Verified DEA-C01 dumps Q&As - Pass Guarantee Exam Dumps Test Engine [2023]
DEA-C01 dumps and 67 unique questions
NEW QUESTION # 19
A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector.
Based on the user's selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long How can this situation be improved?
- A. Increase the number of maximum clusters of the virtual warehouse
- B. Define a clustering key for the sales data table
- C. Rewrite the report to eliminate the use of the loop construct
- D. Increase the size of the virtual warehouse
Answer: C
Explanation:
Explanation
This option is the best way to improve the situation, as using a loop construct to run SQL queries for each customer is very inefficient and slow. Instead, the report should be rewritten to use a single SQL query that joins the customer and sales data tables and applies the query parameters as filters. This way, the report can leverage Snowflake's parallel processing and optimization capabilities and reduce the network overhead and latency.
NEW QUESTION # 20
Snowflake does not treat the inner transaction as nested; instead, the inner transaction is a separate transaction.
What is term used to call these Transaction?
- A. Nested Scope Transaction
- B. Inner Transaction
- C. Atomic Transaction
- D. Scoped transactions
- E. Enclosed Transaction
Answer: D
NEW QUESTION # 21
Which of the following statements is/are incorrect regarding Fail-safe data recovery?
- A. Historical data in transient tables can be recovered by Snowflake due to Operation fail-ure after the Time Travel retention period ends using Fail-safe.
- B. If downtime and the time required to reload lost data are factors, permanent tables, even with their added Fail-safe costs, may offer a better overall solution than transient tables.
- C. Long-lived tables, such as fact tables, should always be defined as permanent to ensure they are fully protected by Fail-safe.
- D. Short-lived tables (i.e. <1 day), such as ETL work tables, can be defined as transient to eliminate Fail-safe costs.
- E. Data stored in temporary tables is not recoverable after the table is dropped as they do not have fail-safe.
Answer: A
NEW QUESTION # 22
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)
- A. DataFraas.writeO
- B. session.jdbc_connection()
- C. session.read.json{)
- D. session.sql()
- E. session,table()
- F. session.builder()
Answer: C,D,E
Explanation:
Explanation
The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries. The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.
NEW QUESTION # 23
Which two Account usage views can be used for auditing Dynamic data masking purpose?
- A. DYNAMIC POLICY_REFERENCES
- B. DYNAMIC MASKING POLICIES
- C. POLICY_REFERENCES
- D. MASKING POLICIES
Answer: C,D
NEW QUESTION # 24
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?
- A. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.
- B. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
- C. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table
- D. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage
Answer: A
Explanation:
Explanation
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.
NEW QUESTION # 25
Steven created the task, what additional privileges required by Steven on the task so that he can suspend or resume the tasks?
- A. In addition to the task owner, a Steven Role must have OPERATE privilege on the task so that he can suspend or resume the task.
- B. Steven is already owner of the task; he can execute the task & suspend/resume the task without any additional privileges.
- C. Steven must have SUSPEND privilege on the task so that he can suspend or resume the task.
- D. Steven needs to have Global Managed RESUME privilege by TASK administrator.
Answer: A
Explanation:
Explanation
In addition to the task ownership privilege, a role that has the OPERATE privilege on the task can suspend or resume the task.
NEW QUESTION # 26
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. 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.
- B. Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused.
- C. All the events get processed from day 1 if the PURGE properties in the PIPE object definition set to be FALSE initially.
- D. Once the Pipe got stale, all the events got purged automatically & pipe needs to be rec-reated with modified properties.
Answer: B
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 # 27
Stuart, a Lead Data Engineer in MACRO Data Company created streams on set of External tables. He has been asked to extend the data retention period of the stream for 90 days, which parameter he can utilize to enable this extension?
- A. MAX_DATA_EXTENSION_TIME_IN_DAYS
- B. DATA_RETENTION_TIME_IN_DAYS
- C. None of the above
- D. DATA_EXTENSION_TIME_IN_DAYS
Answer: C
Explanation:
Explanation
External tables do not have data retention period applicable.
Good to Understand other Options available.
DATA_RETENTION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Number of days for which Snowflake retains historical data for performing Time Trav-el actions (SELECT, CLONE, UNDROP) on the object. A value of 0 effectively disables Time Travel for the specified database, schema, or table.
