated as a semi-structured data format. When you stage a Parquet file, Snowflake does not automatically parse it into multiple columns like it might with a flat CSV file.
Instead, the entire content of a single row or record is loaded into a single VARIANT column, which is referenced in SQL using the positional notation $1.
The fundamental mistake often made-and represented in Option A-is treating Parquet as a delimited format where $1, $2, and $3 refer to different columns. In Parquet ingestion, columns $2 and beyond will return NULL because the schema is contained within the object in $1.
To successfully "shred" or flatten this semi-structured data into a relational table with separate columns, an analyst must use path notation. This involves referencing the root object ($1), followed by a colon (:), and then the specific element key (e.g., $1:o_custkey). Furthermore, because the values extracted from a Variant are technically still Variants, they must be explicitly cast to the correct data type using the double-colon syntax (e.g., ::number, ::date) to ensure they land in the target table with the correct data types.
Evaluating the Options:
* Option A is incorrect because it uses positional references ($2, $3, etc.) which are only valid for structured files like CSVs.
* Option B is incorrect because it attempts to reference keys directly without the required stage variable ($1) and colon separator.
* Option D is incorrect as it uses a non-standard parse() function that does not exist for this purpose in Snowflake SQL.
* Option C is the 100% correct syntax. It correctly identifies that the Parquet data resides in $1, utilizes the colon to access internal keys, and applies the necessary type casting. This specific method is known as "Transformation During Ingestion" and is a core competency for any SnowPro Advanced Data Analyst.


問題 #36
A ride-sharing company wants to analyze the density of ride requests in different city areas. They have a table 'RIDES' with a 'LOCATION' (GEOGRAPHY) column representing the pickup location of each ride. They want to divide the city into a grid of hexagonal cells and count the number of rides originating in each cell. Which sequence of steps would achieve this, assuming the 'city_boundary' is defined and accessible as a GEOGRAPHY object?

答案：C

解題說明：
'ST HEXGRID is the correct function for generating a hexagonal grid. is used to check if a hexagonal cell contains a ride's location. Aggregating the ride counts for each cell provides the density information.


問題 #37
......

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