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Snowflake SnowPro Advanced Administrator 認定 ADA-C01 試験問題 (Q49-Q54):
質問 # 49
MY_TABLE is a table that has not been updated or modified for several days. On 01 January 2021 at 07:01, a user executed a query to update this table. The query ID is
'8e5d0ca9-005e-44e6-b858-a8f5b37c5726'. It is now 07:30 on the same day.
Which queries will allow the user to view the historical data that was in the table before this query was executed? (Select THREE).
- A. SELECT * FROM my table PRIOR TO STATEMENT '8e5d0ca9-005e-44e6-b858-a8f5b37c5726';
- B. SELECT * FROM my_table AT (TIMESTAMP => '2021-01-01 07:00:00' :: timestamp);
- C. SELECT * FROM TIME_TRAVEL ('MY_TABLE', 2021-01-01 07:00:00);
- D. SELECT * FROM my_table BEFORE (STATEMENT => '8e5d0ca9-005e-44e6-b858-a8f5b37c5726');
- E. SELECT * FROM my table WITH TIME_TRAVEL (OFFSET => -60*30);
- F. SELECT * FROM my_table AT (OFFSET => -60*30);
正解：A、B、D
解説：
Explanation
According to the AT | BEFORE documentation, the AT or BEFORE clause is used for Snowflake Time Travel, which allows you to query historical data from a table based on a specific point in the past. The clause can use one of the following parameters to pinpoint the exact historical data you wish to access:
*TIMESTAMP: Specifies an exact date and time to use for Time Travel.
*OFFSET: Specifies the difference in seconds from the current time to use for Time Travel.
*STATEMENT: Specifies the query ID of a statement to use as the reference point for Time Travel.
Therefore, the queries that will allow the user to view the historical data that was in the table before the query was executed are:
*B. SELECT * FROM my_table AT (TIMESTAMP => '2021-01-01 07:00:00' :: timestamp); This query uses the TIMESTAMP parameter to specify a point in time that is before the query execution time of 07:01.
*D. SELECT * FROM my table PRIOR TO STATEMENT '8e5d0ca9-005e-44e6-b858-a8f5b37c5726'; This query uses the PRIOR TO STATEMENT keyword and the STATEMENT parameter to specify a point in time that is immediately preceding the query execution time of 07:01.
*F. SELECT * FROM my_table BEFORE (STATEMENT => '8e5d0ca9-005e-44e6-b858-a8f5b37c5726'); This query uses the BEFORE keyword and the STATEMENT parameter to specify a point in time that is immediately preceding the query execution time of 07:01.
The other queries are incorrect because:
*A. SELECT * FROM my table WITH TIME_TRAVEL (OFFSET => -60*30); This query uses the OFFSET parameter to specify a point in time that is 30 minutes before the current time, which is 07:30. This is after the query execution time of 07:01, so it will not show the historical data before the query was executed.
*C. SELECT * FROM TIME_TRAVEL ('MY_TABLE', 2021-01-01 07:00:00); This query is not valid syntax for Time Travel. The TIME_TRAVEL function does not exist in Snowflake. The correct syntax is to use the AT or BEFORE clause after the table name in the FROM clause.
*E. SELECT * FROM my_table AT (OFFSET => -60*30); This query uses the AT keyword and the OFFSET parameter to specify a point in time that is 30 minutes before the current time, which is 07:30. This is equal to the query execution time of 07:01, so it will not show the historical data before the query was executed. The AT keyword specifies that the request is inclusive of any changes made by a statement or transaction with timestamp equal to the specified parameter. To exclude the changes made by the query, the BEFORE keyword should be used instead.
質問 # 50
MY_TABLE is a table that has not been updated or modified for several days. On 01 January 2021 at 07:01, a user executed a query to update this table. The query ID is
'8e5d0ca9-005e-44e6-b858-a8f5b37c5726'. It is now 07:30 on the same day.
Which queries will allow the user to view the historical data that was in the table before this query was executed? (Select THREE).