Values:
0 or 1 (for Standard Edition)
0 to 90 (for Enterprise Edition or higher)
Default:
1
MAX_DATA_EXTENSION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Maximum number of days for which Snowflake can extend the data retention period for tables to prevent streams on the tables from becoming stale. By default, if the DA-TA_RETENTION_TIME_IN_DAYS setting for a source table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to the stream's offset, up to a maximum of 14 days, regardless of the Snowflake Edition for your account. The MAX_DATA_EXTENSION_TIME_IN_DAYS parameter enables you to limit this automatic ex-tension period to control storage costs for data retention or for compliance reasons.
This parameter can be set at the account, database, schema, and table levels. Note that setting the parameter at the account or schema level only affects tables for which the parameter has not already been explicitly set at a lower level (e.g. at the table level by the table owner). A value of 0 effective-ly disables the automatic extension for the specified database, schema, or table.
Values:
0 to 90 (i.e. 90 days) - a value of 0 disables the automatic extension of the data retention period. To increase the maximum value for tables in your account, Client needs to contact Snowflake Sup-port.
Default:14
NEW QUESTION # 28
Select the correct usage statements with regards to SQL UDF?
- A. You can include only one query expression.
- B. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
- C. Scalar functions (UDFs) have a limit of 500 input arguments.
- D. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
- E. All of above are correct.
Answer: E
NEW QUESTION # 29
Which stages support external tables?
- A. External stages only from any region, and any cloud provider
- B. Internal stages only; within a single Snowflake account
- C. internal stages only from any Snowflake account in the organization
- D. External stages only, only on the same region and cloud provider as the Snowflake account
Answer: A
Explanation:
Explanation
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.
NEW QUESTION # 30
A Data Engineer wants to check the status of a pipe named my_pipe. The pipe is inside a database named test and a schema named Extract (case-sensitive).
Which querywill provide the status of the pipe?
- A. SELECT FROM SYSTEM$PIPE_STATUS (,test.,,Extracr,,.ny_pipe, i I
- B. SELE2T * FROM SYSTEM$PIPE_STATUS < ' test. "Extract", my_pipe');
- C. SELECT * FROM SYSTEM$PIPE_STATUS ("test. 'extract' .my_pipe"};
- D. SELECT FROM SYSTEM$PIPE_STATUS (''test.'extract'.my_pipe"i:
Answer: B
Explanation:
Explanation
The query that will provide the status of the pipe is SELECT * FROM
SYSTEM$PIPE_STATUS('test."Extract".my_pipe');. The SYSTEM$PIPE_STATUS function returns information about a pipe, such as its name, status, last received message timestamp, etc. The function takes one argument: the pipe name in a qualified form. The pipe name should include the database name, the schema name, and the pipe name, separated by dots. If any of these names are case-sensitive identifiers, they should be enclosed in double quotes. In this case, the schema name Extract is case-sensitive and should be quoted. The other options are incorrect because they do not follow the correct syntax for the pipe name argument. Option A and B use single quotes instead of double quotes for case-sensitive identifiers. Option D uses double quotes instead of single quotes for non-case-sensitive identifiers.
NEW QUESTION # 31
Which callback function is required within a JavaScript User-Defined Function (UDF) for it to execute successfully?
- A. handler
- B. finalize ()
- C. processRow ()
- D. initialize ()
Answer: C
Explanation:
Explanation
The processRow () callback function is required within a JavaScript UDF for it to execute successfully. This function defines how each row of input data is processed and what output is returned. The other callback functions are optional and can be used for initialization, finalization, or error handling.
