2023 Latest DEA-C01 DUMPS Q&As with Explanations Verified & Correct Answers
DEA-C01 dumps Exam Material with 67 Questions
NEW QUESTION # 35
Jeff, a Data Engineer, accessing elements in JSON object in its 3 data loading scripts, he unknow-ingly use the upper case while accessing the elements. e.g.
Script 1 --> fruits:apple.sweet
Script 2 --> FRUITS:apple.sweet
Script 3 --> FRUITS:Apple.Sweet
Which are the correct statements?
- A. Script 1 & 3 traverse path will be treated in same way.
- B. Script 2&3 traverse path will be same.
- C. Script 1 & Script 2 traverse path twill be treated same, but Script 2 will not.
- D. Script 1,2,3 traverse path will treat in same way.
Answer: C
Explanation:
Explanation
There are two ways to access elements in a JSON object:
Dot Notation
Bracket Notation
Regardless of which notation you use, the column name is case-insensitive but element names are case-sensitive.
NEW QUESTION # 36
Which Snowflake feature facilitates access to external API services such as geocoders. data transformation, machine Learning models and other custom code?
- A. External tables
- B. Security integration
- C. External functions
- D. Java User-Defined Functions (UDFs)
Answer: C
Explanation:
Explanation
External functions are Snowflake functions that facilitate access to external API services such as geocoders, data transformation, machine learning models and other custom code. External functions allow users to invoke external services from within SQL queries and pass arguments and receive results as JSON values. External functions require creating an API integration object and an external function object in Snowflake, as well as deploying an external service endpoint that can communicate with Snowflake via HTTPS.
NEW QUESTION # 37
The following CREATE DATABASE command creates a clone of a database snowmy_db i.e.
Create database pods_db clone snowmy_db
before (statement => '7e5d0cb9-005e-94e6-b058-k8f5b37c5725');
What are possible reason of failing cloning operation for this database?
- A. CREATE DATABASE query fails due to compilation error as it do not support state-ment keyword.
- B. Time Travel Statement query time is beyond the retention time of few current child (e.g., a table) of the Database entity.
- C. Time Travel Statement query time is at or before the point in time when the object was created.
- D. SQL Compilation error: "Incorrect Syntax 'before' while creating database"
Answer: B,C
NEW QUESTION # 38
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?
- A. To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true.
- B. Set the FORCE option to load all files, ignoring load metadata if it exists.
- C. Since the initial set of data for the table (i.e. the first batch after the table was created) was loaded, we can simply use the COPY INTO command to load all the product files with the known load status irrespective of their column LAST_MODIFIED date values.
- D. The COPY command cannot definitively determine whether a file has been loaded al-ready if the LAST_MODIFIED date is older than 64 days and the initial set of data was loaded into the table more than 64 days earlier (and if the file was loaded into the table, that also occurred more than 64 days earlier). In this case, to prevent accidental reload, the command skips the product files by default.
Answer: A
Explanation:
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files
NEW QUESTION # 39
Alex, a Data Engineer with one of the Data analytics Organization, created the Materialized view over External tables to improve Data Reporting Experience.
Step 1: He created materialized view named DataReportMV
1.create or replace materialized view DataReportMV as
2.select Item_id, Item_price from Items;
Step 2: He joined a materialized view with a sales table as
1.create or replace view Revenue as
2.select m.item_id, sum(ifnull(s.quantity, 0)) as quantity,
3.sum(ifnull(quantity * (s.price - m.item_price), 0)) as profit
4.from DataReportMV as m left outer join sales as s on s.item_id = m.item_id
5.group by m.item_id;
Step 3: After 1 hour, he decided to temporarily suspend the use (and maintenance) of the DataRe-portMV materialized view for cost saving purpose.
alter materialized view DataReportMV suspend;
Please select what Alex is doing wrong here?
- A. Alex is doing everything correct.
- B. Materialized view on top of External tables is not supported feature.
