2023年最新の実際に出るDEA-C01問題集には試験のコツがあるPDF試験材料 [Q37-Q60]

2023年最新の実際に出るDEA-C01問題集には試験のコツがあるPDF試験材料

心強いDEA-C01PDF問題集はDEA-C01問題

質問 # 37
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. GCP
• C. AZURE
• D. AWS

正解：D

解説：
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.

質問 # 38
What are Invalid rules applicable when using stored procedure contains transaction?

• A. You cannot start a transaction inside the stored procedure, then complete the transac-tion after returning from the procedure.
• B. A transaction can be inside a stored procedure, or a stored procedure can be inside a transaction.
• C. You cannot start a transaction before calling the stored procedure, then complete the transaction inside the stored procedure.
• D. All Rules are Applicable.
• E. If a transaction is started inside a stored procedure and is still active when the stored procedure finishes, then an error occurs, and the transaction is rolled back.
• F. A transaction inside a stored procedure can include a call to another stored procedure that contains a transaction.

正解：D

質問 # 39
A Data Engineer is evaluating the performance of a query in a development environment.

Based on the Query Profile what are some performance tuning options the Engineer can use? (Select TWO)

• A. Move the query to a larger virtual warehouse
• B. Create indexes to ensure sorted access to data
• C. Increase the max cluster count
• D. Add a LIMIT to the ORDER BY If possible
• E. Use a multi-cluster virtual warehouse with the scaling policy set to standard

正解：A、D

解説：
Explanation
The performance tuning options that the Engineer can use based on the Query Profile are:
Add a LIMIT to the ORDER BY If possible: This option will improve performance by reducing the amount of data that needs to be sorted and returned by the query. The ORDER BY clause requires sorting all rows in the input before returning them, which can be expensive and time-consuming. By adding a LIMIT clause, the query can return only a subset of rows that satisfy the order criteria, which can reduce sorting time and network transfer time.
Create indexes to ensure sorted access to data: This option will improve performance by reducing the amount of data that needs to be scanned and filtered by the query. The query contains several predicates on different columns, such as o_orderdate, o_orderpriority, l_shipmode, etc. By creating indexes on these columns, the query can leverage sorted access to data and prune unnecessary micro-partitions or rows that do not match the predicates. This can reduce IO time and processing time.
The other options are not optimal because:
Use a multi-cluster virtual warehouse with the scaling policy set to standard: This option will not improve performance, as the query is already using a multi-cluster virtual warehouse with the scaling policy set to standard. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on the load. Changing the warehouse size or the number of clusters will not affect the performance of this query, as it is already using the optimal resources.
Increase the max cluster count: This option will not improve performance, as the query is not limited by the max cluster count. The max cluster count is a parameter that specifies the maximum number of clusters that a multi-cluster virtual warehouse can scale up to. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on theload. The default max cluster count for a 2XL warehouse is 10, which means that the warehouse can scale up to 10 clusters if needed. However, the query does not need more than 4 clusters, as it is not CPU-bound or memory-bound. Increasing the max cluster count will not affect the performance of this query, as it will not use more clusters than necessary.

質問 # 40
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. Automated Snowpipe uses event notifications to determine when new files arrive in monitored cloud storage and are ready to load.
• C. 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.
• D. Triggering automated Snowpipe data loads using S3 event messages is supported by Snowflake accounts hosted on Cloud Platform like AWS, GCP or AZURE.

正解：D

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 # 41
Data Engineer wants to analyze query performance & looking out for profiling information, He went to Query/Operator Details also called Profile Overview of Query Profile Interface & searching for statistics attributes around I/O. Which of the following information he can't get from there?

• A. Bytes sent over the wireframe - amount of data sent over the wireframe
• B. Bytes written - bytes written (e.g. when loading into a table).
• C. Bytes read from result - bytes read from the result object.
• D. External bytes scanned - bytes read from an external object, e.g. a stage.
• E. Percentage scanned from cache - the percentage of data scanned from the local disk cache.

正解：A

解説：
Explanation
To help you analyze query performance, Query/Operator Details panel also called Profile overview panel provides two classes of profiling information:
Execution time, broken down into categories
Detailed statistics
Apart from Option a Bytes sent over the wireframe - amount of data sent over the wireframe , Rest of the Statistics Information provided by Query/Operator details in the Query Profile Inter-face.
To Know More about the Query/Operator Details options , please refer the link:
https://docs.snowflake.com/en/user-guide/ui-query-profile#query-operator-details

質問 # 42
Which property can be used with ALTER USER command to temporarily disable MFA for the user so that they can log in?

• A. MINS_TO_SKIP_MFA
• B. HOURS_TO_BYPASS_MFA
• C. SECS_TO_BYPASS_MFA
• D. MINS_TO_BYPASS_MFA

正解：D

解説：
Explanation
You can use the following properties for the ALTER USER command to perform these tasks:
MINS_TO_BYPASS_MFA
Specifies the number of minutes to temporarily disable MFA for the user so that they can log in. Af-ter the time passes, MFA is enforced and the user cannot log in without the temporary token gener-ated by the Duo Mobile application.