NEW QUESTION # 32
Data Engineer decided to call the public REST endpoints to load data and retrieve load history re-ports. Which of the following REST endpoints and a Snowflake Information Schema table function for viewing your load history can be used by her? [Select All that apply]
- A. REST endpoints: insertReport or loadHistoryScan
- B. Information Schema table function: LOAD_HISTORY
- C. Information Schema table function: COPY_HISTORY
- D. Account Usage view:COPY_HISTORY
- E. REST endpoints: loadinsertReport or HistoryScan
Answer: A,C,D
Explanation:
Explanation
Snowflake provides REST endpoints and an Snowflake Information Schema table function for viewing your load history:
REST endpoints:
insertReport
loadHistoryScan
Information Schema table function:
COPY_HISTORY
Account Usage view:
COPY_HISTORY
NEW QUESTION # 33
Assuming a Data Engineer has all appropriate privileges and context which statements would be used to assess whether the User-Defined Function (UDF), MTBATA3ASZ. SALES .REVENUE_BY_REGION, exists and is secure? (Select TWO)
- A. SHOW SECURE FUNCTIONS LIKE 'REVENUE 3Y REGION' IN SCHEMA SALES;
- B. SHOW EXTERNAL FUNCTIONS LIKE 'REVENUE_BY_REGION'IB SCHEMA SALES;
- C. SHOW DS2R FUNCTIONS LIKE 'REVEX'^BYJIESION' IN SCHEMA SALES;
- D. SELECT IS_SEC"JRE FROM INFOR>LVTICN_SCHEMA. FUNCTIONS WHERE
FUNCTION_SCHEMA = 'SALES1 AND FUNGTZON_NAME = ' REVENUE_BY_REGION'; - E. SELECT IS_SECURE FROM SNOWFLAKE. INFCRXATION_SCKZMA. FUNCTIONS WHERE FUNCTI0N_3CHEMA = 'SALES' AND FUNCTI CN_NAXE = *ftEVEXUE_BY_RKXQH4;
Answer: C,E
Explanation:
Explanation
The statements that would be used to assess whether the UDF, MTBATA3ASZ. SALES
.REVENUE_BY_REGION, exists and is secure are:
SHOW DS2R FUNCTIONS LIKE 'REVEX'^BYJIESION' IN SCHEMA SALES;: This statement will show information about the UDF, including its name, schema, database, arguments, return type, language, and security option. If the UDF does not exist, the statement will return an empty result set.
SELECT IS_SECURE FROM SNOWFLAKE. INFCRXATION_SCKZMA. FUNCTIONS WHERE
FUNCTI0N_3CHEMA = 'SALES' AND FUNCTI CN_NAXE = *ftEVEXUE_BY_RKXQH4;: This statement will query the SNOWFLAKE.INFORMATION_SCHEMA.FUNCTIONS view, which contains metadata about the UDFs in the current database. The statement will return the IS_SECURE column, which indicates whether the UDF is secure or not. If the UDF does not exist, the statement will return an empty result set. The other statements are not correct because:
SELECT IS_SEC"JRE FROM INFOR>LVTICN_SCHEMA. FUNCTIONS WHERE
FUNCTION_SCHEMA = 'SALES1 AND FUNGTZON_NAME = ' REVENUE_BY_REGION';: This statement will query the INFORMATION_SCHEMA.FUNCTIONS view, which contains metadata about the UDFs in the current schema. However, the statement has a typo in the schema name ('SALES1' instead of 'SALES'), which will cause it to fail or return incorrect results.
SHOW EXTERNAL FUNCTIONS LIKE 'REVENUE_BY_REGION' IB SCHEMA SALES;: This statement will show information about external functions, not UDFs. External functions are Snowflake functions that invoke external services via HTTPS requests and responses. The statement will not return any results for the UDF.
SHOW SECURE FUNCTIONS LIKE 'REVENUE 3Y REGION' IN SCHEMA SALES;: This
statement is invalid because there is no such thing as secure functions in Snowflake. Secure functions are a feature of some other databases, such as PostgreSQL, but not Snowflake. The statement will cause a syntax error.
NEW QUESTION # 34
Data Engineer looking out for quick tool for understanding the mechanics of queries & need to know more about the performance or behaviour of a particular query.
He should go to which feature of snowflake which can help him to spot typical mistakes in SQL query expressions to identify potential performance bottlenecks and improvement opportunities?
- A. Query Profile
- B. Performance Metadata table
- C. Query Optimizer
- D. Query Designer
Answer: A
Explanation:
Explanation
Query Profile, available through the classic web interface, provides execution details for a query. For the selected query, it provides a graphical representation of the main components of the pro-cessing plan for the query, with statistics for each component, along with details and statistics for the overall query.
