[Aug 05, 2024] 100% Real & Accurate DEA-C01 Questions with Free and Fast Updates [Q31-Q51]

[Aug 05, 2024] 100% Real & Accurate DEA-C01 Questions with Free and Fast Updates

Self-Study Guide for Becoming an SnowPro Advanced: Data Engineer Certification Exam Expert

NEW QUESTION # 31
Company A and Company B both have Snowflake accounts. Company A's account is hosted on a different cloud provider and region than Company B's account Companies A and B are not in the same Snowflake organization.
How can Company A share data with Company B? (Select TWO).

• A. Create a separate database within Company A's account to contain only those data sets they wish to share with Company B Create a share within Company A's account and add all the objects within this separate database to the share Add Company B's account as a recipient of the share
• B. Create a new account within Company A's organization in the same cloud provider and region as Company B's account Use database replication to replicate Company A's data to the new account Create a share within the new account and add Company B's account as a recipient of that share
• C. Create a share within Company A's account and add Company B's account as a recipient of that share
• D. Create a share within Company A's account, and create a reader account that is a recipient of the share Grant Company B access to the reader account
• E. Use database replication to replicate Company A's data into Company B's account Create a share within Company B's account and grant users within Company B's account access to the share

Answer: A,C

Explanation:
Explanation
The ways that Company A can share data with Company B are:
Create a share within Company A's account and add Company B's account as a recipient of that share:
This is a valid way to share data between different accounts on different cloud platforms and regions.
Snowflake supports cross-cloud and cross-region data sharing, which allows users to create shares and grant access to other accounts regardless of their cloud platform or region. However, this option may incur additional costs for network transfer and storage replication.
Create a separate database within Company A's account to contain only those data sets they wish to share with Company B Create a share within Company A'saccount and add all the objects within this separate database to the share Add Company B's account as a recipient of the share: This is also a valid way to share data between different accounts on different cloud platforms and regions. This option is similar to the previous one, except that it uses a separate database to isolate the data sets that need to be shared. This can improve security and manageability of the shared data. The other options are not valid because:
Create a share within Company A's account, and create a reader account that is a recipient of the share Grant Company B access to the reader account: This option is not valid because reader accounts are not supported for cross-cloud or cross-region data sharing. Reader accounts are Snowflake accounts that can only consume data from shares created by their provider account. Reader accounts must be on the same cloud platform and region as their provider account.
Use database replication to replicate Company A's data into Company B's account Create a share within Company B's account and grant users within Company B's account access to the share: This option is not valid because database replication cannot be used for cross-cloud or cross-region data sharing.
Database replication is a feature in Snowflake that allows users to copy databases across accounts within the same cloud platform and region. Database replication cannot copy databases across different cloud platforms or regions.
Create a new account within Company A's organization in the same cloud provider and region as Company B's account Use database replication to replicate Company A's data to the new account Create a share within the new account and add Company B's account as a recipient of that share: This option is not valid because it involves creating a new account within Company A's organization, which may not be feasible or desirable for Company A. Moreover, this option is unnecessary, as Company A can directly share data with Company B without creating an intermediate account.

NEW QUESTION # 32
Which Snowflake objects does the Snowflake Kafka connector use? (Select THREE).

• A. Serverless task
• B. Internal user stage
• C. Pipe
• D. Internal table stage
• E. Storage integration
• F. Internal named stage

Answer: C,D,F

Explanation:
Explanation
The Snowflake Kafka connector uses three Snowflake objects: pipe, internal table stage, and internal named stage. The pipe object is used to load data from an external stage into a Snowflake table using COPY statements. The internal table stage is used to store files that are loaded from Kafka topics into Snowflake using PUT commands. The internal named stage is used to store files that are rejected by the COPY statements due to errors or invalid data. The other options are not objects that are used by the Snowflake Kafka connector.
Option B, serverless task, is an object that can execute SQL statements on a schedule without requiring a warehouse. Option C, internal user stage, is an object that can store files for a specific user in Snowflake using PUT commands. Option F, storage integration, is an object that can enable secure access to external cloud storage services without exposing credentials.