質問 # 43
What kind of Snowflake integration is required when defining an external function in Snowflake?

• A. HTTP integration
• B. Notification integration
• C. Security integration
• D. API integration

正解：D

解説：
Explanation
An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.

質問 # 44
A Data Engineer defines the following masking policy:

....
must be applied to the full_name column in the customer table:

Which query will apply the masking policy on the full_name column?

• A. ALTER TABLE customer MODIFY COLUMN first_name ADD MASKING POLICY name_policy,
• B. ALTER TABLE customer MODIFY COLUMN full_nam ADD MASKING POLICY name_poiicy;
• C. ALTER TABLE customer MODIFY COLUMN first_nane SET MASKING POLICY name_policy; lasT_name SET MASKING POLICY name_pclicy;
• D. ALTER TABLE customer MODIFY COLUMN full_name Set MASKING POLICY name_policy;

正解：D

解説：
Explanation
The query that will apply the masking policy on the full_name column is ALTER TABLE customer MODIFY COLUMN full_name SET MASKING POLICY name_policy;. This query will modify the full_name column and associate it with the name_policy masking policy, which will mask the first and last names of the customers with asterisks. The other options are incorrect because they do not follow the correct syntax for applying a masking policy on a column. Option B is incorrect because it uses ADD instead of SET, which is not a valid keyword for modifying a column. Option C is incorrect because it tries to apply the masking policy on two columns, first_name and last_name, which are not part of the table structure. Option D is incorrect because it uses commas instead of dots to separate the database, schema, and table names

質問 # 45
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. IV
• C. I
• D. II

正解：D

解説：
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')

質問 # 46
Robert, A Data Engineer, found that Pipe become stale as it was paused for longer than the limited retention period for event messages received for the pipe (14 days by default) & also the previous pipe owner transfers the ownership of this pipe to Robert role while the pipe was paused. How Robert in this case, Resume this stale pipe?

• A. He can apply System function SYSTEM$PIPE_STALE_RESUME with ALTER PIPE statement.
• B. ALTER PIPES ... RESUME statement will resume the pipe.
• C. Robert can use SYSTEM$PIPE_FORCE_RESUME function to resume this stale pipe.
• D. PIPE needs to recreate in this scenario, as pipe already past 14 days of period & stale.
• E. select sys-tem$pipe_force_resume('mydb.myschema.stalepipe','staleness_check_override, ownership_transfer_check_override');

正解：E

解説：
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.stalepipe1','staleness_check_override'); While the stale pipe was paused, if ownership of the pipe was transferred to another role, then re-suming the pipe requires the additional OWNERSHIP_TRANSFER_CHECK_OVERRIDE argu-ment. For example, resume the stale stalepipe2 pipe in the mydb.myschema database and schema, which transferred to a new role:
SELECT SYS-TEM$PIPE_FORCE_RESUME('mydb.myschema.stalepipe1','staleness_check_override, own-ership_transfer_check_override');

質問 # 47
Which is the non-supportable JavaScript UDF data types?

• A. String
• B. Double
• C. Binary
• D. Integers

正解：D

質問 # 48
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. Inner Transaction
• B. Atomic Transaction
• C. Nested Scope Transaction
• D. Scoped transactions
• E. Enclosed Transaction

正解：D

質問 # 49
Which use case would be BEST suited for the search optimization service?

• A. Data Engineers who create clustered tables with frequent reads against clustering keys
• B. Business users who need fast response times using highly selective filters
• C. Analysts who need to perform aggregates over high cardinality columns
• D. Data Scientists who seek specific JOIN statements with large volumes of data

正解：B

解説：
Explanation
The use case that would be best suited for the search optimization service is business users who need fast response times using highly selective filters. The search optimization service is a feature that enables faster queries on tables with high cardinality columns by creating inverted indexes on those columns. High cardinality columns are columns that have a large number of distinct values, such as customer IDs, product SKUs, or email addresses. Queries that use highly selective filters on high cardinality columns can benefit from the search optimization service because they can quickly locate the relevant rows without scanning the entire table. The other options are not best suited for the search optimization service. Option A is incorrect because analysts who need to perform aggregates over high cardinality columns will not benefit from the search optimization service, as they will still need to scan all the rows that match the filter criteria. Option C is incorrect because data scientists who seek specific JOIN statements with large volumes of data will not benefit from the search optimization service, as they will still need to perform join operations that may involve shuffling or sorting data across nodes. Option D is incorrect because data engineers who create clustered tables with frequent reads against clustering keys will not benefit from the search optimization service, as they already have an efficient way to organize and access data based on clustering keys.

質問 # 50
Tasks may optionally use table streams to provide a convenient way to continuously process new or changed data. A task can transform new or changed rows that a stream surfaces. Each time a task is scheduled to run, it can verify whether a stream contains change data for a table and either consume the change data or skip the current run if no change data exists. Which System Function can be used by Data engineer to verify whether a stream contains changed data for a table?

