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NEW QUESTION # 145
The Snowpipe API provides REST endpoints for fetching load reports. One of the Endpoint named insertReport helps to retrieves a report of files submitted via insertFiles end point whose contents were recently ingested into a table. A success response (200) contains information about files that have recently been added to the table. Response Looks like below:
1.{
2."pipe": "SNOWTESTDB.SFTESTSCHEMA.SFpipe",
3."completeResult": true,
4."nextBeginMark": "1_16",
5."files": [
6.{
7."path": "data4859992083898.csv",
8."stageLocation": "s3://mybucket/",
9."fileSize": 89,
10."timeReceived": "2022-01-31T04:47:41.453Z",
11."lastInsertTime": "2022-01-31T04:48:28.575Z",
12."rowsInserted": 1,
13."rowsParsed": 1,
14."errorsSeen": 0,
15."errorLimit": 1,
16."complete": true,
17."status": "????"
18.}
19.]
20.}
Which one is the correct value of status string data in the Response Body?
Answer: A
Explanation:
Explanation
Permissible Load status for the file:
LOAD_IN_PROGRESS: Part of the file has been loaded into the table, but the load process has not completed yet.
LOADED: The entire file has been loaded successfully into the table.
LOAD_FAILED: The file load failed.
PARTIALLY_LOADED: Some rows from this file were loaded successfully, but others were not loaded due to errors. Processing of this file is completed.
Please not the different Response Codes available with their meaning.
200 - Success. Report returned.
400 - Failure. Invalid request due to an invalid format, or limit exceeded.
404 - Failure. pipeName not recognized.
This error code can also be returned if the role used when calling the endpoint does not have suffi-cient privileges. For more information, see Granting Access Privileges.
429 - Failure. Request rate limit exceeded.
500 - Failure. Internal error occurred.
As you could understand from the questions, there is 200 Success response returned, Status in the response body would be LOADED.
NEW QUESTION # 146
A company plans to provision a log delivery stream within a VPC. The company configured the VPC flow logs to publish to Amazon CloudWatch Logs. The company needs to send the flow logs to Splunk in near real time for further analysis.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Kinesis Data Firehose has built-in support for Splunk as a destination, making the integration straightforward. Using a CloudWatch Logs subscription filter directly to Firehose simplifies the data flow, eliminating the need for additional Lambda functions or custom integrations.
NEW QUESTION # 147
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework.
What combination of steps will meet these requirements? (Choose two.)
Answer: A,E
Explanation:
To run AWS Glue Apache Spark jobs with the Apache Iceberg framework, you must:
Set --datalake-formats to iceberg so Glue knows to use the Iceberg format.
Specify the Iceberg library by using the --extra-jars parameter to include the appropriate Iceberg JAR files for Glue to access the Iceberg table format.
NEW QUESTION # 148
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?
Answer: D
Explanation:
DynamoDB Streams can capture changes to items in DynamoDB tables (such as inserts, updates, and deletes) in near real-time. An AWS Lambda function can be triggered by these streams, and the function can process the changes and update the data in Amazon OpenSearch Service. This solution meets the requirement for near real-time updates with minimal latency.
The "Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service." solution involves exporting data periodically, which does not meet the requirement for near real- time updates. Periodic exports could introduce delays.
AWS Glue is generally used for batch processing, and it is not designed for near real-time data ingestion. It would not be an ideal solution for real-time or low-latency requirements.
The "Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables." option suggests building a custom solution, which would involve significant development effort.
Moreover, OpenSearch does not natively support such plugins, making this solution less practical compared to using DynamoDB Streams and Lambda, which are designed to work together.
NEW QUESTION # 149
In one of your created Schema, you have been required to create Internal Stages, what are the In-correct considerations you can noticed from the below options? [Select All that Apply]
Answer: B
Explanation:
Explanation
A stage specifies where data files are stored (i.e. "staged") so that the data in the files can be loaded into a table.
Types of Internal Stages
User Stages
Table Stages
Named Stages
By default, each user and table in Snowflake is automatically allocated an internal stage for staging data files to be loaded. In addition, you can create named internal stages.
File staging information is required during both steps in the data loading process:
You must specify an internal stage in the PUT command when uploading files to Snowflake.
You must specify the same stage in the COPY INTO <table> command when loading data into a table from the staged files.
Consider the best type of stage for specific data files. Each option provides benefits and potential drawbacks.
User Stages
Each user has a Snowflake stage allocated to them by default for storing files. This stage is a con-venient option if your files will only be accessed by a single user, but need to be copied into multi-ple tables.
User stages have the following characteristics and limitations:
User stages are referenced using @~; e.g. use LIST @~ to list the files in a user stage.
Unlike named stages, user stages cannot be altered or dropped.
User stages do not support setting file format options. Instead, you must specify file format and copy options as part of the COPY INTO <table> command.
This option is not appropriate if:
Multiple users require access to the files.
The current user does not have INSERT privileges on the tables the data will be loaded into.
Table Stages
Each table has a Snowflake stage allocated to it by default for storing files. This stage is a conven-ient option if your files need to be accessible to multiple users and only need to be copied into a sin-gle table.
Table stages have the following characteristics and limitations:
Table stages have the same name as the table; e.g. a table named mytable has a stage referenced as
@%mytable.
Unlike named stages, table stages cannot be altered or dropped.
Table stages do not support transforming data while loading it (i.e. using a query as the source for the COPY command).
Note that a table stage is not a separate database object; rather, it is an implicit stage tied to the table itself. A table stage has no grantable privileges of its own. To stage files to a table stage, list the files, query them on the stage, or drop them, you must be the table owner (have the role with the OWNERSHIP privilege on the table).
This option is not appropriate if you need to copy the data in the files into multiple tables.
Named Stages
Named stages are database objects that provide the greatest degree of flexibility for data loading:
Users with the appropriate privileges on the stage can load data into any table.
Because the stage is a database object, the security/access rules that apply to all objects apply. The privileges to use a stage can be granted or revoked from roles. In addition, ownership of the stage can be transferred to another role.
If you plan to stage data files that will be loaded only by you, or will be loaded only into a single table, then you may prefer to simply use either your user stage or the stage for the table into which you will be loading data.
Named stages are optional but recommended when you plan regular data loads that could involve multiple users and/or tables.
NEW QUESTION # 150
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