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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q246-Q251):

NEW QUESTION # 246
You are investigating a performance bottleneck in a frequently used Snowflake query. You suspect the bottleneck might be due to data skew in a particular table. Which Snowflake system function and query combination would be the MOST efficient way to collect data to diagnose data skew on the 'orders' table, specifically for the column?

Answer: D

Explanation:
Option B directly addresses data skew by grouping by 'order_date' and counting occurrences, then ordering by count in descending order to show the most frequent dates. The LIMIT 10 clause ensures that only the dates with the highest counts are returned, improving efficiency. Option A only provides an approximate distinct count, not showing the distribution. Option C is used to estimate query cost and not identify data skew. Option D identifies dates with counts above average, which doesn't necessarily indicate skew in the TOP most frequent dates. Option E only shows table properties.

NEW QUESTION # 247
A Snowflake table 'SALES_DATA' contains a 'TRANSACTION_ID' (VARCHAR), 'AMOUNT (VARCHAR), and 'TRANSACTION DATE (VARCHAR) column. Some 'TRANSACTION_ID' values are alphanumeric, others are purely numeric. The 'AMOUNT' column sometimes contains currency symbols ('$', ' ') or commas, and 'TRANSACTION DATE' is in 'MM/DD/YYYY' format. You need to perform the following transformations: 1. Extract only numeric 'TRANSACTION ID's. 2. Convert "AMOUNT' to a numeric type for calculations, removing currency symbols and commas. 3. Convert 'TRANSACTION DATE to a DATE type. Which of the following SQL queries effectively accomplishes these data type transformations in Snowflake?

A. Option D
B. Option E
C. Option B
D. Option A
E. Option C

Answer: B

Explanation:
Option E is the most comprehensive and robust solutiom - It uses 'REGEXP LIKE to filter out non-numeric and the CASE statement, which is important because 'TRANSACTION_lD's will have both numeric and alphanumeric values. - The 'AMOUNT' column correctly uses 'REGEXP_REPLACE and 'TRY_CAST to handle multiple currency symbols and converts the values to DECIMAL. -is used which handles incorrect data in DATE conversion and return NULL in case of invalid 'TRANSACTION_DATE. Option A is incorrect because IS_INTEGER is not a standard built-in Snowflake function. Option B can cause errors if the TRANSACTION_ID cannot be converted to INTEGER after being checked with REGEXP LIKE. Option C's CAST statements can cause errors if there's any data that cannot be correctly CAST.

NEW QUESTION # 248
You're working with product catalog data in Snowflake. The product information is stored in a table named 'PRODUCTS' , and a key attribute, 'attributes' , contains a semi-structured JSON object for each product. This 'attributes' object can have varying keys, but you are interested in extracting specific keys and pivoting them into columns. The relevant JSON structure is as follows : { "color": "red", "size": "L", "material": "cotton", "style": "casual"} '"What method is the MOST efficient to transform this data to a relational structure, assuming you want to analyze product attributes such as 'color' and 'size' as separate columns?

A. Using dynamic SQL to generate a query that extracts the required attributes using JSON path accessors and then creates a new table.
B. Creating a new table with a 'VARIANT column for the attributes and performing transformations in a BI tool.
C. Using a stored procedure to iterate through each row, parse the JSON, and update a new table with pivoted columns.
D. Creating a view with direct JSON path accessors (e.g., for each desired attribute.
E. Using LATERAL FLATTEN to unnest the 'attributes' and then using a CASE statement to pivot the data.

Answer: D

Explanation:
Option B is the most efficient. Directly accessing the JSON elements using path accessors like allows Snowflake to optimize the query execution, which typically offers superior performance compared to flattening and pivoting with 'CASE statements. Flattening (Option A) introduces unnecessary complexity and overhead when specific attributes are known and desired. Options C and D are generally inefficient and should be avoided for this type of transformation. Creating a view is more performant and simple. Option E is overkill and introduces complexity that isn't needed since the required attributes are known.

NEW QUESTION # 249
You are designing a data pipeline in Snowflake that ingests data from multiple external sources with varying schemas and data quality. After ingestion, you need to standardize the data format, handle missing values, and perform data type conversions before loading it into your analytical tables. You need to implement a reusable and maintainable solution. Which approach minimizes code duplication and maximizes data quality?

A. Create separate SQL scripts for each data source to handle the specific data cleaning and transformation requirements.
B. Use Snowflake's pipes and Snowpipe to load raw data into staging tables, then use a combination of dynamic SQL, user-defined functions (UDFs), and stored procedures to perform the data cleaning and transformation in a modular and reusable manner.
C. Use Snowflake's external tables to directly query the data in its raw format and perform the data cleaning and transformation on-the-fly during query execution.
D. Implement a centralized stored procedure that accepts the data source name as a parameter and performs all data cleaning and transformation logic based on conditional statements (CASE statements).
E. Ingest the data into a single large table without any transformation, and rely on business intelligence tools to handle data cleaning and transformation during analysis.

Answer: B

Explanation:
Option C is the most robust and maintainable approach. It leverages Snowflake's features (pipes, Snowpipe, dynamic SQL, UDFs, and stored procedures) to create a modular and reusable data pipeline. Pipes and Snowpipe handle data ingestion efficiently. Dynamic SQL allows for building flexible queries based on metadata. UDFs encapsulate reusable data transformation logic. Stored procedures orchestrate the entire process. Options A and B lead to code duplication and are difficult to maintain. Option D can be inefficient for complex transformations. Option E pushes data quality issues to the BI layer, which is not ideal.

NEW QUESTION # 250
You are tasked with building a data ingestion pipeline to retrieve data from a transactional database using Change Data Capture (CDC).The source database is a MySQL instance. Which of the following approaches is MOST suitable for efficiently retrieving and loading the changed data into Snowflake while minimizing latency?

A. Use a scheduled task to periodically query the MySQL database for records modified within a specific time window and load them into Snowflake using COPY INTO. Implement a timestamp-based or version-based change tracking mechanism in the query.
B. Create a Snowpark Python UDF that connects to the MySQL database, reads the binary logs, and applies the changes directly to Snowflake tables.
C. Use a third-party CDC tool that supports MySQL as a source and Snowflake as a target. Configure the tool to capture changes from the MySQL binary logs and stream them directly into Snowflake tables.
D. Configure MySQL binary log replication to a cloud storage location (e.g., AWS S3, Azure Blob Storage, Google Cloud Storage) and use Snowpipe to continuously ingest the binary log files into Snowflake.
E. Create a Snowflake external table pointing to the MySQL database. Use the external table to read data directly from the MySQL database into Snowflake tables.

Answer: C,D

Explanation:
Options B and C are the most efficient and reliable for CDC. Option B leverages the MySQL binary logs directly. Configuring replication to cloud storage and using Snowpipe provides a continuous and near real-time ingestion pipeline. Option C, using a dedicated CDC tool, offers a managed solution that handles the complexities of binary log parsing, change tracking, and data transformation. Option A is less efficient due to the need for periodic polling and potential data loss if changes occur between polls. Option D involves significant coding complexity and potential performance issues. Option E is generally not recommended for transactional data due to performance limitations.

NEW QUESTION # 251
......

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DAA-C01 LATEST EXAMPREP | DAA-C01 LEARNING MODE

DAA-C01 Latest Examprep | DAA-C01 Learning Mode