DEA-C01更新された試験問題集で[2024年最新] 練習有効な試験問題集
DEA-C01サンプルには正確で更新された問題
質問 # 21
A Data Engineer needs to load JSON output from some software into Snowflake using Snowpipe.
Which recommendations apply to this scenario? (Select THREE)
- A. Load a single huge array containing multiple records into a single table row
- B. Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute
- C. Extract semi-structured data elements containing null values into relational columns before loading
- D. Verify each value of each unique element stores a single native data type (string or number)
- E. Load large files (1 GB or larger)
- F. Ensure that data files are 100-250 MB (or larger) in size compressed
正解：B、D、F
解説：
Explanation
The recommendations that apply to this scenario are:
Ensure that data files are 100-250 MB (or larger) in size compressed: This recommendation will improve Snowpipe performance by reducing the number of files that need to be loaded and increasing the parallelism of loading. Smallerfiles can cause performance degradation or errors due to excessive metadata operations or network latency.
Verify each value of each unique element stores a single native data type (string or number): This recommendation will improve Snowpipe performance by avoiding data type conversions or errors when loading JSON data into variant columns. Snowflake supports two native data types for JSON elements:
string and number. If an element has mixed data types across different files or records, such as string and boolean, Snowflake will either convert them to string or raise an error, depending on the FILE_FORMAT option.
Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute: This recommendation will minimize Snowpipe costs by reducing the number of notifications that need to be sent to Snowpipe for auto-ingestion. Snowpipe charges for notifications based on the number of files per notification and the frequency of notifications. By creating smaller files and staging them at a lower frequency, fewer notifications will be needed.
質問 # 22
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 micro-partitions cannot have changed due to changes to other data in the table
- B. The results must be reused within 72 hours.
- C. The USED_CACHED_RESULT parameter must be included in the query.
- D. The query must be executed using the same virtual warehouse.
- E. The new query must have the same syntax as the previously executed query.
- F. The table structure contributing to the query result cannot have changed
正解：B、E、F
解説：
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.
質問 # 23
Which of the below concepts/functions helps while implementing advanced Column-level Security?
- A. INVOKER_ROLE
- B. CURRENT_ROLE
- C. Role Hierarchy
- D. CURRENT_CLIENT
正解：A、B、C
解説：
Explanation
Column-level Security supports using Context Functions in the conditions of the masking policy body to enforce whether a user has authorization to see data. To determine whether a user can see data in a given SQL statement, it is helpful to consider:
Masking policy conditions using CURRENT_ROLE target the role in use for the current session.
Masking policy conditions using INVOKER_ROLE target the executing role in a SQL statement.
Role hierarchy
Determine if a specified role in a masking policy condition (e.g. ANALYST custom role) is a lower privilege role in the CURRENT_ROLE or INVOKER_ROLE role hierarchy. If so, then the role returned by the CURRENT_ROLE or INVOKER_ROLE functions inherits the privileges of the specified role.
質問 # 24
Ron, Snowflake Developer needs to capture change data (insert only) on the source views, for that he follows the below steps:
Enable change tracking on the source views & its underlying tables.
Inserted the data via Scripts scheduled with the help of Tasks.
then simply run the below Select statements.
1.select *
2.from test_table
3.changes(information => append_only)
4.at(timestamp => (select current_timestamp()));
Select the Correct Query Execution Output option below:
- A. Select query will fail with error: 'SQL compilation error-Incorrect Keyword "Chang-es()" found'
- B. Select statement complied but gives erroneous results.
- C. No Error reported, select command gives Changed records with Metadata columns as change tracking enabled on the Source views & its underlying tables.
- D. Developer missed to create stream on the source table which can further query to cap-ture DML records.
正解：C
解説：
Explanation
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.
To Know more about Snowflake CHANGES clause, please refer the mentioned link:
https://docs.snowflake.com/en/sql-reference/constructs/changes
質問 # 25
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?
- A. Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.
- B. Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.
- C. Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.
- D. Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.
正解：D
解説：
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.
質問 # 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 Snowpipe to ingest the files that land in an external stage into a Snowflake table
- B. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
- C. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data
- D. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
正解：C
解説：
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.
質問 # 27
By default, a newly-created Custom role is not assigned to any user, nor granted to any other role?
- A. TRUE
- B. FALSE
正解：A
質問 # 28
UDTFs also called a table function, returns zero, one, or multiple rows for each input row?
- A. NO
- B. YES
正解：B
解説：
Explanation
UDFs may be scalar or tabular.
A scalar function returns one output row for each input row. The returned row consists of a single column/value.
A tabular function, also called a table function, returns zero, one, or multiple rows for each input row. A tabular UDF is defined by specifying a return clause that contains the TABLE keyword and specifies the names and data types of the columns in the table results. Tabular UDFs are often called UDTFs (user-defined table functions) or table UDFs.
質問 # 29
A secure function returns data coming through an inbound share
What will happen if a Data Engineer tries to assign usage privileges on this function to an outbound share?
- A. The Engineer will be able to share the secure function with other accounts
- B. An error will be returned because only secure functions can be shared with inbound shares
- C. An error will be returned because the Engineer cannot share data that has already been shared
- D. An error will be returned because only views and secure stored procedures can be shared
正解：C
解説：
Explanation
An error will be returned because the Engineer cannot share data that has already been shared. A secure function is a Snowflake function that can access data from an inbound share, which is a share that is created by another account and consumed by the current account. A secure function can only be shared with an inbound share, not an outbound share, which is a share that is created by the current account and shared with other accounts. This is to prevent data leakage or unauthorized access to the data from the inbound share.
質問 # 30
External Function is a type of UDF & can be Scaler or Tabular?
- A. TRUE
- B. FALSE
正解：B
解説：
Explanation
External functions must be scalar functions. A scalar external function returns a single value for each input row.
質問 # 31
What kind of Snowflake integration is required when defining an external function in Snowflake?
- A. Security integration
- B. HTTP integration
- C. API integration
- D. Notification integration
正解：C
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 # 32
Data engineer designed the data pipelines using Snowpipe to load data files into Snowflake tables, what will happen in case few files with same name but modified data are queued for reloading?
- A. Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.
- B. eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.
- C. Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.
- D. Snowpipe uses file loading metadata associated with each pipe object to prevent reload-ing the same files (and duplicating data) in a table.
正解：D
解説：
Explanation
Snowflake uses file loading metadata to prevent reloading the same files (and duplicating data) in a table.
Snowpipe prevents loading files with the same name even if they were later modified (i.e. have a different eTag).
The file loading metadata is associated with the pipe object rather than the table. As a result:
Staged files with the same name as files that were already loaded are ignored, even if they have been modified, e.g. if new rows were added or errors in the file were corrected.
Truncating the table using the TRUNCATE TABLE command does not delete the Snowpipe file loading metadata.
質問 # 33
Streams cannot be created to query change data on which of the following objects? [Select All that Apply]
- A. Directory tables
- B. External tables
- C. Standard tables, including shared tables.
- D. Query Log Tables
- E. Views, including secure views
正解：D
解説：
Explanation
Streams supports all the listed objects except Query Log tables.
質問 # 34
Which are supported Programming Languages for Creating UDTFs?
- A. Perl
- B. Java
- C. Python
- D. Node.javascript
- E. Javascript
正解：B、C、E
質問 # 35
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 table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering
- B. 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.
- C. 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
- D. 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
正解：B
解説：
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.
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. Automated Snowpipe uses event notifications to determine when new files arrive in monitored cloud storage and are ready to load.
- 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. 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.
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
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