- C. There is no command like suspend for temporarily suspension of Materialized views, Step 3 will give error like invalid Suspend command.
- D. A materialized view, DataReportMV does not support Join operations, so Step 2 would be failed & he cannot proceed further.
- E. Once DataReportMV got suspended , any query on the top of the view will generate er-ror like:
Failure during expansion of view 'DATAREPORTMV': SQL compilation error: Material-ized view DataReportMV is invalid.
Answer: A
Explanation:
Explanation
All Steps will be executed successfully by Alex without any error.
NEW QUESTION # 40
Data engineer designed the data pipelines using Snowpipe to load data files into Snowflake tables, what will happen in case few files with same name but modified data are queued for reloading?
- A. Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.
- B. Snowpipe uses file loading metadata associated with each pipe object to prevent reload-ing the same files (and duplicating data) in a table.
- C. eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.
- D. Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.
Answer: B
Explanation:
Explanation
Snowflake uses file loading metadata to prevent reloading the same files (and duplicating data) in a table.
Snowpipe prevents loading files with the same name even if they were later modified (i.e. have a different eTag).
The file loading metadata is associated with the pipe object rather than the table. As a result:
Staged files with the same name as files that were already loaded are ignored, even if they have been modified, e.g. if new rows were added or errors in the file were corrected.
Truncating the table using the TRUNCATE TABLE command does not delete the Snowpipe file loading metadata.
NEW QUESTION # 41
1.+--------------------------------------------------------------+
2.| SYSTEM$CLUSTERING_INFORMATION('SF_DATA', '(COL1, COL3)') |
3.|--------------------------------------------------------------|
4.| { |
5.| "cluster_by_keys" : "(COL1, COL3)", |
6.| "total_partition_count" : 1156, |
7.| "total_constant_partition_count" : 0, |
8.| "average_overlaps" : 117.5484, |
9.| "average_depth" : 64.0701, |
10.| "partition_depth_histogram" : { |
11.| "00000" : 0, |
12.| "00001" : 0, |
13.| "00002" : 3, |
14.| "00003" : 3, |
15.| "00004" : 4, |
16.| "00005" : 6, |
17.| "00006" : 3, |
18.| "00007" : 5, |
19.| "00008" : 10, |
20.| "00009" : 5, |
21.| "00010" : 7, |
22.| "00011" : 6, |
23.| "00012" : 8, |
24.| "00013" : 8, |
25.| "00014" : 9, |
26.| "00015" : 8, |
27.| "00016" : 6, |
28.| "00032" : 98, |
29.| "00064" : 269, |
30.| "00128" : 698 |
31.| } |
32.| } |
33.+--------------------------------------------------------------+
The Above example indicates that the SF_DATA table is not well-clustered for which of following valid reasons?
- A. Zero (0) constant micro-partitions out of 1156 total micro-partitions.
- B. High average of overlap depth across micro-partitions.
- C. ALL of the above
- D. Most of the micro-partitions are grouped at the lower-end of the histogram, with the majority of micro-partitions having an overlap depth between 64 and 128.
- E. High average of overlapping micro-partitions.
Answer: C
NEW QUESTION # 42
Which is the non-supportable JavaScript UDF data types?
- A. Double
- B. String
- C. Integers
- D. Binary
Answer: C
NEW QUESTION # 43
Mark the correct Statements with respect to Secure views & its creation in the SnowFlake Account?
- A. For non-materialized views, the IS_SECURE column in the Information Schema and Account Usage views identifies whether a view is secure.
- B. For a secure view, internal optimizations can indirectly expose data & the view defini-tion is visible to other users.
- C. Secure views should not be used for views that are defined solely for query conven-ience, such as views created to simplify queries for which users do not need to under-stand the underlying data representation.
- D. The internals of a secure view are not exposed in Query Profile (in the web interface). This is the case even for the owner of the secure view, because non-owners might have access to an owner's Query Profile.
