DEA-C01問題集と練習テスト（67問題） [Q36-Q58]

Share

（2024)DEA-C01問題集と練習テスト（67問題)

ガイド（2024年最新)リアルなSnowflake DEA-C01試験問題

質問 # 36
Mark the incorrect statement when Data Engineer implement Automating Continuous Data Loading Using Cloud Messaging?

  • A. Notifications identify the cloud storage event and include a list of the file names. They do not include the actual data in the files.
  • B. 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.
  • C. Triggering automated Snowpipe data loads using S3 event messages is supported by Snowflake accounts hosted on Cloud Platform like AWS, GCP or AZURE.
  • D. Automated Snowpipe uses event notifications to determine when new files arrive in monitored cloud storage and are ready to load.

正解：C

解説：
Explanation
Triggering automated Snowpipe data loads using S3 event messages is supported by Snowflake ac-counts hosted on Amazon Web Services (AWS) only.
Rest is correct statements.


質問 # 37
To advance the offset of a stream to the current table version without consuming the change data in a DML operation, which of the following operations can be done by Data Engineer? [Select 2]

  • A. Delete the offset using STREAM properties SYSTEM$RESET_OFFSET( <stream_id> )
  • B. using the CREATE OR REPLACE STREAM syntax, Recreate the STREAM
  • C. Insert the current change data into a temporary table. In the INSERT statement, query the stream but include a WHERE clause that filters out all of the change data (e.g. WHERE 0 = 1).
  • D. A stream advances the offset only when it is used in a DML transaction, so none of the options works without consuming the change data of table.

正解：B、C

解説：
Explanation
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 thestate 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.
A new table version is created whenever a transaction that includes one or more DML statements is committed to the table.
In the transaction history for a table, a stream offset is located between two table versions. Query-ing a stream returns the changes caused by transactions committed after the offset and at or before the current time.
Multiple queries can independently consume the same change data from a stream without changing the offset.
A stream advances the offset only when it is used in a DML transaction. This behavior applies to both explicit and autocommit transactions. (By default, when a DML statement is execut-ed, an autocommit transaction is implicitly started and the transaction is committed at the comple-tion of the statement. This behavior is controlled with the AUTOCOMMIT parameter.) Querying a stream alone does not advance its offset, even within an explicit transaction; the stream contents must be consumed in a DML statement.
To advance the offset of a stream to the current table version without consuming the change data in a DML operation, complete either of the following actions:
Recreate the stream (using the CREATE OR REPLACE STREAM syntax).
Insert the current change data into a temporary table. In the INSERT statement, query the stream but include a WHERE clause that filters out all of the change data (e.g. WHERE 0 = 1).


質問 # 38
In efforts to recover the dropped child tables within schema named SCV_SCHEMA by Data Engi-neer, She found that DATA_RETENTION_TIME_IN_DAYS parameter set with value 45 days at Schema level &the data retention period for child tables explicitly set at 85 days. What will happen when she will try to run undrop table command on Child tables to recover them on the 50th day as-suming SCV_SCHEMA is already dropped on 45th day?

  • A. Child tables can be recovered using Fail-Safe SQL commands.
  • B. When a schema is already dropped, the data retention period for child tables, if explicit-ly set to be different from the retention of the schema, is not honoured. So UNDROP command will fail to run on
    50th day for Child tables recovery.
  • C. Data Engineer needs to first recover the Schema & then Child tables will automatically be recovered irrespective of Retention Inheritance.
  • D. To honor the data retention period for child tables, She will ab able to recover the child tables on 50th day as DATA_RETENTION_TIME_IN_DAYS is explicitly set with higher retention value.

正解：B

解説：
Explanation
Dropped Containers and Object Retention Inheritance
Currently, when a database is dropped, the data retention period for child schemas or tables, if ex-plicitly set to be different from the retention of the database, is not honored. The child schemas or tables are retained for the same period of time as the database.
Similarly, when a schema is dropped, the data retention period for child tables, if explicitly set to be different from the retention of the schema, is not honored. The child tables are retained for the same period of time as the schema.
To honor the data retention period for these child objects (schemas or tables), drop them explicitly before you drop the database or schema.


質問 # 39
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. Rewrite the report to eliminate the use of the loop construct
  • B. Define a clustering key for the sales data table
  • C. Increase the number of maximum clusters of the virtual warehouse
  • D. Increase the size of the virtual warehouse

正解：A

解説：
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.


質問 # 40
To support Time Travel, Which of the following SQL extensions/parameters/commands have been implemented?

