Snowflake DEA-C01 Daily Practice Exam New 2024 Updated 67 Questions
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NEW QUESTION # 22
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. Increase the size of the virtual warehouse
- B. Increase the number of maximum clusters of the virtual warehouse
- C. Rewrite the report to eliminate the use of the loop construct
- D. Define a clustering key for the sales data table
Answer: C
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 # 23
Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)
- A. DateFrame.select ()
- B. DataFrame.random_split ( )
- C. DataFrame.col ( )
- D. DataFrame.show ()
- E. DataFrame.collect ()
Answer: D,E
Explanation:
Explanation
The methods that will trigger an action that will evaluate a DataFrame are DataFrame.collect() and DataFrame.show(). These methods will force the execution of any pending transformations on the DataFrame and return or display the results. The other options are not methods that will evaluate a DataFrame. Option A, DataFrame.random_split(), is a method that will split a DataFrame into two or more DataFrames based on random weights. Option C, DataFrame.select(), is a method that will project a set of expressions on a DataFrame and return a new DataFrame. Option D, DataFrame.col(), is a method that will return a Column object based on a column name in a DataFrame.
NEW QUESTION # 24
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$CLUSTERING_DEPTH
- D. SYSTEM$CLUSTERING_DEPTH_KEY
Answer: B,C
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 # 25
While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. gen-erated by UUID_STRING) instead of sequence-generated values?
- A. TRUE
(Correct) - B. FALSE
Answer: A
NEW QUESTION # 26
A Data Engineer has written a stored procedure that will run with caller's rights. The Engineer has granted ROLEA right to use this stored procedure.
What is a characteristic of the stored procedure being called using ROLEA?
- A. If the stored procedure accesses an object that ROLEA does not have access to the stored procedure will fail
- B. The stored procedure must run with caller's rights it cannot be converted later to run with owner's rights
- C. ROLEA will not be able to see the source code for the stored procedure even though the role has usage privileges on the stored procedure
- D. The stored procedure will run in the context (database and schema) where the owner created the stored procedure
Answer: A
Explanation:
Explanation
A stored procedure that runs with caller's rights executes with the privileges of the role that calls it. Therefore, if the stored procedure accesses an object that ROLEA does not have access to, such as a table or a view, the stored procedure will fail with an insufficient privileges error. The other options are not correct because:
A stored procedure can be converted from caller's rights to owner's rights by using the ALTER PROCEDURE command with the EXECUTE AS OWNER option.
A stored procedure that runs with caller's rights executes in the context (database and schema) of the caller, not the owner.
ROLEA will be able to see the source code for the stored procedure by using the GET_DDL function or the DESCRIBE command, as long as it has usage privileges on the stored procedure.
NEW QUESTION # 27
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 # 28
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. Third-party supported Python packages are locked down to prevent hitting
- B. Python packages can only be loaded in a local environment
- C. Python packages can access any external endpoints
- D. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
- E. Querying information__schema .packages will provide a list of supported Python packages and versions
Answer: C,D,E
Explanation:
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.
NEW QUESTION # 29
Which Role inherits the privileges of the USERADMIN role via the system role hierarchy?
- A. SYSADMIN
- B. SECURITYADMIN
- C. PUBLIC
- D. CUSTOM ROLE
Answer: B
NEW QUESTION # 30
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 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
- 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 table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering
- 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
Answer: B
Explanation:
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.
NEW QUESTION # 31
Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?
- A. CORRECT_ROLE
- B. IS_GRANTED_TO_INVOKER_ROLE
- C. IS_RCLE_IN_SESSION
- D. IKVOKER_ROLE
Answer: C
Explanation:
Explanation
The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user's session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:
CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION('HR') THEN val ELSE REGEXP_REPLACE(val, '(.).(.@.)', '\1****\2') END; In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column.
The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.
NEW QUESTION # 32
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. IV
- B. II
- C. I
- D. III
Answer: B
Explanation:
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')
NEW QUESTION # 33
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions?
- A. FALSE
- B. TRUE
Answer: B
Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions. The rea-son is that the owner of the data being shared does not typically control the users or roles in the ac-count with which the UDF is being shared.
NEW QUESTION # 34
Search optimization works best to improve the performance of a query when the following condi-tions are true:[Select All that apply]
- A. The table is frequently queried on columns other than the primary cluster key.
- B. The table is not clustered.
- C. Search Query uses Equality predicates (for example, <column_name> = <constant>) OR Predicates that use IN.
- D. Search Query uses Sort Operations.
Answer: A,B,C
Explanation:
Explanation
Materialized Views works best for search query performance in case of Sort Operations. For Rest of the points Search optimization works best to improve query performance.
NEW QUESTION # 35
Data Engineer Loading File named snowdata.tsv in the /datadir directory from his local machine to Snowflake stage and try to prefix the file with a folder named tablestage, please mark the correct command which helps him to load the files data into snowflake internal Table stage?
- A. put file://c:\datadir\snowdata.tsv @%tablestage;
- B. put file://c:\datadir\snowdata.tsv @~/tablestage;
- C. put file:///datadir/snowdata.tsv @%tablestage;
- D. put file://c:\datadir\snowdata.tsv @tablestage;
Answer: A
Explanation:
Explanation
Execute PUT to upload (stage) local data files into an internal stage.
@% character combination identifies a table stage.
NEW QUESTION # 36
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 a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data
- C. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
- D. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
Answer: B
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 # 37
Which of the following security and governance tools/technologies are known to provide native connectivity to Snowflake? [Select 2]
- A. BIG Squid
- B. ALTR
- C. Baffle
- D. Dataiku
- E. Zepl
Answer: B,C
Explanation:
Explanation
Security and governance tools ensure sensitive data maintained by an organization is protected from inappropriate access and tampering, as well as helping organizations to achieve and maintain regula-tory compliance. These tools are often used in conjunction with observability solutions/services to provide organizations with visibility into the status, quality, and integrity of their data, including identifying potential issues.
Together, these tools support a wide range of operations, including risk assessment, intrusion detec-tion/monitoring/notification, data masking, data cataloging, data health/quality checks, issue identi-fication/troubleshooting/resolution, and more.
ALTR & Baffle are correct options here.
NEW QUESTION # 38
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