NEW QUESTION # 33
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. Developer missed to create stream on the source table which can further query to cap-ture DML records.
• 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. Select query will fail with error: 'SQL compilation error-Incorrect Keyword "Chang-es()" found'

Answer: C

Explanation:
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

NEW QUESTION # 34
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. arrow_nunber_to_decimal
• B. client_prefetch_threads
• C. authenticator
• D. validate_default_parameters
• E. client_session_keep_alivs

Answer: B,D

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 # 35
A company has an extensive script in Scala that transforms data by leveraging DataFrames. A Data engineer needs to move these transformations to Snowpark.
...characteristics of data transformations in Snowpark should be considered to meet this requirement? (Select TWO)

• A. User-Defined Functions (UDFs) are not pushed down to Snowflake
• B. Snowpark operations are executed lazily on the server.
• C. Columns in different DataFrames with the same name should be referred to with squared brackets
• D. It is possible to join multiple tables using DataFrames.
• E. Snowpark requires a separate cluster outside of Snowflake for computations

Answer: B,D

Explanation:
Explanation
The characteristics of data transformations in Snowpark that should be considered to meet this requirement are:
It is possible to join multiple tables using DataFrames.
Snowpark operations are executed lazily on the server.
These characteristics indicate how Snowpark can perform data transformations using DataFrames, which are similar to the ones used in Scala. DataFrames are distributed collections of rows that can be manipulated using various operations, such as joins, filters, aggregations, etc. DataFrames can be created from different sources, such as tables, files, or SQL queries. Snowpark operations are executed lazily on the server, which means that they are not performed until an action is triggered, such as a write or a collect operation. This allows Snowpark to optimize the execution plan and reduce the amount of data transferred between the client and the server.
The other options are not characteristics of data transformations in Snowpark that should be considered to meet this requirement. Option C is incorrect because User-Defined Functions (UDFs) are pushed down to Snowflake and executed on the server. Option D is incorrect because Snowpark does not require a separate cluster outside of Snowflake for computations, but rather uses virtual warehouses within Snowflake. Option E is incorrect because columns in different DataFrames with the same name should be referred to with dot notation, not squared brackets.

NEW QUESTION # 36
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?

• A. SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)
• B. SYSTEM$CLUSTERING_INFORMATION (including clustering depth)
• C. SYSTEM$CLUSTERING_DEPTH_KEY
• D. SYSTEM$CLUSTERING_DEPTH

Answer: B,D

Explanation:
Explanation
SYSTEM$CLUSTERING_DEPTH:
Computes the average depth of the table according to the specified columns (or the clustering key defined for the table). The average depth of a populated table (i.e. a table containing data) is always 1 or more. The smaller the average depth, the better clustered the table is with regards to the speci-fied columns.
Calculate the clustering depth for a table using two columns in the table:
SELECT SYSTEM$CLUSTERING_DEPTH('TPCH_PRODUCT', '(C2, C9)');
SYSTEM$CLUSTERING_INFORMATION:
Returns clustering information, including average clustering depth, for a table based on one or more columns in the table.
SELECT SYSTEM$CLUSTERING_INFORMATION('SAMPLE_TABLE', '(col1, col3)');

NEW QUESTION # 37
A Data Engineer is building a set of reporting tables to analyze consumer requests by region for each of the Data Exchange offerings annually, as well as click-through rates for each listing Which views are needed MINIMALLY as data sources?