- E. To convert an existing view to a secure view and back to a regular view, set/unset the SECURE keyword in the ALTER VIEW or ALTER MATERIALIZED VIEW com-mand.
Answer: A,C,D,E
Explanation:
Explanation
Why Should I Use Secure Views?
For a non-secure view, internal optimizations can indirectly expose data.
Some of the internal optimizations for views require access to the underlying data in the base tables for the view. This access might allow data that is hidden from users of the view to be exposed through user code, such as user-defined functions, or other programmatic methods. Secure views do not utilize these optimizations, ensuring that users have no access to the underlying data.
For a non-secure view, the view definition is visible to other users.
By default, the query expression used to create a standard view, also known as the view definition or text, is visible to users in various commands and interfaces.
For security or privacy reasons, you might not wish to expose the underlying tables or internal struc-tural details for a view. With secure views, the view definition and details are visible only to author-ized users (i.e.
users who are granted the role that owns the view).
When Should I Use a Secure View?
Views should be defined as secure when they are specifically designated for data privacy (i.e. to limit access to sensitive data that should not be exposed to all users of the underlying table(s)).
Secure views should not be used for views that are defined solely for query convenience, such as views created to simplify queries for which users do not need to understand the underlying data representation. Secure views can execute more slowly than non-secure views.
Secure views are defined using the SECURE keyword with the standard DDL for views:
To create a secure view, specify the SECURE keyword in the CREATE VIEW or CREATE MA-TERIALIZED VIEW command.
To convert an existing view to a secure view and back to a regular view, set/unset the SECURE keyword in the ALTER VIEW or ALTER MATERIALIZED VIEW command.
The definition of a secure view is only exposed to authorized users (i.e. users who have been grant-ed the role that owns the view). If an unauthorized user uses any of the following commands or in-terfaces, the view definition is not displayed:
SHOW VIEWS and SHOW MATERIALIZED VIEWS commands.
GET_DDL utility function.
VIEWS Information Schema view.
VIEWS Account Usage view.
For non-materialized views, the IS_SECURE column in the Information Schema and Account Us-age views identifies whether a view is secure.
The internals of a secure view are not exposed in Query Profile (in the web interface). This is the case even for the owner of the secure view, because non-owners might have access to an owner's Query Profile.
NEW QUESTION # 44
Which are the Cloud Platforms that Support Calling an External Function?
- A. GCP
- B. AWS & AZURE
- C. AWS only
- D. AWS,GCP,AZURE
Answer: D
NEW QUESTION # 45
Snowflake web interface can be used to create users with no passwords or remove passwords from existing users?
- A. FALSE
- B. TRUE
Answer: A
Explanation:
Explanation
You cannot use the Snowflake web interface to create users with no passwords or remove pass-words from existing users. You must use CREATE USER or ALTER USER command instead.
Also If you create a user with no password (or alter an existing user and remove their password), this effectively disables Snowflake authentication for the user. Without a password in Snowflake, a user cannot log in using Snowflake authentication and must use federated authentication instead.
NEW QUESTION # 46
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?
- A. Enabling change tracking adds a pair of hidden columns to the table and begins storing change tracking metadata. The values in these hidden CDC data columns provide the input for the stream metadata columns. The columns consume a small amount of stor-age.
- B. 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.
- C. As an alternative to streams, Snowflake supports querying change tracking metadata for views using the CHANGES clause for SELECT statements.
- D. For streams on views, change tracking must be enabled explicitly for the view and un-derlying tables to add the hidden columns to these tables.
- E. Views with GROUP BY & LIMIT Clause are supported by Snowflake.
Answer: E
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 # 47
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?
- A.
- B.
- C.
- D.
Answer: C
Explanation:
Explanation
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = 'North America'. Therefore, the function call in Option B will return the desired result.
NEW QUESTION # 48
Streams record the differences between two offsets. If a row is added and then updated in the cur-rent offset, what will be the value of METADATA$ISUPDATE Columns in this scenario?
