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Practice Exam Software Snowflake DEA-C02 Exam Questions

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q28-Q33):

NEW QUESTION # 28
You are tasked with loading Parquet files into Snowflake from an AWS S3 bucket. The Parquet files are compressed using Snappy compression and contain a complex nested schem a. Some of the columns contain timestamps with nanosecond precision. You want to create a Snowflake table that preserves the timestamp precision. Which COPY INTO statement options and table definition are MOST appropriate?

A. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE - - pARQUET COMPRESSION = AUTO) ON_ERROR = 'SKIP_FILE';
B. Table Definition: CREATE TABLE my_table (ts VARCHAR, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' = PARSE TIMESTAMP(ts));
C. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' VALIDATION_MODE = RETURN_ERRORS;
D. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';
E. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';

Answer: A

Explanation:
The correct approach is to define the timestamp column with TIMESTAMP NTZ(9) to preserve nanosecond precision. Also, setting COMPRESSION = AUTO is a good practice to let Snowflake automatically detect and handle the compression type, even though Snappy is explicitly mentioned. Option A is close, but AUTO compression is preferred for robustness. B would lose precision as timestamp_ntz defaults to (0), C converts TIMESTAMP to VARCHAR which causes issues with ordering. E will throw errors but does not solve the problem.

NEW QUESTION # 29
Your team is developing a set of complex analytical queries in Snowflake that involve multiple joins, window functions, and aggregations on a large table called 'TRANSACTIONS. These queries are used to generate daily reports. The query execution times are unacceptably high, and you need to optimize them using caching techniques. You have identified that the intermediate results of certain subqueries are repeatedly used across different reports, but they are not explicitly cached. Given the following options, which combination of strategies would MOST effectively utilize Snowflake's caching capabilities to optimize these analytical queries and improve report generation time?

A. Utilize the "RESULT_SCAN' function in conjunction with the query ID of the initial subquery execution to explicitly cache and reuse the results in subsequent queries. This approach requires careful management of query IDs.
B. Consider using 'CACHE RESULT for particularly expensive subqueries or views. This is a hint to snowflake to prioritize caching the result set for future calls.
C. Use temporary tables to store the intermediate results of the subqueries. These tables will be automatically cached by Snowflake and can be reused by subsequent queries within the same session.
D. Create common table expressions (CTEs) for the subqueries and reference them in the main query. CTEs will force Snowflake to cache the results of the subqueries, improving performance.
E. Create materialized views that pre-compute the intermediate results of the subqueries. This will allow Snowflake to automatically refresh the materialized views when the underlying data changes and serve the results directly from the cache.

Answer: B,E

Explanation:
Creating materialized views (D) for the intermediate results is the most effective approach, as Snowflake automatically manages the refresh and caching. 'CACHE RESULT (E) Provides a way to explicitly cache the results. Temporary tables (A) are session-specific and not suitable for persistent caching across reports. CTEs (B) do not guarantee caching and are primarily for query readability. 'RESULT SCAN' (C) is complex to manage and requires manual tracking of query IDs. Therefore, a combination of materialized views and CACHE RESULT will provide the best caching strategy.

NEW QUESTION # 30
You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

A. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;
B. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;
C. CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
D. ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;
E. ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;

Answer: D

Explanation:
The correct syntax for applying a masking policy to a column is 'ALTER TABLE ALTER COLUMN SET MASKING POLICY MODIFY COLUMN' is used for changing data types or constraints, not applying masking policies. The sign after 'SET MASKING POLICY is incorrect syntax. There's no 'APPLY MASKING POLICY command in Snowflake.

NEW QUESTION # 31
You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

A. Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.
B. Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.
C. Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.
D. Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
E. Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.

Answer: D

Explanation:
Since the Time Travel window (1 day) has passed, the 'UNDROP' command and cloning using or 'BEFORE clauses will not work (eliminating options B and D). While option C might be true if no external backups exist, the most appropriate first step is to contact Snowflake support (option A). This is because, even though Time Travel has expired, Fail-safe might still contain the data, offering a potential recovery path, although it is a longer process and not guaranteed. Option E is also a valid answer.

NEW QUESTION # 32
You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

A. Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.
B. Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
C. Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.
D. Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.
E. Restore the entire Snowflake account to a point in time before the incorrect transformation.

Answer: A,D

Explanation:
Options B and E are the correct methods for rolling back changes using Time Travel. Option B clones the table to the desired state and replaces the original, effectively rolling it back. Option E directly overwrites the original table with the historical data obtained through Time Travel. Option A is an extreme measure. Option C is for recovering dropped tables, not rolling back changes within a table. Option D does not directly rollback the table to the previous state, the correct data has to be loaded, that will take more time.

NEW QUESTION # 33
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