• A. SNOWFLAKE.DATA_SHARING_USAGE.LISTING_CONSOKE>TION_DAILY
• B. SNOWFLAKE. DATA_SHARING_USAGE. LISTING_TELEMETRY_DAILy
• C. SNOWFLAKE- DATA_SHARING_USAGE - LISTING_EVENTS_BAILY
• D. SNOWFLAKE.ACCOUNT_USAGE.DATA _TRANSFER_HISTORY

Answer: A

Explanation:
Explanation
The SNOWFLAKE.DATA SHARING _USAGE.LISTING_CONSOKE>TION_DAILY view provides information about consumer requests by region for each of the Data Exchange offeringsannually, as well as click-through rates for each listing. This view is the minimal data source needed for building the reporting tables. The other views are not relevant for this use case.

NEW QUESTION # 38
Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?

• A. OPEARTE
• B. OWNERSHIP
• C. MODIFY
• D. MANAGE GRANTS

Answer: B

NEW QUESTION # 39
Changing the retention period for your account or individual objects changes the value for all lower-level objects that do not have a retention period explicitly set?

• A. TRUE
• B. FALSE

Answer: A

NEW QUESTION # 40
The smaller the average depth, the better clustered the table is with regards to the specified column?

• A. TRUE
• B. FALSE

Answer: A

NEW QUESTION # 41
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

Answer: A

Explanation:
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.

NEW QUESTION # 42
By default, a newly-created Custom role is not assigned to any user, nor granted to any other role?

• A. TRUE
• B. FALSE

Answer: A

NEW QUESTION # 43
Mark the correct Statements with respect to Secure views & its creation in the SnowFlake Account?

• A. Secure views should not be used for views that are defined solely for query conven-ience, such as views created to simplify queries for which users do not need to under-stand the underlying data representation.
• B. For a secure view, internal optimizations can indirectly expose data & the view defini-tion is visible to other users.
• C. For non-materialized views, the IS_SECURE column in the Information Schema and Account Usage views identifies whether a view is secure.
• D. To convert an existing view to a secure view and back to a regular view, set/unset the SECURE keyword in the ALTER VIEW or ALTER MATERIALIZED VIEW com-mand.
• E. The internals of a secure view are not exposed in Query Profile (in the web interface). This is the case even for the owner of the secure view, because non-owners might have access to an owner's Query Profile.

Answer: A,C,D,E

Explanation:
Explanation
Why Should I Use Secure Views?
For a non-secure view, internal optimizations can indirectly expose data.
Some of the internal optimizations for views require access to the underlying data in the base tables for the view. This access might allow data that is hidden from users of the view to be exposed through user code, such as user-defined functions, or other programmatic methods. Secure views do not utilize these optimizations, ensuring that users have no access to the underlying data.
For a non-secure view, the view definition is visible to other users.
By default, the query expression used to create a standard view, also known as the view definition or text, is visible to users in various commands and interfaces.
For security or privacy reasons, you might not wish to expose the underlying tables or internal struc-tural details for a view. With secure views, the view definition and details are visible only to author-ized users (i.e.
users who are granted the role that owns the view).
When Should I Use a Secure View?
Views should be defined as secure when they are specifically designated for data privacy (i.e. to limit access to sensitive data that should not be exposed to all users of the underlying table(s)).
Secure views should not be used for views that are defined solely for query convenience, such as views created to simplify queries for which users do not need to understand the underlying data representation. Secure views can execute more slowly than non-secure views.
Secure views are defined using the SECURE keyword with the standard DDL for views:
To create a secure view, specify the SECURE keyword in the CREATE VIEW or CREATE MA-TERIALIZED VIEW command.
To convert an existing view to a secure view and back to a regular view, set/unset the SECURE keyword in the ALTER VIEW or ALTER MATERIALIZED VIEW command.
The definition of a secure view is only exposed to authorized users (i.e. users who have been grant-ed the role that owns the view). If an unauthorized user uses any of the following commands or in-terfaces, the view definition is not displayed:
SHOW VIEWS and SHOW MATERIALIZED VIEWS commands.
GET_DDL utility function.
VIEWS Information Schema view.
VIEWS Account Usage view.
For non-materialized views, the IS_SECURE column in the Information Schema and Account Us-age views identifies whether a view is secure.
The internals of a secure view are not exposed in Query Profile (in the web interface). This is the case even for the owner of the secure view, because non-owners might have access to an owner's Query Profile.

