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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Storage and Retrieval | 15-20% | - Performance Optimization
|
| Topic 2: Performance Engineering | 10-15% | - Query Optimization
|
| Topic 3: Data Governance and Security | 15-20% | - Compliance and Monitoring
|
| Topic 4: Data Ingestion | 15-20% | - Bulk Data Loading
|
| Topic 5: Data Transformation | 20-25% | - Data Processing Patterns
|
| Topic 6: Advanced Snowflake Features | 10-15% | - Data Sharing
|
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NEW QUESTION # 107
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?
Answer: A,C
Explanation:
Explanation
Automating Warehouse Suspension
Data Engineer might want to consider disabling auto-suspend for a warehouse if:
He/She have a heavy, steady workload for the warehouse.
He/She require the warehouse to be available with no delay or lag time. Warehouse provisioning is generally very fast (e.g. 1 or 2 seconds); however, depending on the size of the warehouse and the availability of compute resources to provision, it can take longer.
If he/she chose to disable auto-suspend, He/she must carefully consider the costs associated with running a warehouse continually, even when the warehouse is not processing queries. The costs can be significant, especially for larger warehouses (X-Large, 2X-Large, etc.).
To disable auto-suspend, Engineer must explicitly select Never in the web interface, or specify 0 or NULL in SQL.
NEW QUESTION # 108
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?
Answer: A
Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.
NEW QUESTION # 109
Snowflake computes and adds partitions based on the defined partition column expressions when an external table metadata is refreshed.
What are the Correct Statements to configure Partition metadata refresh in case of External Tables?
Answer: C,D
Explanation:
Explanation
Snowflake strongly recommend partitioning your external tables, which requires that your underly-ing data is organized using logical paths that include date, time, country, or similar dimensions in the path.
Partitioning divides your external table data into multiple parts using partition columns.
An external table definition can include multiple partition columns, which impose a multi-dimensional structure on the external data.
Partitions are stored in the external table metadata.
Benefits of partitioning include improved query performance.
Because the external data is partitioned into separate slices/parts, query response time is faster when processing a small part of the data instead of scanning the entire data set.
Based on your individual use cases, you can either:
Add new partitions automatically by refreshing an external table that defines an expression for each partition column.
Add new partitions manually.
Partition columns are defined when an external table is created, using the CREATE EXTERNAL TABLE ...
PARTITION BY syntax.
After an external table is created, the method by which partitions are added cannot be changed.
Partitions Added Automatically
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn.
A partition consists of all data files that match the path and/or filename in the expression for the partition column.
The CREATE EXTERNAL TABLE syntax for adding partitions automatically based on expres-sions is as follows:
CREATE EXTERNAL TABLE
<table_name>
( <part_col_name> <col_type> AS <part_expr> )
[ , ... ]
[ PARTITION BY ( <part_col_name> [, <part_col_name> ... ] ) ]
Snowflake computes and adds partitions based on the defined partition column expressions when an external table metadata is refreshed.
By default, the metadata is refreshed automatically when the object is created.
In addition, the object owner can configure the metadata to refresh automatically when new or up-dated data files are available in the external stage.
The owner can alternatively refresh the metadata manually by executing the ALTER EXTERNAL TABLE ...
REFRESH command.
The metadata for an external table can be refreshed automatically using the event notification ser-vice for your cloud storage service.
NEW QUESTION # 110
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?
Answer: A
Explanation:
IAM roles for service accounts (IRSA) allow assigning fine-grained permissions to EKS pods based on Kubernetes service accounts. This approach ensures that AWS credentials are not hardcoded or stored in environment variables, which enhances security. Instead, the pods running in the EKS cluster assume the IAM role with permissions to access Amazon DynamoDB securely.
This solution meets the requirement of providing secure access without embedding AWS credentials in the containers, while also allowing for more granular access control at the pod level rather than relying on the node-level IAM role.
Storing AWS credentials in S3 is insecure and not a recommended practice. This approach does not meet the security requirement of avoiding embedded AWS credentials.
Storing AWS credentials in environment variables is insecure and not recommended. This exposes credentials within the container, increasing the risk of credential leakage.
Although Kubernetes secrets can provide some protection, storing long-term AWS credentials (access key and secret key) in secrets still presents a security risk. The preferred method is to use temporary credentials via IAM roles (IRSA), which rotate automatically.
NEW QUESTION # 111
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 # 112
......
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