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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Advanced Snowflake Features | 10-15% | - Data Sharing
|
| Topic 2: Data Storage and Retrieval | 15-20% | - Snowflake Architecture
|
| Topic 3: Data Ingestion | 15-20% | - Bulk Data Loading
|
| Topic 4: Performance Engineering | 10-15% | - Warehouse Management
|
| Topic 5: Data Transformation | 20-25% | - SQL-Based Transformations
|
| Topic 6: Data Governance and Security | 15-20% | - Compliance and Monitoring
|
The second format is web-based DEA-C01 practice exam and can be accessed through browsers and candidates can take it online. The students don’t need to install or use any plugins or software to attempt the web-based practice exam. The third and last form is the Snowflake DEA-C01 desktop practice test software that can be used from Windows computers. Candidates that have Windows laptops or computers can take the DEA-C01 practice exam efficiently.
NEW QUESTION # 21
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?
Answer: A
Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/register-data-lake.html
https://docs.aws.amazon.com/lake-formation/latest/dg/registration-role.html
NEW QUESTION # 22
A hotel management company receives daily data files from each of its hotels. The company wants to upload its data to AWS. The company plans to use Amazon Athena to access the files.
The company needs to protect the files from accidental deletion. The company will develop an application on its on-premises servers to automatically forward the files to a fully managed AWS ingestion service.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Kinesis Agent on-prem sends files to fully managed Kinesis Data Firehose, which delivers them to Amazon S3. Enabling S3 versioning protects against accidental deletions, and Athena can query directly from S3 - meeting requirements with minimal operational overhead.
NEW QUESTION # 23
What kind of Snowflake integration is required when defining an external function in Snowflake?
Answer: D
Explanation:
Explanation
An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.
NEW QUESTION # 24
Mark the Correct Statements:
Statement 1. Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database.
Statement 2. Data Engineer can use zero-copy cloning feature for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).
Answer: A
Explanation:
Explanation
Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database and create a derived copy of that object which initially shares the underlying storage. This can be extremely useful for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).
For example, when a clone is created of a table, the clone utilizes no data storage because it shares all the existing micro-partitions of the original table at the time it was cloned; however, rows can then be added, deleted, or updated in the clone independently from the original table. Each change to the clone results in new micro-partitions that are owned exclusively by the clone and are protect-ed through CDP.
NEW QUESTION # 25
Mark a Data Engineer, looking to implement streams on local views & want to use change tracking metadata for one of its Data Loading use case. Please select the incorrect understanding points of Mark with respect to usage of Streams on Views?
Answer: A
Explanation:
Explanation
A stream object records data manipulation language (DML) changes made to tables, including in-serts, updates, and deletes, as well as metadata about each change, so that actions can be taken us-ing the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at the row level, between two transac-tional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
Standard tables, including shared tables.
Views, including secure views
Directory tables
External tables
When created, a stream logically takes an initial snapshot of every row in the source object (e.g. ta-ble, external table, or the underlying tables for a view) by initializing a point in time (called an off-set) as the current transactional version of the object. The change tracking system utilized by the stream then records information about the DML changes after this snapshot was taken. Change rec-ords provide the state of a row before and after the change. Change information mirrors the column structure of the tracked source object and includes additional metadata columns that describe each change event.
Note that a stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, a pair of hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables.
Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views.
Views with the following operations are not yet supported:
GROUP BY clauses
QUALIFY clauses
Subqueries not in the FROM clause
Correlated subqueries
LIMIT clauses
Change Tracking:
Change tracking must be enabled in the underlying tables.
Prior to creating a stream on a view, you must enable change tracking on the underlying tables for the view.
Set the CHANGE_TRACKING parameter when creating a view (using CREATE VIEW) or later (using ALTER VIEW).
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.
NEW QUESTION # 26
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