NEW QUESTION # 44
Snowflake supports using key pair authentication for enhanced authentication security as an alterna-tive to basic authentication (i.e. username and password). Select the list of SnowFlake Clients sup-port the same?
[Select All that Apply]

• A. SnowFlake Connector for Spark
• B. SnowCD
• C. Node.js
• D. SnowSQL
• E. Go Driver

Answer: A,C,D,E

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

Answer: A,B,E

Explanation:
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.

NEW QUESTION # 46
If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]

• A. field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.
• B. Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.
• C. Snowflake reads the leading space rather than the opening quotation character as the beginning of the field and the quotation characters are interpreted as string data.
  (Correct)
• D. COPY command trims the leading space and removes the quotation marks enclosing each field
  1.copy into SFtable
  2.from @%SFtable
  3.file_format = (type = csv trim_space=true field_optionally_enclosed_by = '0x22');

Answer: D

Explanation:
Explanation
If your external software exports fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, Snowflake reads the leading space rather than the open-ing quotation character as the beginning of the field. The quotation characters are interpreted as string data.
Use the TRIM_SPACE file format option to remove undesirable spaces during the data load.

NEW QUESTION # 47
Mark a Data Engineer, looking to implement streams on local views & want to use change tracking metadata for one of its Data Loading use case. Please select the incorrect understanding points of Mark with respect to usage of Streams on Views?

• A. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table.
• B. As an alternative to streams, Snowflake supports querying change tracking metadata for views using the CHANGES clause for SELECT statements.
• C. Views with GROUP BY & LIMIT Clause are supported by Snowflake.
• D. For streams on views, change tracking must be enabled explicitly for the view and un-derlying tables to add the hidden columns to these tables.
• E. Enabling change tracking adds a pair of hidden columns to the table and begins storing change tracking metadata. The values in these hidden CDC data columns provide the input for the stream metadata columns. The columns consume a small amount of stor-age.

Answer: C

Explanation:
Explanation
A stream object records data manipulation language (DML) changes made to tables, including in-serts, updates, and deletes, as well as metadata about each change, so that actions can be taken us-ing the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at the row level, between two transac-tional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
Standard tables, including shared tables.
Views, including secure views
Directory tables
External tables
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 the state 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. When the first stream for a table is created, a pair of hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables.
Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views.
Views with the following operations are not yet supported:
GROUP BY clauses
QUALIFY clauses
Subqueries not in the FROM clause
Correlated subqueries
LIMIT clauses
Change Tracking:
Change tracking must be enabled in the underlying tables.
Prior to creating a stream on a view, you must enable change tracking on the underlying tables for the view.
Set the CHANGE_TRACKING parameter when creating a view (using CREATE VIEW) or later (using ALTER VIEW).
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.

NEW QUESTION # 48
Which are false statements about Star Schema?

• A. The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes re-lated to fact data.
• B. The star schema is an important special case of the snowflake schema and is more effec-tive for handling simpler queries.
• C. Star schema is more flexible in terms of analytical needs compared to Data Vault Mod-elling.
• D. Star schemas are denormalized.

Answer: C

NEW QUESTION # 49
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. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data
• 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. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.

Answer: A

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 # 50
Mark the incorrect statement when Data Engineer implement Automating Continuous Data Loading Using Cloud Messaging?

• A. Automated Snowpipe uses event notifications to determine when new files arrive in monitored cloud storage and are ready to load.
• B. Notifications identify the cloud storage event and include a list of the file names. They do not include the actual data in the files.
• 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.

Answer: C

Explanation:
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.

NEW QUESTION # 51
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