  • A. UNDROP command for tables, schemas, and databases.
  • B. ONSET (time difference in seconds from the present time)
  • C. OFFSET (time difference in seconds from the present time)
  • D. STATEMENT_ID (identifier for statement, e.g. query ID)
  • E. STATEMENT (identifier for statement, e.g. query ID)
  • F. AT | BEFORE clause which can be specified in the CREATE ... CLONE commands.

正解：A、C、E、F


質問 # 41
Elon, a Data Engineer, needs to Split Semi-structured Elements from the Source files and load them as an array into Separate Columns.
Source File:
1.+----------------------------------------------------------------------+
2.| $1 |
3.|----------------------------------------------------------------------|
4.| {"mac_address": {"host1": "197.128.1.1","host2": "197.168.0.1"}}, |
5.| {"mac_address": {"host1": "197.168.2.1","host2": "197.168.3.1"}} |
6.+----------------------------------------------------------------------+ Output: Splitting the Machine Address as below.
1.COL1 | COL2 |
2.|----------+----------|
3.| [ | [ |
4.| "197", | "197", |
5.| "128", | "168", |
6.| "1", | "0", |
7.| "1" | "1" |
8.| ] | ] |
9.| [ | [ |
10.| "197", | "197", |
11.| "168", | "168", |
12.| "2", | "3", |
13.| "1" | "1" |
14.| ] | ]
Which SnowFlake Function can Elon use to transform this semi structured data in the output for-mat?

  • A. GROUP_BY_CONNECT
  • B. SPLIT
  • C. NEST
  • D. CONVERT_TO_ARRAY

正解：B


質問 # 42
Data Engineer Loading File named snowdata.tsv in the /datadir directory from his local machine to Snowflake stage and try to prefix the file with a folder named tablestage, please mark the correct command which helps him to load the files data into snowflake internal Table stage?

  • A. put file://c:\datadir\snowdata.tsv @%tablestage;
  • B. put file://c:\datadir\snowdata.tsv @~/tablestage;
  • C. put file:///datadir/snowdata.tsv @%tablestage;
  • D. put file://c:\datadir\snowdata.tsv @tablestage;

正解：A

解説：
Explanation
Execute PUT to upload (stage) local data files into an internal stage.
@% character combination identifies a table stage.


質問 # 43
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?

  • A. SELECT SYSTEM$CLUSTERING_INTFORMATICII ('Invoice' ) ;
  • B. CALL $CLUSTERINS_INFORMATION('Invoice');
  • C. CALL SYSTEM$CLUSTERING_INFORMATION ('Invoice');
  • D. SELECT $CLUSTERXNG_INFQRMATION ('Invoice')'

正解：A

解説：
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.


質問 # 44
Which Role that is dedicated to user and role management only?

  • A. USERADMIN
  • B. ORGADMIN
  • C. PUBLIC
  • D. SYSADMIN
  • E. SECURITYADMIN

正解：A


質問 # 45
A Data Engineer is working on a Snowflake deployment in AWS eu-west-1 (Ireland). The Engineer is planning to load data from staged files into target tables using the copy into command Which sources are valid? (Select THREE)

  • A. Internal stage on AWS eu-central-1 (Frankfurt)
  • B. Internal stage on GCP us-central1 (Iowa)
  • C. External stage on GCP us-central1 (Iowa)
  • D. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)
  • E. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
  • F. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)

正解：C、E、F

解説：
Explanation
The valid sources for loading data from staged files into target tables using the copy into command are:
External stage on GCP us-central1 (Iowa): This is a valid source because Snowflake supports cross-cloud data loading from external stages on different cloud platforms and regions than the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland): This is a valid source because Snowflake supports data loading from external stages on the same cloud platform and region as the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt): This is a valid source because Snowflake supports cross-region data loading from external stages on different regions than the Snowflake deployment within the same cloud platform. The invalid sources are:
Internal stage on GCP us-central1 (Iowa): This is an invalid source because internal stages are always located on the same cloud platform and region as the Snowflake deployment. Therefore, an internal stage on GCP us-central1 (Iowa) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
Internal stage on AWS eu-central-1 (Frankfurt): This is an invalid source because internal stages are always located on the same region as the Snowflake deployment. Therefore, an internal stage on AWS eu-central-1 (Frankfurt) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland): This is an invalid source because SSO stands for Single Sign-On, which is a security integration feature in Snowflake, not a data staging option.


