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最新的 SnowPro Advanced DEA-C01 免費考試真題 (Q56-Q61):
問題 #56
A data engineer created a table named cloudtrail_logs in Amazon Athena to query AWS CloudTrail logs and prepare data for audits. The data engineer needs to write a query to display errors with error codes that have occurred since the beginning of 2024. The query must return the
10 most recent errors.
Which query will meet these requirements?
- A. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessageorder by eventname asc limit 10;
- B. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessage order by TotalEvents desc limit 10;
- C. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where errorcode is not nulland eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessagelimit 10;
- D. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere errorcode is not nulland eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessageorder by TotalEvents desclimit 10;
答案:D
解題說明:
This query meets the requirements by:
1. Filtering results where errorcode is not null, so only error events are included.
2. Filtering by eventtime to include events occurring since the beginning of 2024.
3. Grouping by eventname, errorcode, and errormessage to summarize the error events.
4. Sorting by TotalEvents in descending order to show the most recent errors.
5. Limiting the results to the 10 most recent errors.
問題 #57
Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)
- A. DataFrame.show ()
- B. DataFrame.col ( )
- C. DataFrame.random_split ( )
- D. DataFrame.collect ()
- E. DateFrame.select ()
答案:A,D
解題說明:
Explanation
The methods that will trigger an action that will evaluate a DataFrame are DataFrame.collect() and DataFrame.show(). These methods will force the execution of any pending transformations on the DataFrame and return or display the results. The other options are not methods that will evaluate a DataFrame. Option A, DataFrame.random_split(), is a method that will split a DataFrame into two or more DataFrames based on random weights. Option C, DataFrame.select(), is a method that will project a set of expressions on a DataFrame and return a new DataFrame. Option D, DataFrame.col(), is a method that will return a Column object based on a column name in a DataFrame.
問題 #58
A data engineer is building a new data pipeline that stores metadata in an Amazon DynamoDB table. The data engineer must ensure that all items that are older than a specified age are removed from the DynamoDB table daily.
Which solution will meet this requirement with the LEAST configuration effort?
- A. Create a DynamoDB stream that has an AWS Lambda function that reacts to data modifications.Configure the Lambda function to delete items that are older than the specified age.
- B. Create an Amazon EventBridge rule that uses a daily cron expression to trigger an AWS Lambda function to delete items that are older than the specified age.
- C. Enable DynamoDB TTL on the DynamoDB table. Adjust the application source code to set the TTL attribute appropriately.
- D. Add a lifecycle configuration to the DynamoDB table that deletes items that are older than the specified age.
答案:C
解題說明:
DynamoDB Time to Live (TTL) is a fully managed feature that automatically expires and deletes items based on a timestamp attribute you define. By enabling TTL and setting the TTL attribute in the application code, items older than the specified age are removed without the need for custom scheduling or Lambda functions, offering the least configuration effort.
問題 #59
A company needs to optimize storage costs for an Amazon S3 bucket. The S3 bucket receives
10 million objects every day. The objects range in size from 2 KB to 5 MB. The objects need to be immediately accessible for the first 60 days. Users access objects infrequently from 61 to 180 days. The objects must be accessible within an hour from 181 to 365 days. The company can delete the objects after 365 days. Which solution will meet these requirements?
- A. Use S3 Intelligent-Tiering to automatically transition objects. Select the Archive Access tier for Intelligent-Tiering. Configure an S3 bucket policy to expire objects that are older than 365 days.
- B. Create an S3 Lifecycle policy to move objects. Configure the policy to move objects from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after 60 days. Move the objects to S3 Glacier Flexible Retrieval after 180 days. Expire objects after 365 days.
- C. Enable S3 Inventory. Run an AWS Lambda function each day to fetch an inventory report and move objects from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after 60 days. Move objects to S3 Glacier Flexible Retrieval after 180 days. Expire objects after 365 days.
- D. Enable S3 Inventory. Use a daily inventory report to configure an S3 Batch Operations job that moves objects from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after 60 days. Move objects to S3 Glacier Flexible Retrieval after 180 days. Expire objects after 365 days.
答案:B
解題說明:
An S3 Lifecycle policy provides native, automatic tiering by object age without operational overhead, which is critical at 10 million new objects per day. Keeping objects in S3 Standard for the first 60 days satisfies immediate access. Transitioning to S3 Standard-IA from days 61-180 matches infrequent access with millisecond retrieval. Transitioning to S3 Glacier Flexible Retrieval after 180 days meets the "accessible within an hour" requirement. Expiring objects after
365 days satisfies the deletion requirement.
問題 #60
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning.
The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?
- A. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.
- B. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times.
Schedule lower capacity during off-peak times. - C. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.
- D. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
答案:B
問題 #61
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