- A. FALSE
- B. TRUE
- C. UPDATE
- D. INSERT
Answer: A
Explanation:
Explanation
Stream Columns
A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:
METADATA$ACTION
Indicates the DML operation (INSERT, DELETE) recorded.
METADATA$ISUPDATE
Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records in the stream with a metadata column METADATA$ISUPDATE values set to TRUE.
METADATA$ROW_ID
Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.
Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.
NEW QUESTION # 49
How Data Engineer can do Monitoring of Files which are Staged Internally during Continuous data pipelines loading process? [Select all that apply]
- A. Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days.
- B. She Can Monitor the files using Metadata maintained by Snowflake i.e. file-name,last_modified date etc.
- C. She can use the DATA_LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
- D. She can Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
- E. She can use the DATA_VALIDATE function to validate the data files She have loaded and can retrieve any errors encountered during the load.
Answer: A,B,D
Explanation:
Explanation
Monitoring Files Staged Internally
Snowflake maintains detailed metadata for each file uploaded into internal stage (for users, tables, and stages), including:
File name
File size (compressed, if compression was specified during upload)
LAST_MODIFIED date, i.e. the timestamp when the data file was initially staged or when it was last modified, whichever is later In addition, Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days. The metadata can be used to monitor and manage the loading process, including de-leting files after upload completes:
Use the LIST command to view the status of data files that have been staged.
Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
Use the VALIDATE function to validate the data files you've loaded and retrieve any errors en-countered during the load.
Use the LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
NEW QUESTION # 50
Can Masking policies be applied to virtual columns?
- A. FALSE
- B. TRUE
Answer: A
NEW QUESTION # 51
A Data Engineer has developed a dashboard that will issue the same SQL select clause to Snowflake every 12 hours.
---will Snowflake use the persisted query results from the result cache provided that the underlying data has not changed^
- A. 14 days
- B. 24 hours
- C. 12 hours
- D. 31 days
Answer: A
Explanation:
Explanation
Snowflake uses the result cache to store the results of queries that have been executed recently. The result cache is maintained at the account level and is shared across all sessions and users. The result cache is invalidated when any changes are made to the tables or views referenced by the query. Snowflake also has a retention policy for the result cache, which determines how long the results are kept in the cache before they are purged. The default retention period for the result cache is 24 hours, but it can be changed at the account, user, or session level. However, there is a maximum retention period of 14 days for the result cache, which cannot be exceeded. Therefore, if the underlying data has not changed, Snowflake will use the persisted query results from the result cache for up to 14 days.
NEW QUESTION # 52
What is a characteristic of the use of binding variables in JavaScript stored procedures in Snowflake?
- A. All types of JavaScript variables can be bound
- B. Users are restricted from binding JavaScript variables because they create SQL injection attack vulnerabilities
- C. Only JavaScript variables of type number, string and sf Date can be bound
- D. All Snowflake first-class objects can be bound
Answer: C
Explanation:
Explanation
A characteristic of the use of binding variables in JavaScript stored procedures in Snowflake is that only JavaScript variables of type number, string and sf Date can be bound. Binding variables are a way to pass values from JavaScript variables to SQL statements within a stored procedure. Binding variables can improve the security and performance of the stored procedure by preventing SQL injection attacks and reducing the parsing overhead. However, not all types of JavaScript variables can be bound. Only the primitive types number and string, and the Snowflake-specific type sf Date, can be bound. The other options are incorrect because they do not describe a characteristic of the use of binding variables in JavaScript stored procedures in Snowflake. Option A is incorrect because authenticator is not a type of JavaScript variable, but a parameter of the snowflake.connector.connect function. Option B is incorrect because arrow_number_to_decimal is not a type of JavaScript variable, but a parameter of the snowflake.connector.connect function. Option D is incorrect because users are not restricted from binding JavaScript variables, but encouraged to do so.
NEW QUESTION # 53
Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?