質問 # 46
Harry using Snowflake Enterprise Edition & decided to scale in/out the Cluster in automatic mode. He needs to configure some warehouses as multi cluster mode and some among them in Standard mode as per needs.
If Harry is using Snowflake Enterprise Edition (or a higher edition), all his warehouses should be configured as multi-cluster warehouses only.

  • A. TRUE
  • B. FALSE

正解：A

解説：
Explanation
If you are using Snowflake Enterprise Edition (or a higher edition), all your warehouses should be configured as multi-cluster warehouses.


質問 # 47
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.

  • A. Querying information__schema .packages will provide a list of supported Python packages and versions
  • B. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
  • C. Python packages can only be loaded in a local environment
  • D. Third-party supported Python packages are locked down to prevent hitting
  • E. Python packages can access any external endpoints

正解：A、B、E

解説：
Explanation
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.


質問 # 48
Mark the Incorrect Statements with respect to types of streams supported by Snowflake?

  • A. Insert-only Stream supported on external tables only.
  • B. An insert-only stream tracks row inserts & Delete ops only
  • C. Standard streams cannot retrieve update data for geospatial data.
  • D. An append-only stream returns the appended rows only and therefore can be much more performant than a standard stream for extract, load, transform (ELT).

正解：B

解説：
Explanation
Standard Stream:
Supported for streams on tables, directory tables, or views. A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, updates, and deletes (including table trun-cates). This stream type performs a join on inserted and deleted rows in the change set to provide the row level delta. As a net effect, for example, a row that is inserted and then deleted between two transactional points of time in a table is removed in the delta (i.e. is not returned when the stream is queried).
Append-only Stream:
Supported for streams on standard tables, directory tables, or views. An append-only stream tracks row inserts only. Update and delete operations (including table truncates) are not recorded. For ex-ample, if 10 rows are inserted into a table and then 5 of those rows are deleted before the offset for an append-only stream is advanced, the stream records 10 rows.
An append-only stream returns the appended rows only and therefore can be much more performant than a standard stream for extract, load, transform (ELT) and similar scenarios that depend exclu-sively on row inserts. For example, a source table can be truncated immediately after the rows in an append-only stream are consumed, and the record deletions do not contribute to the overhead the next time the stream is queried or consumed.
Insert-only Stream:
Supported for streams on external tables only. An insert-only stream tracks row inserts only; they do not record delete operations that remove rows from an inserted set (i.e. no-ops). For example, in-between any two offsets, if File1 is removed from the cloud storage location referenced by the ex-ternal table, and File2 is added, the stream returns records for the rows in File2 only. Unlike when tracking CDC data for standard tables, Snowflake cannot access the historical records for files in cloud storage.


質問 # 49
Which query will show a list of the 20 most recent executions of a specified task kttask, that have been scheduled within the last hour that have ended or are stillrunning's.

  • A.
  • B.
  • C.
  • D.

正解：C


質問 # 50
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. Materialized view on top of External tables is not supported feature.
  • B. 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.
  • C. Alex is doing everything correct.
  • D. A materialized view, DataReportMV does not support Join operations, so Step 2 would be failed & he cannot proceed further.
  • E. There is no command like suspend for temporarily suspension of Materialized views, Step 3 will give error like invalid Suspend command.

正解：C

解説：
Explanation
All Steps will be executed successfully by Alex without any error.


質問 # 51
Which of the following security and governance tools/technologies are known to provide native connectivity to Snowflake? [Select 2]

  • A. Baffle
  • B. Dataiku
  • C. ALTR
  • D. BIG Squid
  • E. Zepl

正解：A、C

解説：
Explanation
Security and governance tools ensure sensitive data maintained by an organization is protected from inappropriate access and tampering, as well as helping organizations to achieve and maintain regula-tory compliance. These tools are often used in conjunction with observability solutions/services to provide organizations with visibility into the status, quality, and integrity of their data, including identifying potential issues.
Together, these tools support a wide range of operations, including risk assessment, intrusion detec-tion/monitoring/notification, data masking, data cataloging, data health/quality checks, issue identi-fication/troubleshooting/resolution, and more.
ALTR & Baffle are correct options here.


質問 # 52
Which are the two ways to access elements in a JSON object?

  • A. Use dot notation to traverse a path in a JSON object:
    <col-umn>:<level1_element>.<level2_element>.<level3_element>.
  • B. use bracket notation to traverse the path in an object:
    <col-umn>['<level1_element>']['<level2_element>'].
  • C. use Curly bracket notation to traverse the path in an object:
    <col-umn>{'<level1_element>'}{'<level2_element>'}.
  • D. Use SemiColon notation to traverse a path in a JSON object:
    <col-umn>:<level1_element>;<level2_element>;<level3_element>.