- A. SELECT * FROM my table PRIOR TO STATEMENT '8e5d0ca9-005e-44e6-b858-a8f5b37c5726';
- B. SELECT * FROM my_table AT (TIMESTAMP => '2021-01-01 07:00:00' :: timestamp);
- C. SELECT * FROM TIME_TRAVEL ('MY_TABLE', 2021-01-01 07:00:00);
- D. SELECT * FROM my_table BEFORE (STATEMENT => '8e5d0ca9-005e-44e6-b858-a8f5b37c5726');
- E. SELECT * FROM my table WITH TIME_TRAVEL (OFFSET => -60*30);
- F. SELECT * FROM my_table AT (OFFSET => -60*30);
正解：A、B、D
解説：
According to the AT | BEFORE documentation, the AT or BEFORE clause is used for Snowflake Time Travel, which allows you to query historical data from a table based on a specific point in the past. The clause can use one of the following parameters to pinpoint the exact historical data you wish to access:
* TIMESTAMP: Specifies an exact date and time to use for Time Travel.
* OFFSET: Specifies the difference in seconds from the current time to use for Time Travel.
* STATEMENT: Specifies the query ID of a statement to use as the reference point for Time Travel.
Therefore, the queries that will allow the user to view the historical data that was in the table before the query was executed are:
* B. SELECT * FROM my_table AT (TIMESTAMP => '2021-01-01 07:00:00' :: timestamp); This query uses the TIMESTAMP parameter to specify a point in time that is before the query execution time of 07:01.
* D. SELECT * FROM my table PRIOR TO STATEMENT '8e5d0ca9-005e-44e6-b858-a8f5b37c5726'; This query uses the PRIOR TO STATEMENT keyword and the STATEMENT parameter to specify a point in time that is immediately preceding the query execution time of 07:01.
* F. SELECT * FROM my_table BEFORE (STATEMENT => '8e5d0ca9-005e-44e6-b858-a8f5b37c5726'); This query uses the BEFORE keyword and the STATEMENT parameter to specify a point in time that is immediately preceding the query execution time of 07:01.
The other queries are incorrect because:
* A. SELECT * FROM my table WITH TIME_TRAVEL (OFFSET => -60*30); This query uses the OFFSET parameter to specify a point in time that is 30 minutes before the current time, which is 07:30. This is after the query execution time of 07:01, so it will not show the historical data before the query was executed.
* C. SELECT * FROM TIME_TRAVEL ('MY_TABLE', 2021-01-01 07:00:00); This query is not valid syntax for Time Travel. The TIME_TRAVEL function does not exist in Snowflake. The correct syntax is to use the AT or BEFORE clause after the table name in the FROM clause.
* E. SELECT * FROM my_table AT (OFFSET => -60*30); This query uses the AT keyword and the OFFSET parameter to specify a point in time that is 30 minutes before the current time, which is 07:30. This is equal to the query execution time of 07:01, so it will not show the historical data before the query was executed. The AT keyword specifies that the request is inclusive of any changes made by a statement or transaction with timestamp equal to the specified parameter. To exclude the changes made by the query, the BEFORE keyword should be used instead.
質問 # 51
A Snowflake Administrator has a multi-cluster virtual warehouse and is using the Snowflake Business Critical edition. The minimum number of clusters is set to 2 and the maximum number of clusters is set to 10. This configuration works well for the standard workload, rarely exceeding 5 running clusters. However, once a month the Administrator notes that there are a few complex long-running queries that are causing increased queue time and the warehouse reaches its maximum limit at 10 clusters.
Which solutions will address the issues happening once a month? (Select TWO).
- A. Examine the complex queries and determine if they can be made more efficient usingclustering keys or materialized views.
- B. Increase the multi-cluster maximum to 20 or more clusters.
- C. Increase the minimum number of clusters started in the multi-cluster configuration to 5.
- D. Have the group running the complex monthly queries use a separate appropriately-sized warehouse to support their workload.
- E. Use a task to increase the cluster size for the time period that the more complex queries are running and another task to reduce the size of the cluster once the complex queries complete.
正解：D、E
解説：
Explanation
According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option A is a possible solution to address the issues happening once a month, as it allows the administrator to use a task to increase the cluster size for the time period that the more complex queries are running and another task to reduce the size of the cluster once the complex queries complete. This way, the warehouse can have more resources available to handle the complex queries without reaching the maximum limit of 10 clusters, and then return to the normal cluster size to save costs. Option B is another possible solution to address the issues happening once a month, as it allows the administrator to have the group running the complex monthly queries use a separate appropriately-sized warehouse to support their workload. This way, the warehouse can isolate the complex queries from the standard workload and avoid queue time and resource contention. Option C is not a recommended solution to address the issues happening once a month, as it would increase the costs and complexity of managing the multi-cluster warehouse, and may not solve the underlying problem of inefficient queries. Option D is a good practice to improve the performance of the queries, but it is not a direct solution to address the issues happening once a month, as it requires analyzing and optimizing the complex queries using clustering keys or materialized views, which may not be feasible or effective in all cases. Option E is not a recommended solution to address the issues happening once a month, as it would increase the costs and waste resources by starting more clusters than needed for the standard workload.
