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| Section | Objectives |
|---|---|
| Topic 1: Data Loading and Unloading | - Data export
|
| Topic 2: Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Topic 3: Security, Governance, and Data Sharing | - Data sharing and governance
|
| Topic 4: Data Modeling and Performance Optimization | - Performance tuning
|
| Topic 5: Data Transformation and Analysis | - Analytical workloads
|
>> DAA-C01 Valid Test Objectives <<
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NEW QUESTION # 12
You are working with a table named 'PRODUCT DESCRIPTIONS that contains product descriptions in a 'description' (VARCHAR) column. You need to implement a solution to identify potentially sensitive information within these descriptions, specifically looking for mentions of credit card numbers or social security numbers (SSNs). You want to flag any description that contains either of these patterns. Which of the following Snowflake SQL snippets, leveraging scalar string functions and regular expressions, provides the most efficient and accurate way to achieve this? (Assume that valid credit card numbers are 16 digits and valid SSNs are in the format 'XXX-XX-XXXX'). Select all correct options.
Answer: A,B
Explanation:
Options A and C are both correct. Option A uses 'REGEXP_LIKE with two separate regular expressions to search for a 16-digit number (credit card) and a 'XXX-XX-XXXX' pattern (SSN). Option C uses a single 'REGEXP LIKE' function with an alternation to combine both patterns into one regular expression , which is generally more efficient. Option B relies on 'CONTAINS' which performs a simple substring search, not regular expression matching; this will not accurately identify the patterns. Option D uses 'STARTS WITH' which will only identify descriptions that begin with the specified keywords, missing most cases. Option E uses 'LIKE operator for the mentioned words in the sentence that can be 'Credit Card' or 'Social Security Number', but this isn't a search for Credit card numbers or SSNs.
NEW QUESTION # 13
You have a table named 'event_data' that tracks user activities. The table contains 'event_id' (INT), 'user _ id' (INT), (TIMESTAMP NTZ), 'event_type' (VARCHAR), and 'event_details' (VARIANT). The table is partitioned by Performance on queries filtering by both 'event_type' and a specific date range on is slow You suspect inefficient partition pruning and JSON parsing as potential bottlenecks. Which combination of actions will most effectively address these performance issues?
Answer: D
Explanation:
Option B provides the most effective solution. Creating a materialized view addresses both problems: Partitioning by ensures efficient partition pruning when querying by date ranges. Clustering by 'event_type' improves performance when filtering on this column. Pre-extracting fields from sevent_detailS into separate columns avoids expensive JSON parsing at query time. Option A, adding index will not perform better than partition pruning. Option C changing partition key will require full reload of data and clustering table is expensive. Option D, masking policy will secure sensitive data but won't resolve performance issues. Option E, creating a temporary table and performing a Merge operation will increase cost and time.
NEW QUESTION # 14
You are tasked with analyzing website clickstream data stored in a Snowflake table named 'clickstream_data'. The table contains a 'variant' column named 'event data' that holds JSON data representing various events like 'page view', 'button click', and 'form submission'. You need to extract the 'page_url' from 'page_view' events, the 'button id' from 'button click' events, and the 'form id' from 'form submission' events. Design the most efficient Snowflake SQL query to achieve this, considering the performance implications of variant parsing and filtering. Assume the 'event_type' is directly available within the variant object as well. Which of the following queries would be most efficient?
Answer: B
Explanation:
Option C is the most efficient. Snowflake's native variant access is generally faster than using 'PARSE_JSON' Also, explicit casting to VARCHAR ensures consistency in the output data type. Option A although correct will produce an output that is of variant type for all fields. Option B is incorrect because if none of the when conditions matches, then it returns NULL. Option D uses PARSE JSON unneccessarily making it slower. Option E uses GET which is used to extract data from an object within VARIANT and not to directly evaluate the VARIANT column.
NEW QUESTION # 15
What types of Snowflake functions are available for data analysis and manipulation? (Select all that apply)
Answer: A,B,C
Explanation:
Snowflake functions include scalar, aggregate, and system functions for data analysis and manipulation.
NEW QUESTION # 16
A financial institution has implemented both Row Access Policies and Dynamic Data Masking. The Row Access Policy restricts access to transaction data based on the user's department (e.g., 'Fraud Detection', 'Compliance'). Dynamic Data Masking is applied to the 'ACCOUNT NUMBER column, masking all but the last four digits. The institution wants to build a report that shows the distribution of transaction amounts across different departments. Analysts in the 'Compliance' department need to be able to see the full, unmasked 'ACCOUNT NUMBER when investigating potential regulatory violations for transactions within their department only , while still adhering to the Row Access Policy. Which of the following approaches is the MOST secure and compliant way to implement this?
Answer: B
Explanation:
Option B is the most secure and compliant solution. By modifying the Dynamic Data Masking policy to include a CASE statement, you can conditionally unmask the 'ACCOUNT_NUMBER only when both the user is in the 'Compliance' department AND the Row Access Policy grants them access to the relevant transaction data. This ensures that the masking policy is only bypassed when absolutely necessary and that the Row Access Policy remains in effect. Option E is problematic because the UNMASK privilege is too broad and bypasses masking on all tables and columns. Using caller rights (option C) bypasses Row Access Policies too. Role hierarchies don't inherently affect masking. They change object privileges. Option A allows inheritance of the roles that the user is executing, however in the current context, the user will still get blocked due to ROW level security policy.
NEW QUESTION # 17
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