DAA-C01인기자격증, DAA-C01시험대비최신버전문제

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DAA-C01시험대비 최신버전 문제 - DAA-C01높은 통과율 덤프샘플문제

인재도 많고 경쟁도 많은 이 사회에, 업계인재들은 인기가 아주 많습니다.하지만 팽팽한 경쟁률도 무시할 수 없습니다.많은 Snowflake인재들도 어려운 인증시험을 패스하여 자기만의 자리를 지키고 있습니다.우리ExamPassdump에서는 마침 전문적으로 이러한 Snowflake인사들에게 편리하게 시험을 DAA-C01패스할수 있도록 유용한 자료들을 제공하고 있습니다.

최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q44-Q49):

질문 # 44
You are building a Snowsight dashboard that visualizes website traffic data'. The data includes a column 'visit_timestamp' (TIMESTAMP NTZ) and you need to display the number of unique visitors per hour for the last 24 hours. You also want to allow users to filter the data by country. You plan to use a chart to visualize the trend. Which of the following approaches are the MOST efficient and accurate for achieving this?

A. Use the visit_timestamp)' function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter directly in the SQL query.
B. Use the function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
C. Create a calculated field in Snowsight that extracts the hour from the 'visit_timestamp' and then group by that calculated field to calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
D. Use the 'TO_CHAR(visit_timestamp, 'YYYY-MM-DD HH24')' function to group data by hour, but only use this option if 'visit_timestamp' is already stored as TEXT.
E. Create a view that pre-aggregates the hourly visitor counts and includes the country. The dashboard queries this view and applies no additional filters.

정답：A,E

설명：
Option A is a good approach because 'DATE _ TRUNC is efficient for truncating timestamps, and applying the filter directly in the SQL query can optimize data retrieval. Option D is also very efficient, as it pre-aggregates the data, which improves dashboard performance. Option B is less efficient because the 'HOUR' function only returns the hour value without the date, making it harder to filter for last 24 hours. Also, dashboard-level filters can sometimes be less performant than SQL-level filters for large datasets. Option C introduces a calculated field, which is generally less efficient than performing the transformation directly in SQL. Option E is technically correct, it only applies to TEXT stored visit timestamp. Thus, pre-aggregation and 'DATE_TRUNC' are superior.

질문 # 45
A Snowflake data pipeline utilizes Snowpipe to ingest data from cloud storage into a staging table. After the data is ingested, a series of transformation tasks process the data and load it into a target table. Due to network connectivity issues, Snowpipe occasionally experiences delays in loading data, causing downstream tasks to fail. Which strategies can be implemented to monitor the latency and improve the resilience of this pipeline?

A. Configure a Snowflake resource monitor to track the Snowpipe's credit consumption and trigger alerts when consumption exceeds a threshold.
B. Implement a check within the transformation tasks to verify that the staging table contains data newer than a specific timestamp before proceeding with the transformation. Use ' on the staging table to get the last updated timestamp.
C. Implement error handling within the transformation tasks to gracefully handle the case where the staging table is empty and log the error to a dedicated logging table.
D. Implement a retry mechanism within the transformation tasks that waits for a specified amount of time and then checks the staging table for new data before proceeding.
E. Increase the 'MAX CONCURRENCY parameter of the Snowpipe to improve the ingestion rate.

정답：B,C,D

설명：
Option A is correct because checking the last updated timestamp allows the task to skip its execution or alert if data isn't timely. Option C is correct as it offers a resilient approach. Option E is also a correct since error logging is a critical part of resilience. Option B addresses throughput, not necessarily latency or resilience related to external issues, increasing concurrency does not guarantee latency improvements if connectivity is the issue. Option D only monitors credit consumption, not pipeline latency.

질문 # 46
You are tasked with enriching a customer dataset in Snowflake. The 'CUSTOMER DATA table contains customer IDs and country codes. You have a separate 'COUNTRY INFORMATION' table that contains country codes and corresponding currency codes. Both tables reside in the 'RAW DATA schema of the 'ANALYTICS DB' database. You need to create a view called ENRICHED CUSTOMER DATA' in the 'TRANSFORMED DATA' schema that joins these tables to add currency information to the customer data'. You want to optimize this view for performance. Which of the following approaches would be the MOST efficient and scalable, considering potential data volume increases?

