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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q62-Q67):NEW QUESTION # 62
You have a Snowflake table 'CUSTOMER DATA' containing customer information. You want to enrich this data using two separate data shares from the Snowflake Marketplace. Share A provides demographic information, and Share B provides credit risk scores. Both shares contain views named 'CUSTOMER ENRICHMENT with a common column 'CUSTOMER ID'. Due to compliance requirements, you need to ensure that only customers with a credit risk score above a certain threshold (e.g., 700) are enriched with demographic data'. Which of the following approaches ensures that the customer data is enriched securely, efficiently, and in compliance with the credit risk threshold?
  • A. Create a task that periodically runs a query that joins 'CUSTOMER DATA with both shared views (CUSTOMER ENRICHMENT from Share A and Share B), filtering based on the credit risk threshold. Insert the results into a new enriched table.
  • B. Replicate data from both data shares and perform enrichment and credit risk filtering on the replicated data.
  • C. Create a stored procedure that iterates through the 'CUSTOMER_DATA' table, retrieves demographic and credit risk information for each customer, applies the credit risk threshold, and inserts the enriched data into a new table.
  • D. Create a single view that joins 'CUSTOMER_DATA' with both shared views ('CUSTOMER_ENRICHMENT from Share A and Share B) using a common table expression (CTE) to filter records from Share B to include only customers with a credit risk score above the defined threshold.
  • E. Create two separate views, one for each data share, and then join them based on in a final view, filtering for the credit risk threshold in the final view.
Answer: D
Explanation:
Option C is the most secure, efficient, and compliant approach. Using a single view with a CTE allows you to encapsulate the credit risk filtering logic within the view definition, ensuring that only customers meeting the threshold are enriched. This avoids exposing sensitive credit risk information to unauthorized users. Views provide row-level security for the table. Options A requires two steps and is less efficient, Option B is less efficient and harder to maintain than a view, and Options D is not recommended for sharing scenarios. Option E will require an additional task creation. Using single view with CTE will simplify the query to implement it with minimum code.

NEW QUESTION # 63
What will the following query return?
SELECT * FROM testtable SAMPLE BLOCK (0.012) REPEATABLE (99992);
  • A. A sample of a table in which each block of rows has a 1.2% probability of being included in the sample, with the seed set to 99992.
  • B. A sample of a table in which each block of rows has a 1.2% probability of being included in the sample where repeated elements are allowed.
  • C. A sample of a table in which each block of rows has a 0.012% probability of being included in the sample, with the seed set to 99992.
  • D. A sample containing 99992 records of a table in which each block of rows has a 0.012% probability of being included in the sample.
Answer: C
Explanation:
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).

NEW QUESTION # 64
How can a Data Analyst automatically create a table structure for loading a Parquet file?
  • A. Use the INFER_SCHEMA together with the CREATE TABLE LIKE command.
  • B. Use INFER_SCHEMA together with the CREATE TABLE USING TEMPLATE command.
  • C. Use the GENERATE_COLUMN_DESCRIPTION with the CREATE TABLE USING TEMPLATE command.
  • D. Use the GENERATE_COLUMN_DESCRIPTION with the CREATE TABLE LIKE command.
Answer: B
Explanation:
Manually defining table structures for complex semi-structured files like Parquet can be error-prone and time- consuming. Snowflake provides a specific automation workflow to handle this, involving the detection of the file's internal schema and the dynamic creation of a matching table.
The process starts with the INFER_SCHEMA function. Because Parquet files are self-describing, they contain metadata about their columns and data types. INFER_SCHEMA reads this metadata from files in a stage and returns a list of column names and types. To turn this list into an actual table, the analyst uses the CREATE TABLE ... USING TEMPLATE syntax. This command takes the output of INFER_SCHEMA as an input and automatically builds a table with the corresponding definition.
Evaluating the Options:
* Option A is incorrect because CREATE TABLE LIKE is used to copy the structure of an existing table
, not to build a new one from file metadata.
* Option C and D are incorrect because GENERATE_COLUMN_DESCRIPTION is a helper function used to create a formatted string of column definitions, but it is not the primary command used with USING TEMPLATE for automated table creation.
* Option B is the Correct answer. The combination of INFER_SCHEMA (to find the columns) and USING TEMPLATE (to build the table) is the standard Snowflake pattern for schema-on-read automation in Data Ingestion workflows.

NEW QUESTION # 65
You have a table 'product_catalog' containing a 'description' column of type TEXT, and a 'tags' column which is a VARIANT containing an array of strings representing tags associated with the product. You need to build an efficient search mechanism that allows users to find products matching specific tags. Considering scalability and performance for large catalogs, which of the following methods using table functions and Snowflake's search capabilities would be most suitable? Choose all that apply.
  • A. Create a search optimization service on the 'product_catalog' table including the 'description' column. When querying, use a combination of CONTAINS() for 'description' and ARRAY_CONTAINS() on the 'tags' column.
  • B. Create a view that flattens the 'tags' array using LATERAL FLATTEN into a 'tag' column, and then create a full-text index on the 'description' column. Query the view using CONTAINS() or LIKE operator on the 'description' and EQUALS operator on the 'tag' column.
  • C. Use a Java UDF to iterate over the 'tags' array and check if any of the tags match the search terms. Apply this UDF in a WHERE clause along with a CONTAINS() check on the 'description'
  • D. Create a search optimization service on the 'product_catalog' table including the 'description' and 'tags' columns. Use LATERAL FLATTEN to expand the 'tags' array and then create an index on the flattened 'tag' values.
  • E. Create a search optimization service on the 'product_catalog' table including the 'description' and 'tags' columns. When querying, use a combination of CONTAINS() for 'description' and ARRAY_CONTAINS() on the 'tags' column and a 'SEARCH' clause to filter results.
Answer: A,E
Explanation:
Search optimization service in Snowflake is designed to accelerate search queries and is best practice here. Using 'ARRAY on the 'tags' column lets you directly check if the array contains specific tags. Using on the 'description' column can search for specific search terms in your description. Using a 'SEARCH' clause can improve search performnce significantly. Option C and E, are both correct, since they use contains as well as the array_contains but option E includes the use of Search which is more effiecient. Option A is incorrect, as indexes are not allowed on flattened data. UDF will have performance issues. Creating a view and indexing the view is not optimal as querying directly with CONTAINS on the tags column gives faster results.

NEW QUESTION # 66
How can stored procedures be beneficial in data analysis using SQL?
  • A. Stored procedures are limited to read-only operations.
  • B. Stored procedures cannot handle large data sets effectively.
  • C. They allow execution of repetitive tasks, enhancing data analysis efficiency.
  • D. Stored procedures can't be used in conjunction with UDFs.
Answer: C
Explanation:
Stored procedures aid in data analysis by enabling the execution of repetitive tasks, thereby enhancing efficiency.

NEW QUESTION # 67
......
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