正解：A、B


質問 # 53
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 Designer
  • B. Query Optimizer
  • C. Query Profile
  • D. Performance Metadata table

正解：C

解説：
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.


質問 # 54
Michael, a Data Engineer Running a Data query to achieve Union of Data sets coming from Multi-ple data sources, later he figured out that Data processing query is taking more time than expected. He started analyzing the Query performance using query profile interface. He discovered & realized that he used UNION when the UNION ALL semantics was sufficient.
Which Extra Data Processing Operator Michael figured out while doing query profile analysis in this case which helps him to identify this performance bottlenecks?

  • A. Join
  • B. Filter
  • C. Flatten
  • D. UNION ALL
  • E. Aggregate

正解：E

解説：
Explanation
In SQL, it is possible to combine two sets of data with either UNION or UNION ALL constructs. The difference between them is that UNION ALL simply concatenates inputs, while UNION does the same, but also performs duplicate elimination.
A common mistake is to use UNION when the UNION ALL semantics are sufficient. These que-ries show in Query Profile as a UnionAll operator with an extra Aggregate operator on top (which performs duplicate elimination).
To Know more about Data Processing Operators, please do refer:
https://docs.snowflake.com/en/user-guide/ui-query-profile#operator-types


質問 # 55
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. Time Travel Statement query time is beyond the retention time of few current child (e.g., a table) of the Database entity.
  • B. SQL Compilation error: "Incorrect Syntax 'before' while creating database"
  • C. Time Travel Statement query time is at or before the point in time when the object was created.
  • D. CREATE DATABASE query fails due to compilation error as it do not support state-ment keyword.

正解：A、C


質問 # 56
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 EXTERNAL FUNCTIONS LIKE 'REVENUE_BY_REGION'IB SCHEMA SALES;
  • B. SELECT IS_SECURE FROM SNOWFLAKE. INFCRXATION_SCKZMA. FUNCTIONS WHERE FUNCTI0N_3CHEMA = 'SALES' AND FUNCTI CN_NAXE = *ftEVEXUE_BY_RKXQH4;
  • C. SHOW SECURE FUNCTIONS LIKE 'REVENUE 3Y REGION' IN SCHEMA SALES;
  • D. SELECT IS_SEC"JRE FROM INFOR>LVTICN_SCHEMA. FUNCTIONS WHERE
    FUNCTION_SCHEMA = 'SALES1 AND FUNGTZON_NAME = ' REVENUE_BY_REGION';
  • E. SHOW DS2R FUNCTIONS LIKE 'REVEX'^BYJIESION' IN SCHEMA SALES;

正解：B、E

解説：
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.


質問 # 57
Data Engineer try to load data from external stage using Snowpipe & later find out that some Set of Files Not Loaded. To debug the issue, she used COPY_HISTORY function & cross verified that its output indicates a subset of files was not loaded. What is possible reason of arising this situation in both REST API call and Auto-Ingest methods? [Select 2]

  • A. Files modified and staged again after 14 days and Snowpipe ignores modified files that are staged again.
  • B. External event-driven functionality is used to call the REST APIs, and a backlog of da-ta files already existed in the external stage before the events were configured.
  • C. An event notification failure prevented a set of files from getting queued.
  • D. A backlog of data files already existed in the external stage do not have any impact on Load failure, as this is well managed by serverless SnowPipe

正解：B、C

解説：
Explanation
COPY_HISTORY Record Indicates Unloaded Subset of Files:
If the COPY_HISTORY function output indicates a subset of files was not loaded, you may try to "refresh" the pipe.
This situation can arise in any of the following situations:
The external stage was previously used to bulk load data using the COPY INTO table command.
REST API:
o External event-driven functionality is used to call the REST APIs, and a backlog of data files al-ready existed in the external stage before the events were configured.
Auto-ingest:
o A backlog of data files already existed in the external stage before event notifications were con-figured.
o An event notification failure prevented a set of files from getting queued.
To load the data files in your external stage using the configured pipe, execute an ALTER PIPE ... REFRESH statement.


質問 # 58
......

DEA-C01試験問題集パスできる2024年最新の認証された試験問題：https://www.passtest.jp/Snowflake/DEA-C01-shiken.html

DEA-C01試験問題リアルな最新問題PDF：https://drive.google.com/open?id=1dp3xvcP1c3WU5P5MRC3-4WCb0LRNaEFR

関するブログ

もっと
DEA-C01 無料問題集