DAA-C01 Exam Dumps : SnowPro Advanced: Data Analyst Exam

In Snowflake, the performance of a metadata-based query (like MAX, MIN, or COUNT) is typically near-instantaneous because Snowflake maintains constant-time statistics in its Cloud Services layer. For a standard table, SELECT MAX(num) does not even require a virtual warehouse to be active; it simply reads the value from the table's metadata.

However, when a Row Access Policy (RAP) is applied to a table, the query's behavior changes fundamentally. A row access policy is a security feature that restricts which rows are visible to a user based on their role or other attributes. To enforce this policy, Snowflake can no longer rely on the high-level metadata of the entire table because it must first determine which specific rows the user is authorized to see. Consequently, the query engine must scan the individual micro-partitions and evaluate the policy logic for every row (or block of rows) to filter out unauthorized data before calculating the maximum value. This turns a "metadata-only" operation into a data-scanning operation, which requires a running warehouse and significantly more time.

Evaluating the Options:

Option A is incorrect because the prompt states the tables are "similarly-sized." Even if it were slightly larger, a metadata lookup for a standard table would still be nearly instant.

Option C is incorrect because a multi-cluster warehouse helps with concurrency (multiple users), not the raw execution speed of a single simple aggregate query.

Option D is incorrect because USE_CACHED_RESULT refers to the Query Result Cache. While turning it off would prevent a "0ms" response from a previous run, it wouldn't explain a "significant" slowdown compared to a standard metadata fetch.

Option B is the 100% correct answer. The presence of a Row Access Policy forces a full data scan and policy evaluation, which is the most common reason for performance degradation in otherwise simple metadata queries.

The SAMPLE clause (or TABLESAMPLE) is used in Snowflake to return a subset of rows from a table. When performing analysis on massive datasets, sampling allows for faster query execution and reduced credit consumption while still providing a statistically representative view of the data.

There are two primary methods of sampling in Snowflake: BERNOULLI (row-based) and BLOCK (partition-based). The query in this question uses BLOCK sampling, which selects a specific percentage of micro-partitions (blocks) rather than individual rows. This method is significantly faster for very large tables because it avoids the overhead of scanning every single row within a block; it either includes the entire block or skips it entirely.

Evaluating the Syntax:

Probability: The value inside the parentheses (0.012) represents the probability percentage for inclusion. Unlike some systems that might use decimals (where 1.0 = 100%), Snowflake treats this number as a direct percentage. Therefore, 0.012 is exactly 0.012%, not 1.2%.

Repeatable/Seed: The REPEATABLE clause (or SEED) followed by a number (99992) ensures that the sampling is deterministic. If the underlying data does not change, running this same query multiple times with the same seed will return the exact same "random" subset of blocks.

Evaluating the Options:

Options A and C are incorrect because they misinterpret the probability 0.012 as 1.2%.

Option D is incorrect because it mistakenly identifies the seed number 99992 as a target row count.

Option B is the 100% correct answer as it accurately identifies the sampling method (BLOCK), the correct percentage probability (0.012%), and the role of the seed (99992).

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.