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| Section | Objectives |
|---|---|
| Topic 1: Security, Governance, and Data Sharing | - Data governance
|
| Topic 2: Data Engineering Fundamentals on Snowflake | - Snowflake architecture for data engineering
|
| Topic 3: Data Pipelines and Transformation | - Streams and Tasks
|
| Topic 4: Data Loading and Integration | - Bulk data loading
|
| Topic 5: Performance Optimization | - Query performance tuning
|
| Topic 6: Semi-Structured and Advanced Data Handling | - Semi-structured data processing
|
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NEW QUESTION # 368
A Data Engineer needs to know the details regarding the micro-partition layout for a table named invoice using a built-in function.
Which query will provide this information?
Answer: C
Explanation:
Explanation
The query that will provide information about the micro-partition layout for a table named invoice using a built-in function is SELECT SYSTEM$CLUSTERING_INFORMATION('Invoice');. The SYSTEM$CLUSTERING_INFORMATION function returns information about the clustering status of a table, such as the clustering key, the clustering depth, the clustering ratio, the partition count, etc. The function takes one argument: the table name in a qualified or unqualified form. In this case, the table name is Invoice and it is unqualified, which means that it will use the current database and schema as the context. The other options are incorrect because they do not use a valid built-in function for providing information about the micro-partition layout for a table. Option B is incorrect because it uses $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which is not a valid function name. Option C is incorrect because it uses CALL instead of SELECT, which is not a valid way to invoke a table function.
Option D is incorrect because it uses CALL instead of SELECT and $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which are both invalid.
NEW QUESTION # 369
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?
Answer: D
Explanation:
The RootDiskUsed metric in Amazon MSK indicates that the storage space on the broker's disk is filling up. To resolve this, the company can expand the storage for the MSK brokers to prevent them from running out of disk space.
Amazon MSK supports automatic storage scaling, which can be configured to avoid similar issues in the future. By enabling auto-scaling, MSK will automatically expand the storage when needed, ensuring that the brokers have enough space to handle new topics or additional data.
ZooKeeper is used for managing the state of Kafka brokers, but the RootDiskUsed metric pertains to the Kafka brokers, not the ZooKeeper nodes.
Expanding ZooKeeper storage will not resolve the issue.
While increasing the instance size may improve performance, it does not address the issue of running out of disk space on the broker's root volume, which is causing the CloudWatch alarm.
Kafka topics do not have a direct volume specification. Storage for Kafka is managed at the broker level, not at the individual topic level. Therefore, this option does not apply to the issue of disk usage.
NEW QUESTION # 370
A data engineer is processing and analyzing multiple terabytes of raw data that is in Amazon S3.
The data engineer needs to clean and prepare the data. Then the data engineer needs to load the data into Amazon Redshift for analytics.
The data engineer needs a solution that will give data analysts the ability to perform complex queries. The solution must eliminate the need to perform complex extract, transform, and load (ETL) processes or to manage infrastructure.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
NEW QUESTION # 371
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: C
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 # 372
A stream called TRANSACTIONS_STM is created on top of a transactions table in a continuous pipeline running in Snowflake. After a couple of months, the TRANSACTIONS table is renamed transactiok3_raw to comply with new naming standards What will happen to the TRANSACTIONS _STM object?
Answer: C
Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream is associated with a specific table at the time of creation, and it cannot be altered to point to a different table later. Therefore, if the source table is renamed, the stream will become stale and will need to be re-created with the new table name. The other options are not correct because:
TRANSACTIONS _STM will not keep working as expected, as it will lose track of the changes made to the renamed table.
TRANSACTIONS _STM will not be automatically renamed TRANSACTIONS _RAW_STM, as streams do not inherit the name changes of their source tables.
Reading from the transactions_stm stream will not succeed for some time after the expected STALE_TIME, as streams do not have a STALE_TIME property.
NEW QUESTION # 373
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