Free Snowflake DEA-C01 Test Practice Test Questions Exam Dumps
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NEW QUESTION # 10
Ryan, a Data Engineer, accidently drop the Share named SF_SHARE which results in immediate access revoke for all the consumers (i.e., accounts who have created a database from that SF_SHARE). What action he can take to recover the dropped Share?
- A. Consumer accounts that have created databases from the share will still be able to que-ry these databases as Share is separate securable object & it's still accessible using time travel feature.
- B. A dropped share cannot be restored. The share must be created again using the CRE-ATE SHARE command and then configured using GRANT <privilege> ... TO SHARE and ALTER SHARE.
- C. He can recreate a share with the same name as a previous share which does restore the databases created (by any consumers) from the share SF_SHARE.
- D. By Executing UNDROP command he could possibly recover the dropped Share SF_SHARE & its associated Databases for immediate consumer access.
Answer: B
Explanation:
Explanation
You can drop a share at any time using the DROP SHARE command.
Dropping a share instantly invalidates all databases created from the share by consumer accounts.
All queries and other operations performed on these databases will no longer work.
After dropping a share, you can recreate it with the same name;
however, this does not restore any of the databases created from the share by consumer accounts.
The recreated share is treated as a new share and all consumer accounts must create a new database from the new share.
NEW QUESTION # 11
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. CREATE OR REPLACE PIPE command will recreate the PIPE successfully.
- B. 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).
- C. Terminate the existing pipe (using ALTER PIPE ... SET PIPE_EXECUTION_TERMINATE = true) before recreation.
- D. Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).
Answer: B
Explanation:
Explanation
Recreating a pipe (using a CREATE OR REPLACE PIPE statement) is necessary to modify most pipe properties.
Recreating Pipes for Automated Data Loads
When recreating a pipe that automates data loads using event notifications, it's recommended that Data Engineer complete the following steps:
1. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true).
2. Query the SYSTEM$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 # 12
Data Engineer is looking out to delete staged files automatically/periodically when the data is suc-cessfully loaded into tables by the Snowpipe. For achieving the same, which options/command is best suited: [Select 2]
- A. To remove staged files that no longer needed, periodically DELETE command can be executed to delete the files.
- B. To remove staged files that no longer needed, periodically REMOVE command can be executed to delete the files.
- C. REMOVE_STAGE_FILES option can be set as True in the COPY INTO Command embedded in PIPE objects definition.
- D. PURGE option can be set as True in the COPY INTO Command embedded in PIPE objects definition.
Answer: B,D
Explanation:
Explanation
Deleting Staged Files After Snowpipe Loads the Data
Pipe objects do not support the PURGE copy option. Snowpipe cannot delete staged files automat-ically when the data is successfully loaded into tables.
To remove staged files that you no longer need, It is recommended to periodically executing the REMOVE command to delete the files.
Alternatively, configure any lifecycle management features provided by cloud storage service pro-vider.
NEW QUESTION # 13
Select the correct usage statements with regards to SQL UDF?
- A. You can include only one query expression.
- B. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
- C. All of above are correct.
- D. Scalar functions (UDFs) have a limit of 500 input arguments.
- E. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
Answer: C
NEW QUESTION # 14
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. Increase the number of maximum clusters of the virtual warehouse
- C. Define a clustering key for the sales data table
- D. Increase the size of the virtual warehouse
Answer: A
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 # 15
Can Masking policies be applied to virtual columns?
- A. FALSE
- B. TRUE
Answer: A
NEW QUESTION # 16
Select the Correct statements with regard to using Federated authentication/SSO?
- A. Snowflake supports SSO with Private Connectivity to the Snowflake Service for Snow-flake accounts on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.
- B. Snowflake supports using MFA in conjunction with SSO to provide additional levels of security.
- C. Snowflake supports multiple audience values (i.e. Audience or Audience Restriction Fields) in the SAML 2.0 assertion from the identity provider to Snowflake.
- D. Snowflake supports using SSO with organizations, and you can use the corresponding URL in the SAML2 security integration.
Answer: A,B,C,D
NEW QUESTION # 17
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?
- A. SUCCESS
- B. LOADED
- C. LOAD_SUCCESS
- D. LOADED_SUCCESS
Answer: C
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 # 18
What is a characteristic of the operations of streams in Snowflake?
- A. Each committed and uncommitted transaction on the source table automatically puts a change record in the stream.
- B. Whenever a stream is queried, the offset is automatically advanced.
- C. When a stream is used to update a target table the offset is advanced to the current time.
- D. Querying a stream returns all change records and table rows from the current offset to the current time.
Answer: D
Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream has an offset, which is a point in time that marks the beginning of the change records to be returned by the stream. Querying a stream returns all change records and table rows from the current offset to the current time. The offset is not automatically advanced by querying the stream, but it can be manually advanced by using the ALTER STREAM command. When a stream is used to update a target table, the offset is advanced to the current time only if the ON UPDATE clause is specified in the stream definition. Each committed transaction on the source table automatically puts a change record in the stream, but uncommitted transactions do not.