• A. SYSTEM$STREAM_DELTA_DATA
• B. SYSTEM$STREAM_HAS_DATA
• C. SYSTEM$STREAM_CDC_DATA
• D. SYSTEM$STREAM_HAS_CHANGE_DATA

正解：B

解説：
Explanation
SYSTEM$STREAM_HAS_DATA
Indicates whether a specified stream contains change data capture (CDC) records.

質問 # 51
Bob, a Lead Data Engineer is looking out to get the function definition & queried below statement to check if this function is secure enough to use in his script or not.
select is_secure from information_schema.functions where function_name = 'JOHNFUNCTION'; From the query output he is sure that, Function is secure UDF, what are the way provided by snow-flake to get the function definition of secure UDF?

• A. Declaring a UDF as "secure" hide the definition from Bob & all the required Definition commands will throw error.
• B. He can get the secure UDF definition using GET_DDL utility function.
• C. UDF definition or text, is visible to users via Query Profile (in the web interface).
• D. SHOW FUNCTIONS Commands

正解：A

質問 # 52
A table is loaded using Snowpipe and truncated afterwards Later, a Data Engineer finds that the table needs to be reloaded but the metadata of the pipe will not allow the same files to be loaded again.
How can this issue be solved using the LEAST amount of operational overhead?

• A. Wait until the metadata expires and then reload the file using Snowpipe
• B. Recreate the pipe by using the create or replace pipe command
• C. Modify the file by adding a blank row to the bottom and re-stage the file
• D. Set the FORCE=TRUE option in the Snowpipe COPY INTO command

正解：D

解説：
Explanation
The FORCE=TRUE option in the Snowpipe COPY INTO command allows Snowpipe to load files that have already been loaded before, regardless of the metadata. This is the easiest way to reload the same files without modifying them or recreating the pipe.

質問 # 53
If the data retention period for a table is less than 90 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale?

• A. FALSE
• B. TRUE

正解：A

解説：
Explanation
If the data retention period for a table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale. The period is extended to the stream's offset, up to a maximum of 14 days by default, regardless of the Snowflake edition for your account. The maximum number of days for which Snowflake can extend the data retention period is determined by the MAX_DATA_EXTENSION_TIME_IN_DAYS parameter value. When the stream is consumed, the extended data retention period is reduced to the default period for the table.

質問 # 54
Which Role inherits the privileges of the USERADMIN role via the system role hierarchy?

• A. CUSTOM ROLE
• B. SECURITYADMIN
• C. SYSADMIN
• D. PUBLIC

正解：B

質問 # 55
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 customer table
• B. The SYSADMIN role
• C. The SECURITYADMIN role
• D. The role that owns the schema
• E. The role that owns the database
• F. The USERADMIN role with the manage grants privilege

正解：C、D、E

解説：
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.

質問 # 56
A SQL UDF evaluates an arbitrary SQL expression and returns the result(s) of the expression. Which value type it can returns?

• A. A Set of Rows
• B. Regex
• C. Scaler or Tabular depend on input SQL expression
• D. Single Value

正解：C

質問 # 57
Melissa, Senior Data Engineer, looking out to optimize query performance for one of the Critical Control Dashboard, she found that most of the searches by the users on the control dashboards are based on Equality search on all the underlying columns mostly. Which Best techniques she should consider here?

• A. She can go for clustering on underlying tables which can speedup Equality searches.
• B. The search optimization service would best fit here as it can be applied to all underlying columns & speeds up equality searches.
  (Correct)
• C. Melissa can create Indexes & Hints on the searchable columns to speed up Equality search.
• D. A materialized view speeds both equality searches and range searches.

正解：B

解説：
Explanation
Clustering a table can speed any of the following, as long as they are on the clustering key:
Range searches.
Equality searches.
However, a table can be clustered on only a single key (which can contain one or more columns or expressions).
The search optimization service speeds equality searches. However, this applies to all the columns of supported types in a table that has search optimization enabled. This is what required here& best fit for purpose.
A materialized view speeds both equality searches and range searches, as well as some sort opera-tions, but only for the subset of rows and columns included in the materialized view.

質問 # 58
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. Python packages can only be loaded in a local environment
• B. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
• C. Third-party supported Python packages are locked down to prevent hitting
• D. Python packages can access any external endpoints
• E. Querying information__schema .packages will provide a list of supported Python packages and versions

正解：B、D、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 # 59
How can the following relational data be transformed into semi-structured data using the LEAST amount of operational overhead?

• A. Use the OBJECT_CONSTRUCT function to return a Snowflake object
• B. Use the TO_VARIANT function to convert each of the relational columns to VARIANT.
• C. Use the to_json function
• D. Use the PAESE_JSON function to produce a variant value

正解：A

解説：
Explanation
This option is the best way to transform relational data into semi-structured data using the least amount of operational overhead. The OBJECT_CONSTRUCT function takes a variable number of key-value pairs as arguments and returns a Snowflake object, which is a variant type that can store JSON data. The function can be used to convert each row of relational data into a JSON object with the column names as keys and the column values as values.

質問 # 60
......

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