Query Profile is a powerful tool for understanding the mechanics of queries. It can be used whenev-er you want or need to know more about the performance or behavior of a particular query. It is de-signed to help you spot typical mistakes in SQL query expressions to identify potential performance bottlenecks and improvement opportunities.
NEW QUESTION # 35
If using a JavaScript UDF in a masking policy, Data Engineer needs to ensure the data type of the column, UDF, and masking policy match irrespective of case-sensitivity?
- A. TRUE
- B. FALSE
Answer: B
Explanation:
Explanation
Please note JavaScript is case sensitive but if we are using a JavaScript UDF in a masking policy, ensure the data type of the column, UDF, and masking policy match.
NEW QUESTION # 36
The following is returned fromSYSTEMCLUSTERING_INFORMATION () for a tablenamed orders with adate column named O_ORDERDATE:
What does the total_constant_partition_count value indicate about this table?
- A. The data inO_ORDERDATEhas a very low cardinality as there are 493 micro-partitions where there is only a single distinct value in that column for all rows in the micro-partition
- B. The data inO_ORDERDATEdoes not change very often as there are 493 micro-partitionscontaining rows where that column has not been modified since the row was created
- C. The table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering
- D. The table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the range of values in that column overlap with every other micro partition in the table.
Answer: D
Explanation:
Explanation
The total_constant_partition_count value indicates the number of micro-partitions where the clustering key column has a constant value across all rows in the micro-partition. However, this does not necessarily mean that the table is clustered well on that column, as there could be other micro-partitions where the range of values in that column overlap with each other. This is the case for the orders table, as the clustering depth is 1, which means that every micro-partition overlaps with every other micro-partition on O_ORDERDATE. This indicates that the table is not clustered well on O_ORDERDATE and could benefit from reclustering.
NEW QUESTION # 37
The COPY command supports several options for loading data files from a stage i.e.
I. By path
II. Specifying a list of specific files to load.
III. Using pattern matching to identify specific files by pattern.
IV. Organize files into logical paths that reflect a scheduling pattern.
Of the aforesaid options for identifying/specifying data files to load from a stage, which option in general is the fastest & best considerate?
- A. III
- B. II
- C. I
- D. IV
Answer: B
Explanation:
Explanation
Of the above options for identifying/specifying data files to load from a stage, providing a discrete list of files is generally the fastest; however, the FILES parameter supports a maximum of 1,000 files, meaning a COPY command executed with the FILES parameter can only load up to 1,000 files.
For example:
copy into load1 from @%load1/Snow1/ files=('mydata1.csv', 'mydata2.csv', 'mydata3.csv')
NEW QUESTION # 38
A Data Engineer is working on a continuous data pipeline which receives data from Amazon Kinesis Firehose and loads the data into a staging table which will later be used in the data transformation process The average file size is 300-500 MB.
The Engineer needs to ensure that Snowpipe is performant while minimizing costs.
How can this be achieved?
- A. Decrease the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose
- B. Change the file compression size and increase the frequency of the Snowpipe loads
- C. Increase the size of the virtual warehouse used by Snowpipe.
- D. Split the files before loading them andset the SIZE_LIMIT option to 250 MB.
Answer: D
Explanation:
Explanation
This option is the best way to ensure that Snowpipe is performant while minimizing costs. By splitting the files before loading them, the Data Engineer can reduce the size of each file and increase the parallelism of loading.
By setting the SIZE_LIMIT option to 250 MB, the Data Engineer can specify the maximum file size that can be loaded bySnowpipe, which can prevent performance degradation or errors due to large files. The other options are not optimal because:
Increasing the size of the virtual warehouse used by Snowpipe will increase the performance but also increase the costs, as larger warehouses consume more credits per hour.
Changing the file compression size and increasing the frequency of the Snowpipe loads will not have much impact on performance or costs, as Snowpipe already supports various compression formats and automatically loads files as soon as they are detected in the stage.
Decreasing the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose will not affect Snowpipe performance or costs, as Snowpipe does not depend on Kinesis Firehose buffer size but rather on its own SIZE_LIMIT option.
NEW QUESTION # 39
What is a characteristic of the use of external tokenization?