NEW QUESTION # 19
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. Increase the size of the virtual warehouse used by Snowpipe.
- C. Split the files before loading them andset the SIZE_LIMIT option to 250 MB.
- D. Change the file compression size and increase the frequency of the Snowpipe loads
Answer: C
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 # 20
Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?
- A. Spark Connector
- B. Node.js connector
- C. Python Connector
- D. Kafka Connector
Answer: D
Explanation:
Explanation
Apache Kafka software uses a publish and subscribe model to write and read streams of records, similar to a message queue or enterprise messaging system. Kafka allows processes to read and write messages asynchronously. A subscriber does not need to be connected directly to a publisher; a pub-lisher can queue a message in Kafka for the subscriber to receive later.
An application publishes messages to a topic, and an application subscribes to a topic to receive those messages. Kafka can process, as well as transmit, messages; however, that is outside the scope of this document. Topics can be divided into partitions to increase scalability.
Kafka Connect is a framework for connecting Kafka with external systems, including databases. A Kafka Connect cluster is a separate cluster from the Kafka cluster. The Kafka Connect cluster sup-ports running and scaling out connectors (components that support reading and/or writing between external systems).
The Kafka connector is designed to run in a Kafka Connect cluster to read data from Kafka topics and write the data into Snowflake tables.
Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns:
RECORD_CONTENT. This contains the Kafka message.
RECORD_METADATA. This contains metadata about the message, for example, the topic from which the message was read.
NEW QUESTION # 21
As a Data Engineer, you have requirement to query most recent data from the Large Dataset that reside in the external cloud storage, how would you design your data pipelines keeping in mind fastest time to delivery?
- A. Unload data into SnowFlake Internal data storage using PUT command.
- B. Direct Querying External tables on top of existing data stored in external cloud storage for analysis without first loading it into Snowflake.
- C. Data pipelines would be created to first load data into internal stages & then into Per-manent table with SCD Type 2 transformation.
- D. External tables with Materialized views can be created in Snowflake.
- E. Snowpipe can be leveraged with streams to load data in micro batch fashion with CDC streams that capture most recent data only.
Answer: D
Explanation:
Explanation
In a typical table, the data is stored in the database; however, in an external table, the data is stored in files in an external stage. External tables store file-level metadata about the data files, such as the filename, a version identifier and related properties. This enables querying data stored in files in an external stage as if it were inside a database. External tables can access data stored in any format supported by COPY INTO <table> statements.
External tables are read-only, therefore no DML operations can be performed on them; however, external tables can be used for query and join operations. Views can be created against external ta-bles.
Querying data stored external to the database is likely to be slower than querying native database tables; however, materialized views based on external tables can improve query performance.
Creating External tables enable user for querying existing data stored in external cloud storage for analysis without first loading it into Snowflake. The source of truth for the data remains in the ex-ternal cloud storage.
Data sets materialized in Snowflake via materialized views are read-only.
This solution is especially beneficial to accounts that have a large amount of data stored in external cloud storage and only want to query a portion of the data; for example, the most recent data. Users can create materialized views on subsets of this data for improved query performance.
NEW QUESTION # 22
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. 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.
- B. Delete the offset using STREAM properties SYSTEM$RESET_OFFSET( <stream_id> )
- 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. using the CREATE OR REPLACE STREAM syntax, Recreate the STREAM
Answer: C,D
Explanation:
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).
NEW QUESTION # 23
Which of the following statements is/are incorrect regarding Fail-safe data recovery?
- A. Data stored in temporary tables is not recoverable after the table is dropped as they do not have 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. 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.
- E. Short-lived tables (i.e. <1 day), such as ETL work tables, can be defined as transient to eliminate Fail-safe costs.
Answer: D
NEW QUESTION # 24
Which UDF programming language is not supported with Snowflake Secure Data Sharing feature?
- A. JAVASCRIPT
- B. SQL
- C. PYTHON
- D. JAVA
Answer: A
NEW QUESTION # 25
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 # 26
A Data Engineer needs to ingest invoice data in PDF format into Snowflake so that the data can be queried and used in a forecasting solution.
..... recommended way to ingest this data?
- A. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
- B. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
- C. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
- D. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data
Answer: D
Explanation:
Explanation
The recommended way to ingest invoice data in PDF format into Snowflake is to create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data. This option allows for more flexibility and control over how the PDF data is extracted and transformed. The other options are not suitable for ingesting PDF data into Snowflake. Option A and B are incorrect because Snowpipe and COPY INTO commands can only ingest files that are in supported file formats, such as CSV, JSON, XML, etc. PDF files are not supported by Snowflake and will cause errors or unexpected results.
Option C is incorrect because external tables can only query files that are in supported file formats as well.
PDF files cannot be parsed by external tables and will cause errors or unexpected results.
NEW QUESTION # 27
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?