A. Use a User-Defined Function (UDF) to look up the currency code from the "COUNTRY_INFORMATION' table based on the customer's country code.
B. creates a materialized view with clustering enabled on the 'COUNTRY_CODE column after joining 'CUSTOMER_DATX and 'COUNTRY_INFORMATION'.
C. Create a secure view joining the two tables and granting access to users.
D. Create a materialized view using a simple JOIN between 'CUSTOMER_DATA' and 'COUNTRY_INFORMATION'.
E. Create a standard view using a JOIN between 'CUSTOMER DATA' and 'COUNTRY INFORMATION'. Refresh the view regularly using a scheduled task.

정답：B

설명：
Materialized views generally provide better performance than standard views for complex queries, especially with joins. Clustering on the 'COUNTRY_CODE column further enhances performance by physically organizing the data based on this column, making lookups more efficient. Using secure view wouldn't impact on performance. Regular view refreshment using scheduled task is not as efficient compared to materialized view. UDFs can introduce performance overhead, especially for large datasets.

질문 # 47
You have a Snowflake environment where different data analysts run a variety of ad-hoc queries against the same set of tables. You've noticed inconsistent query performance, with some queries running quickly and others taking much longer despite having similar logic. To better manage costs and optimize performance, which of the following strategies would be MOST effective in leveraging virtual warehouse caching and resource management in Snowflake? (Select TWO)

A. Configure the 'AUTO SUSPEND parameter on all virtual warehouses to be a very short duration (e.g., 60 seconds) to minimize costs when the warehouse is idle.
B. Use resource monitors to limit the credit usage of individual virtual warehouses or user groups to control costs and prevent runaway queries.
C. Create separate virtual warehouses for different groups of analysts or types of queries to isolate workloads and prevent resource contention.
D. Implement a single, large virtual warehouse shared by all data analysts to maximize resource utilization and caching benefits.
E. Disable result caching globally at the account level to ensure that all queries always retrieve the most up-to-date data.

정답：B,C

설명：
Creating separate virtual warehouses (B) allows you to isolate workloads, preventing resource contention and ensuring consistent performance for different groups of analysts or types of queries. Resource monitors (D) help control costs by limiting credit usage, preventing runaway queries from consuming excessive resources. Sharing a single, large warehouse (A) can lead to resource contention. Short 'AUTO_SUSPEND (C) can lead to frequent warehouse startups, negating caching benefits. Disabling result caching (E) defeats a key performance optimization mechanism.

질문 # 48
You are preparing to load data from an internal stage that contains multiple CSV files into a Snowflake table. You need to ensure that only files matching a specific pattern are loaded (e.g., files starting with 'sales_'). You also need to transform the date column (located in the third position of each CSV file) from 'YYYYMMDD' to 'YYYY-MM-DD' format during the load. The target table 'SALES has columns 'product_id', and 'region'. Which combination of options correctly filters the files and transforms the date format during the load process?

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

정답：E

설명：
Option D is correct. It uses the 'PATTERN' parameter to filter files starting with 'sales_' and ending with '.csv'. It then uses the TRANSFORM AS SELECT clause to select and transform the data. Inside the select clause, it correctly uses 'substr' to reformat the date and 'TO_DATE to convert the re-formatted string into a DATE datatype. The 'ON_ERROR = 'SKIP_FILE" is a good practice for handling potential errors. Option A has an invalid parameter declaration within the COPY INTO command and does not use PATTERN, making the filter inaccurate. Option B attempts to define date format at file format level, but date transformatinon is required and FILES filter declaration is invalid. Option C uses 'TRANSFORMATION' instead of 'TRANSFORM AS SELECT , which is incorrect and will cause an error. Option E attempts to define DATE FORMAT with the wrong TYPE and is missing concatination of DATE string.

질문 # 49
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