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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q186-Q191):

NEW QUESTION # 186
You are working with a Snowflake environment where data analysts need to query Parquet files located in AWS S3. The analysts require the ability to perform complex aggregations and joins with other Snowflake tables. Some of the Parquet files have evolved their schema over time, adding new columns. Given these requirements, which set of actions would provide the MOST efficient and maintainable solution? Choose the THREE most appropriate actions.

A. Implement a process to periodically load the Parquet files into Snowflake internal tables using 'COPY INTO , inferring the schema from the Parquet files and creatin a new table version whenever the schema changes. Create views to handle the multi le table versions.
B. Create an external table in Snowflake pointing to the S3 bucket and use the 'VALIDATE(100);' command frequently to validate if the stage data integrity
C. Develop custom SQL scripts to manually extract data from the external table into temporary tables with specific schemas, then perform aggregations and joins using those temporary tables.
D. Create materialized views on top of the external table, extracting frequently used columns and performing common aggregations to pre-compute results.
E. Create an external table with 'AUTO REFRESH = TRUE' and use schema evolution features to automatically detect and accommodate new columns in the Parquet files. use parameter to tune query performance.
F. Implement a data pipeline that continuously ingests the Parquet data into a Snowflake internal table with a defined schema, ensuring all possible columns are included and using a VARIANT type for potentially evolving fields.

Answer: B,D,E

Explanation:
'AUTO REFRESH = TRUE' handles schema evolution in external tables. Materialized views on the top of external tables provides precomputed data, which is good for aggregations. The VALIDATE command allows analysts to check the stage data integrity and avoid errors in the data pipeline. Ingesting all data into a single VARIANT column is not efficient for aggregations and joins. Extracting to temporary tables is not maintainable. Periodically creating new tables is complex to manage. Adjusting can help improve performance.

NEW QUESTION # 187
What considerations are crucial when performing table joins between parent/child tables for maintaining data integrity? (Select all that apply)

A. Establishing foreign key constraints
B. Data redundancy prevention
C. Ensuring referential integrity
D. Matching data types

Answer: A,C,D

Explanation:
Table joins require considerations such as matching data types, establishing foreign key constraints, and ensuring referential integrity for data integrity maintenance.

NEW QUESTION # 188
How can automated and repeatable tasks contribute to maintaining reports and dashboards in meeting business requirements?

A. They solely increase the complexity of dashboard management.
B. Automated tasks ensure consistency and reduce manual effort.
C. They limit the scalability of dashboards and reports.
D. Repeatable tasks hinder data updates in dashboards.

Answer: B

Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.

NEW QUESTION # 189
You are loading data into a Snowflake table 'PRODUCT PRICES' using the COPY INTO command from a Parquet file stored in Azure Blob Storage. The 'PRODUCT PRICES' table has the following schema: VARCHAR, PRICE CURRENCY VARCHAR, LAST UPDATED TIMESTAMP NTZ. The Parquet file contains all these columns, but the 'LAST UPDATED column is stored as a Unix epoch timestamp (seconds since 1970-01-01 00:00:00 UTC). You need to transform the Unix epoch timestamp to a Snowflake TIMESTAMP NTZ during the data load. Which of the following options correctly demonstrates how to achieve this using a transformation within the COPY INTO command?

Answer: C

Explanation:
The correct answer is C. When using transformations with COPY INTO and an external stage, you must explicitly select the columns using '$1', '$2, etc. The gazure_stage/data.parquet' path is used in the 'FROM' clause, and converts the fourth column (LAST_UPDATED) from the Parquet file to a Snowflake TIMESTAMP. Note: It assumes column ordering in the Parquet file matches the order in the select statement. Using a SELECT statement within the FROM' clause is the correct approach when applying transformations directly during the COPY. The other options are incorrect because they either use the deprecated 'TRANSFORMATION' parameter (now replaced by select statements in the FROM clause) or attempt to apply transformations incorrectly. This is the only answer that is correct and will work. Options A, B, D, and E will all fail during the COPY INTO statement.

NEW QUESTION # 190
You are tasked with optimizing a dashboard displaying sales performance across different regions. The dashboard queries a large table called 'SALES DATA, which is partitioned by 'SALE DATE. The dashboard loads very slowly. The data analyst team notices that queries for the dashboard often filter on a specific region and a date range. Which of the following steps can you take to improve the dashboard's performance? (Select TWO)

A. Recluster the SALES DATA' table on 'REGION' and 'SALE DATE.
B. Create a table clone of 'SALES_DATX and point the dashboard to the clone instead.
C. Create a materialized view that pre-aggregates the data by region and date. The materialized view should be incrementally updated.
D. Create a search optimization policy on the 'SALES DATA' table for the 'REGION' column.
E. Increase the size of the warehouse used to query the 'SALES DATA' table.

Answer: A,C

Explanation:
Creating a materialized view (A) pre-aggregates the data, significantly reducing the amount of data that needs to be processed for each dashboard refresh. Reclustering on 'REGIONS and (D) physically organizes the data on disk based on these frequently used filter columns, improving query performance by minimizing the need to scan irrelevant partitions. Table clones (B) provide a separate copy of the data but don't inherently improve query performance. Search optimization (C) is useful for point lookups, but not as effective for date range filtering in this scenario. Increasing warehouse size (E) helps, but optimizing the data structure offers a more fundamental and cost-effective improvement.

NEW QUESTION # 191
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