- A. You require the warehouse to be available with no delay or lag time.
- B. You have a low, fluctuating workload for the warehouse.
- C. You require the warehouse to be available with delay.
- D. You have a heavy, steady workload for the warehouse.
Answer: A,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 # 28
A Data Engineer is writing a Python script using the Snowflake Connector for Python. The Engineer will use the snowflake. Connector.connect function to connect to Snowflake The requirementsare:
*Raise an exception if the specified database schema or warehouse does not exist
*improve download performance
Whichparameters of the connect function should be used? (Select TWO).
- A. validate_default_parameters
- B. authenticator
- C. arrow_nunber_to_decimal
- D. client_session_keep_alivs
- E. client_prefetch_threads
Answer: A,E
Explanation:
Explanation
The parameters of the connect function that should be used are client_prefetch_threads and validate_default_parameters. The client_prefetch_threads parameter controls the number of threads used to download query results from Snowflake. Increasing this parameter can improve download performance by parallelizing the download process. The validate_default_parameters parameter controls whether an exception should be raised if the specified database, schema, or warehouse does not exist or is not authorized. Setting this parameter to True can help catch errors early and avoid unexpected results.
NEW QUESTION # 29
For the most efficient and cost-effective Data load experience, Data Engineer needs to inconsider-ate which of the following considerations?
- A. Enabling the STRIP_OUTER_ARRAY file format option for the COPY INTO <ta-ble> command to remove the outer array structure and load the records into separate table rows.
- B. When preparing your delimited text (CSV) files for loading, the number of columns in each row should be consistent.
- C. if the "null" values in your files indicate missing values and have no other special mean-ing, Snowflake recommend setting the file format option STRIP_NULL_VALUES to TRUE when loading the semi-structured data file.
- D. Split larger files into a greater number of smaller files, maximize the processing over-head for each file.
(Correct) - E. Amazon Kinesis Firehose can be convenient way to aggregate and batch data files which also allows defining both the desired file size, called the buffer size, and the wait interval after which a new file is sent, called the buffer interval.
Answer: D
Explanation:
Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.
NEW QUESTION # 30
A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.
Which conditions must be met? (Select THREE).
- A. The query must be executed using the same virtual warehouse.
- B. The USED_CACHED_RESULT parameter must be included in the query.
- C. The new query must have the same syntax as the previously executed query.
- D. The micro-partitions cannot have changed due to changes to other data in the table
- E. The table structure contributing to the query result cannot have changed
- F. The results must be reused within 72 hours.
Answer: C,E,F
Explanation:
Explanation
Snowflake's query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:
The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.
NEW QUESTION # 31
Snowpipe loads data from files as soon as they are available in a stage. Automated data loads lever-age event notifications for cloud storage to inform Snowpipe of the arrival of new data files to load. Which Cloud hosted platform provides cross cloud support for automated data loading via Snow-pipe?
- A. None of the Above currently provide cross cloud support for Snowpipe.
- B. AZURE
- C. AWS
- D. GCP
Answer: C
Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.
NEW QUESTION # 32
While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. gen-erated by UUID_STRING) instead of sequence-generated values?
- A. FALSE
- B. TRUE
(Correct)
Answer: B
NEW QUESTION # 33
Data Engineer identified use case where he decided to use materialized view for query perfor-mance. Which one is not the limitation he must be aware of before using MVs in their use case?
- A. A materialized views does not support clustering.
- B. You cannot directly clone a materialized view by using the CREATE MATERIAL-IZED VIEW ...
CLONE... command. - C. Truncating a materialized view is not supported.
- D. A materialized view cannot include HAVING clauses OR ORDER BY clause.
- E. Context Functions like CURRENT_TIME or CURRENT_TIMESTAMP is not per-mitted.
- F. A materialized views cannot be created on Shared Data.
- G. A materialized view can query only a single table & Joins, including self-joins, are not supported.
- H. Time Travel is not currently supported on materialized views.
Answer: A,D
Explanation:
Explanation
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
If you cluster both the materialized view(s) and the base table on which the materialized view(s) are defined, you can cluster the materialized view(s) on different columns from the columns used to cluster the base table.
You can create a materialized view on shared data.
Also You can use Snowflake's data sharing feature to share a materialized view.
Rest all are correct.
NEW QUESTION # 34
Partition columns optimize query performance by pruning out the data files that do not need to be scanned (i.e.
partitioning the external table). Which pseudocolumn of External table evaluate as an expression that parses the path and/or filename information.
- A. METADATA$COLUMNNAME
- B. METADATA$FILENAME
- C. METADATA$ROW_NUMBER
- D. METADATA$FILEPATH
Answer: B
Explanation:
Explanation
METADATA$FILENAME
A pseudocolumn that identifies the name of each staged data file included in the external table, in-cluding its path in the stage.
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn. A partition consists of all data files that match the path and/or filename in the expression for the parti-tion column.
NEW QUESTION # 35
......
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