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| Section | Objectives |
|---|---|
| Data Loading and Unloading | - Data ingestion methods
|
| Data Transformation and Analysis | - Analytical workloads
|
| Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Data Modeling and Performance Optimization | - Performance tuning
|
| Security, Governance, and Data Sharing | - Access control and security
|
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NEW QUESTION # 55
A data engineer is tasked with auditing changes made to the 'EMPLOYEES' table over the past week. The data retention period for the Snowflake account is set to 7 days. They need to identify all the rows that were updated or deleted during this period. Which of the following approaches, utilizing Snowflake's Time Travel capabilities, will efficiently provide the required audit information, assuming no custom metadata tracking was implemented?
Answer: A
Explanation:
Option C provides the most efficient and comprehensive approach. By querying the 'QUERY HISTORY view, the engineer can identify the specific queries that modified the 'EMPLOYEES table. Then, using Time Travel with the query IDs, they can retrieve the state of the table before those queries were executed, allowing for a precise comparison and identification of the affected rows. Option A is too broad; it provides a snapshot of the table a week ago but doesn't pinpoint specific changes. Option B doesn't allow data extraction of the changes, so incorrect. Option D is computationally expensive and may not be accurate for large tables. Option E is not feasible because the table was already updated, and CDC needs to be setup before the event. Setting up a stream on a historical data point is not possible.
NEW QUESTION # 56
A Data Analyst creates and populates the following table:
create or replace table aggr(v int) as select * from values (1), (2), (3), (4); The Analyst then executes this query:
select percentile_disc(0.60) within group (order by v desc) from aggr;
What will be the result?
Answer: C
Explanation:
The PERCENTILE_DISC (discrete percentile) function is an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification and returns the value from the set that corresponds to that percentile. Unlike PERCENTILE_CONT, which interpolates between values to find a continuous result, PERCENTILE_DISC always returns an actual value from the input set.
In this scenario, we have a set of four values: $\{1, 2, 3, 4\}$. The query specifies a descending order (order by v desc), so the ordered set for the calculation is $\{4, 3, 2, 1\}$.
To find the discrete percentile, Snowflake calculates the cumulative distribution. For a set of $N$ elements, each element represents a percentile rank of $1/N$. With 4 elements, each covers 25% ($0.25$) of the distribution:
* Value 4: Cumulative Percentile $0.25$
* Value 3: Cumulative Percentile $0.50$
* Value 2: Cumulative Percentile $0.75$
* Value 1: Cumulative Percentile $1.00$
The PERCENTILE_DISC(0.60) function looks for the first value whose cumulative distribution is greater than or equal to the specified percentile ($0.60$).
* $0.25$ (Value 4) is not $\ge 0.60$.
* $0.50$ (Value 3) is not $\ge 0.60$.
* $0.75$ (Value 2) is the first value where the cumulative distribution is $\ge 0.60$.
Therefore, the result is 2. If the order had been ascending (ASC), the cumulative distribution would have been
$\{1: 0.25, 2: 0.50, 3: 0.75, 4: 1.00\}$, and the result for $0.60$ would have been 3. Understanding the impact of the ORDER BY clause within the WITHIN GROUP syntax is a critical skill for the Data Analysis domain of the SnowPro Advanced: Data Analyst exam.
NEW QUESTION # 57
You are tasked with validating the 'SALES DATA' table containing sales records. One of the columns, 'SALE AMOUNT', is defined as VARCHAR, but it should be a NUMERIC. Some rows contain non-numeric characters and NULL values represented as the string 'NULL'. You need to identify rows that will cause errors when casting 'SALE AMOUNT to NUMERIC, and replace these rows with valid values using Snowflake SQL. Which of the following SQL statements, when executed in sequence, effectively identifies and corrects problematic 'SALE AMOUNT' values? Note: Assume the "REPLACE INVALID CHARACTERS' UDF correctly replaces non- numeric characters with empty strings.
Answer: C
Explanation:
Option D correctly addresses all aspects of the problem. First, it replaces 'NULL' string values with actual NULLs. Then, it creates a temporary table, 'INVALID SALES, containing rows where 'SALE AMOUNT, after removing invalid characters, cannot be converted to a DECIMAL. Finally, it updates these invalid rows in the original table with a default value of '0'. TRY_TO_DECIMAL is important for handling decimal values that cannot be converted
NEW QUESTION # 58
You are tasked with creating a dashboard to monitor the performance of several marketing campaigns. The dashboard should allow users to quickly identify underperforming campaigns and drill down to understand the reasons. Which of the following combinations of visualizations and data interaction techniques would BEST achieve this goal? Assume that you are using Snowflake as your data source and connecting to a BI tool.
Answer: D,E
Explanation:
Options B and E provide the best combination of visualizations and data interaction techniques. Option B uses a bar chart for easy comparison and cross-filtering to allow for quick drill-down into related data dimensions. Option E utilizes a bullet chart to immediately highlight underperforming campaigns relative to their targets and offers a drill-down for a more in-depth analysis of underlying metrics and customer data. Option A lacks drill-down. Option C is useful for understanding distribution but does not highlight performance against targets or facilitate drill-down. Option D, although useful can quickly see the campaign's performance in one glance, it does not provide in-depth insights into why the campaign isn't performing well.
NEW QUESTION # 59
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?
Answer: D
Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.
NEW QUESTION # 60
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