質問 # 52
An Administrator is evaluating a complex query using the EXPLAIN command. The Globalstats operation indicates 500 partitionsAssigned.
The Administrator then runs the query to completion and opens the Query Profile. They notice that the partitions scanned value is 429.
Why might the actual partitions scanned be lower than the estimate from the EXPLAIN output?
- A. In-flight data compression will result in fewer micro-partitions being scanned at the virtual warehouse layer than were identified at the storage layer.
- B. The GlobalStats partition assignment includes the micro-partitions that will be assigned for preservation of the query results.
- C. Runtime optimizations such as join pruning can reduce the number of partitions and bytes scanned during query execution.
- D. The EXPLAIN results always include a 10-15% safety factor in order to provide conservative estimates.
正解：C
解説：
Explanation
The EXPLAIN command returns the logical execution plan for a query, which shows the upper bound estimates for the number of partitions and bytes that might be scanned by the query1. However, these estimates do not account for the runtime optimizations that Snowflake performs to improve the query performance and reduce the resource consumption2. One of these optimizations is join pruning, which eliminates unnecessary partitions from the join inputs based on the join predicates2. This can result in fewer partitions and bytes scanned than the estimates from the EXPLAIN output3. Therefore, the actual partitions scanned value in the Query Profile can be lower than the partitionsAssigned value in the EXPLAIN output4.
質問 # 53
A user has enrolled in Multi-factor Authentication (MFA) for connecting to Snowflake. The user informs the Snowflake Administrator that they lost their mobile phone the previous evening.
Which step should the Administrator take to allow the user to log in to the system, without revoking their MFA enrollment?
- A. Instruct the user to connect to Snowflake using SnowSQL, which does not support MFA authentication.
- B. Instruct the user to append the normal URL with /?mode=mfa_bypass&code= to log on.
- C. Alter the user and set DISABLE_MFA to true, which will suspend the MFA requirement for 24 hours.
- D. Alter the user and set MINS TO BYPASS MFA to a value that will disable MFA long enough for the user to log in.
正解：D
解説：
The MINS_TO_BYPASS_MFA property allows the account administrator to temporarily disable MFA for a user who has lost their phone or changed their phone number1. The user can log in without MFA for the specified number of minutes, and then re-enroll in MFA using their new phone1. This does not revoke their MFA enrollment, unlike the DISABLE_MFA property, which cancels their enrollment and requires them to re-enroll from scratch1. The other options are not valid ways to bypass MFA, as SnowSQL does support MFA authentication2, and there is no such URL parameter as /?mode=mfa_bypass&code= for Snowflake3
質問 # 54
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私たちは常に、ADA-C01 SnowPro Advanced Administrator受験ガイドで望ましい結果を得るのを助けるために努力します、最後になりましたが、ADA-C01試験問題の無料試用サービスを提供できます、GoShikenはSnowflakeのADA-C01認定試験にたいして短期で有効なウェブサイトでADA-C01認定試験に合格するのを保証したり、Snowflake認証試験に合格しなければ全額で返金いたします、GoShiken ADA-C01受験資料更新版を利用したら、試験に合格しないことは絶対ないです、ADA-C01調査の質問からのレポートによる、Snowflake ADA-C01資格取得講座 私たちは会社の原則に沿い、顧客のプライバシーを尊重し守り、あなたのメッセージを公開したり、違法に編集することはありません、あなたに安心にネットでSnowflakeのADA-C01試験の資料を購入させるために、我々GoShikenは国際の最大の安全的な支払システムPaypalと協力してあなたの支払の安全性を保障します。
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Snowflake ADA-C01 試験は簡単に信頼できるADA-C01資格取得講座: 有効的なSnowPro Advanced Administrator
最後になりましたが、ADA-C01試験問題の無料試用サービスを提供できます、GoShikenはSnowflakeのADA-C01認定試験にたいして短期で有効なウェブサイトでADA-C01認定試験に合格するのを保証したり、Snowflake認証試験に合格しなければ全額で返金いたします。
GoShikenを利用したら、試験に合格しないことは絶対ないです、ADA-C01調査の質問からのレポートによる。