- A. Secure data sharing can be used with external tokenization
- B. External tokenization allows (he preservation of analytical values after de-identification
- C. Pre-loading of unmasked data is supported with external tokenization
- D. External tokenization cannot be used with database replication
Answer: B
Explanation:
Explanation
External tokenization is a feature in Snowflake that allows users to replace sensitive data values with tokens that are generated and managed by an external service. External tokenization allows the preservation of analytical values after de-identification, such as preserving the format, length, or range of the original values.
This way, users can perform analytics on the tokenized data without compromising the security or privacy of the sensitive data.
NEW QUESTION # 40
A Data Engineer has created table t1 with datatype VARIANT:
create or replace table t1 (cl variant);
The Engineer has loaded the following JSON data set. which has information about 4 laptop models into the table:
The Engineer now wants to query that data set so that results are shown as normal structured data. The result should be 4 rows and 4 columns without the double quotes surrounding the data elements in the JSON data.
The result should be similar to the use case where the data was selected from a normal relational table z2 where t2 has string data type columns model__id. model, manufacturer, and =iccisi_r.an=. and is queried with the SQL clause select * from t2; Which select command will produce the correct results?
- A.
- B.
- C.
- D.
Answer: D
NEW QUESTION # 41
Which of the following System keeps the following characteristics?
a. It will keep in it all the raw data.
b. Generally, the users of it is data scientists and data developers.
c. Flat architecture
d. Highly agile
- A. Data Mart
- B. Data Warehouse
- C. Data Lake
- D. Data Hub
Answer: C
NEW QUESTION # 42
A new customer table is created by a data pipeline in a Snowflake schema where MANAGED ACCESSenabled.
.... Can gran access to the CUSTOMER table? (Select THREE.)
- A. The role that owns the database
- B. The USERADMIN role with the manage grants privilege
- C. The role that owns the schema
- D. The role that owns the customer table
- E. The SYSADMIN role
- F. The SECURITYADMIN role
Answer: A,C,F
Explanation:
Explanation
The roles that can grant access to the CUSTOMER table are the role that owns the schema, the role that owns the database, and the SECURITYADMIN role. These roles have the ownership or the manage grants privilege on the schema or the database level, which allows them to grant access to any object within them. The other options are incorrect because they do not have the necessary privilege to grant access to the CUSTOMER table. Option C is incorrect because the role that owns the customer table cannot grant access to itself or to other roles. Option D is incorrect because the SYSADMIN role does not have the manage grants privilege by default and cannot grant access to objects that it does not own. Option F is incorrect because the USERADMIN role with the manage grants privilege can only grant access to users and roles, not to tables.
NEW QUESTION # 43
Data Engineer is using existing pipe that automates data loads using event notifications, later he figured out the needs to modify pipe properties. For the same, He decided to recreate the pipe as best practice. He followed the below steps for the same.
1. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
2. Recreate the pipe (using CREATE OR REPLACE PIPE).
3. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
Which are the Missing recommended steps while Recreating Pipes for Automated Data Loads?
- A. Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).
- B. Terminate the existing pipe (using ALTER PIPE ... SET PIPE_EXECUTION_TERMINATE = true) before recreation.
- C. CREATE OR REPLACE PIPE command will recreate the PIPE successfully.
- D. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true) Pre & Post recreation & Resume after recreation (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = false).
Answer: D
Explanation:
Explanation
Recreating a pipe (using a CREATE OR REPLACE PIPE statement) is necessary to modify most pipe properties.
Recreating Pipes for Automated Data Loads
When recreating a pipe that automates data loads using event notifications, it's recommended that Data Engineer complete the following steps:
1. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true).
2. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is PAUSED.
3. Recreate the pipe (using CREATE OR REPLACE PIPE).
4. Pause the pipe again.
5. Review the configuration steps for your cloud messaging service to ensure the settings are still accurate.
6. Query the SYSTEM$PIPE_STATUS function again and verify that the pipe execution state is RUNNING.
NEW QUESTION # 44
......
DEA-C01 Dumps for Pass Guaranteed - Pass DEA-C01 Exam: https://www.real4dumps.com/DEA-C01_examcollection.html
DEA-C01 Exam Dumps - Try Best DEA-C01 Exam Questions: https://drive.google.com/open?id=1gOtDeywy15H_9lxZ9zc5doyr4k2hYGxV