- A. IS_GRANTED_TO_INVOKER_ROLE
- B. IKVOKER_ROLE
- C. IS_RCLE_IN_SESSION
- D. CORRECT_ROLE
Answer: C
Explanation:
Explanation
The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user's session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:
CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION('HR') THEN val ELSE REGEXP_REPLACE(val, '(.).(.@.)', '\1****\2') END; In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column.
The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.
NEW QUESTION # 54
The smaller the average depth, the better clustered the table is with regards to the specified column?
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 55
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?
- A. You require the warehouse to be available with delay.
- B. You have a low, fluctuating workload for the warehouse.
- C. You have a heavy, steady workload for the warehouse.
- D. You require the warehouse to be available with no delay or lag time.
Answer: C,D
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 # 56
A database contains a table and a stored procedure defined as.
No other operations are affecting the log_table.
What will be the outcome of the procedure call?
- A. The Iog_table contains one record and the stored procedure returned 1 as a return value
- B. The Iog_table contains zero records and the stored procedure returned 1 as a return value
- C. The Iog_table contains zero records and the stored procedure returned NULL as a return value
- D. The log_table contains one record and the stored procedure returned NULL as a return value
Answer: A
Explanation:
Explanation
The stored procedure is defined with a FLOAT return type and a JavaScript language. The body of the stored procedure contains a SQL statement that inserts a row into the log_table with a value of '1' for col1. The body also contains a return statement that returns 1 as a float value. When the stored procedure is called with any VARCHAR parameter, it will execute successfully and insert one record into the log_table and return 1 as a return value. The other options are not correct because:
The log_table will not be empty after the stored procedure call, as it will contain one record inserted by the SQL statement.
The stored procedure will not return NULL as a return value, as it has an explicit return statement that returns 1.
NEW QUESTION # 57
Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?
- A. OPEARTE
- B. MANAGE GRANTS
- C. OWNERSHIP
- D. MODIFY
Answer: C
NEW QUESTION # 58
Dominic, a Data Engineer wants to resume the pipe named stalepipe3 which got stale after 14 days. To do the same, he called the SYSTEM$PIPE_FORCE_RESUME function select sys-tem$pipe_force_resume('snowmydb.mysnowschema.stalepipe3','staleness_check_override'); Let's say If the pipe is resumed 16 days after it was paused, what will happened to the event notifi-cation that were received on the first and second days after the pipe was paused?
- A. Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused.
- B. Pipe maintains Metadata history of files for 64 days, so in this scenarios Snowpipe pro-cessed all the event notifications that were received for 16 days or so.
- C. Once the Pipe got stale, all the events got purged automatically & pipe needs to be rec-reated with modified properties.
- D. All the events get processed from day 1 if the PURGE properties in the PIPE object definition set to be FALSE initially.
Answer: A
Explanation:
Explanation
When a pipe is paused, event messages received for the pipe enter a limited retention period. The period is 14 days by default. If a pipe is paused for longer than 14 days, it is considered stale.
To resume a stale pipe, a qualified role must call the SYSTEM$PIPE_FORCE_RESUME function and input the STALENESS_CHECK_OVERRIDE argument. This argument indicates an under-standing that the role is resuming a stale pipe.
For example, resume the stale stalepipe1 pipe in the mydb.myschema database and schema:
select sys-tem$pipe_force_resume('mydb.myschema.stalepipe3','staleness_check_override'); As an event notification received while a pipe is paused reaches the end of the limited retention pe-riod, Snowflake schedules it to be dropped from the internal metadata. If the pipe is later resumed, Snowpipe processes these older notifications on a best effort basis. Snowflake cannot guarantee that they are processed.
For example, if a pipe is resumed 15 days after it was paused, Snowpipe generally skips any event notifications that were received on the first day the pipe was paused (i.e. that are now more than 14 days old).
If the pipe is resumed 16 days after it was paused, Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused. And so on.
NEW QUESTION # 59
......
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