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NEW QUESTION # 14
Marko, a Data Engineer is using Snowpipe for data loading in micro batches for one of the Finance Data workloads. There are set of files he attempted to load into the snowflake table using Snow-pipe. While monitoring he found that there are set of files has multiple issue, He queried the COPY_HISTORY view & checked the STATUS column which indicates whether a particular set of files was loaded, partially loaded, or failed to load. But he wants to view all errors in the files along with Load status, how he can check all errors?
- A. He can view all errors in the files, by executing a COPY INTO <table> statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS.
- B. He can check RETURN_ALL_ERROR_MESSAGE column in the COPY_HISTORY view which can provides a reason and view all errors in the files.
- C. He can view all errors in the files, by executing a COPY INTO <table> statement with the VALIDATION_ERROR_MODE copy option set to RE-TURN_ALL_PIPE_ERRORS.
- D. Marko can look out for FIRST_ERROR_MESSAGE column in the COPY_HISTORY view which can provides a reason why a file partially loaded or failed for all the files.
Answer: A
Explanation:
Explanation
The STATUS column indicates whether a particular set of files was loaded, partially loaded, or failed to load.
The FIRST_ERROR_MESSAGE column provides a reason when an attempt partial-ly loaded or failed.
Note that if a set of files has multiple issues, the FIRST_ERROR_MESSAGE column only indi-cates the first error encountered. To view all errors in the files, execute a COPY INTO <table> statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS. The VALIDATION_MODE copy option instructs a COPY statement to validate the data to be loaded and return results based on the validation option specified. No data is loaded when this copy option is specified.
NEW QUESTION # 15
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. Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.
- B. Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.
- C. Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.
- D. Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.
Answer: B
Explanation:
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.
NEW QUESTION # 16
You can execute zero, one, or more transactions inside a stored procedure?
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 17
Which callback function is required within a JavaScript User-Defined Function (UDF) for it to execute successfully?
- A. finalize ()
- B. handler
- C. processRow ()
- D. initialize ()
Answer: C
Explanation:
Explanation
The processRow () callback function is required within a JavaScript UDF for it to execute successfully. This function defines how each row of input data is processed and what output is returned. The other callback functions are optional and can be used for initialization, finalization, or error handling.
NEW QUESTION # 18
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. A materialized view speeds both equality searches and range 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. She can go for clustering on underlying tables which can speedup Equality searches.
Answer: B
Explanation:
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.
NEW QUESTION # 19
UDTFs also called a table function, returns zero, one, or multiple rows for each input row?
- A. NO
- B. YES
Answer: B
Explanation:
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.
NEW QUESTION # 20
A Data Engineer is building a pipeline to transform a 1 TD tab e by joining it with supplemental tables The Engineer is applying filters and several aggregations leveraging Common TableExpressions (CTEs) using a size Medium virtual warehouse in a single query in Snowflake.
After checking the Query Profile, what is the recommended approach to MAXIMIZE performance of this query if the Profile shows data spillage?
- A. Enable clustering on the table
- B. Increase the warehouse size
- C. Switch to a multi-cluster virtual warehouse
- D. Rewrite the query to remove the CTEs.
Answer: B
Explanation:
Explanation
The recommended approach to maximize performance of this query if the Profile shows data spillage is to increase the warehouse size. Data spillage occurs when the query requires more memory than the warehouse can provide and has to spill some intermediate results to disk. This can degrade the query performance by increasing the disk IO time. Increasing the warehouse size can increase the amount of memory available for the query and reduce or eliminate data spillage.
NEW QUESTION # 21
A Data Engineer ran a stored procedure containing various transactions During the execution, the session abruptly disconnected preventing one transactionfrom committing or rolling hark.The transaction was left in a detached state and created a lock on resources
...must the Engineer take to immediately run a new transaction?
- A. Set the transaction abort on error to true in the stored procedure.
- B. Set the LOCK_TIMEOUTto FALSE in the stored procedure
- C. Call the system function SYSTEM$CANCEL_TRANSACTION.
- D. Call the system function SYSTEM$ABORT_TRANSACTION.
Answer: D
Explanation:
Explanation
The system function SYSTEM$ABORT_TRANSACTION can be used to abort a detached transaction that was left in an open state due to a session disconnect or termination. The function takes one argument: the transaction ID of the detached transaction. The function will abort the transaction and release any locks held by it. The other options are incorrect because they do not address the issue of a detached transaction. The system function SYSTEM$CANCEL_TRANSACTION can be used to cancel a running transaction, but not a detached one. The LOCK_TIMEOUT parameter can be used to set a timeout period for acquiring locks on resources, but it does not affect existing locks. The TRANSACTION_ABORT_ON_ERROR parameter can be used to control whether a transaction should abort or continue when an error occurs, but it does not affect detached transactions.
NEW QUESTION # 22
Which are the two ways to access elements in a JSON object?
- A. Use SemiColon notation to traverse a path in a JSON object:
<col-umn>:<level1_element>;<level2_element>;<level3_element>. - B. Use dot notation to traverse a path in a JSON object:
<col-umn>:<level1_element>.<level2_element>.<level3_element>. - C. use bracket notation to traverse the path in an object:
<col-umn>['<level1_element>']['<level2_element>']. - D. use Curly bracket notation to traverse the path in an object:
<col-umn>{'<level1_element>'}{'<level2_element>'}.
Answer: B,C
NEW QUESTION # 23
Clones can be cloned, with no limitations on the number or iterations of clones that can be created (e.g. you can create a clone of a clone of a clone, and so on), which results in a n-level hierarchy of cloned objects, each with their own portion of shared and independent data storage?
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 24
A Data Engineer is trying to load the following rows from a CSV file into a table in Snowflake with the following structure:
....engineer is using the following COPY INTO statement:
However, the following error is received.
Which file format option should be used to resolve the error and successfully load all the data into the table?
- A. FIELD_DELIMITER = ","
- B. FIELD OPTIONALLY ENCLOSED BY = " "
- C. ERROR_ON_COLUMN_COUKT_MISMATCH = FALSE
- D. ESC&PE_UNENGLO9ED_FIELD = '\\'
Answer: B
Explanation:
Explanation
The file format option that should be used to resolve the error and successfully load all the data into the table is FIELD_OPTIONALLY_ENCLOSED_BY = '"'. This option specifies that fields in the file may be enclosed by double quotes, which allows for fields that contain commas or newlines within them. For example, in row 3 of the file, there is a field that contains a comma within double quotes: "Smith Jr., John". Without specifying this option, Snowflake will treat this field as two separate fields and cause an error due to column count mismatch. By specifying this option, Snowflake will treat this field as one field and load it correctly into the table.
NEW QUESTION # 25
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. authenticator
- C. client_prefetch_threads
- D. client_session_keep_alivs
- E. validate_default_parameters
Answer: C,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 # 26
In efforts to recover the dropped child tables within schema named SCV_SCHEMA by Data Engi-neer, She found that DATA_RETENTION_TIME_IN_DAYS parameter set with value 45 days at Schema level &the data retention period for child tables explicitly set at 85 days. What will happen when she will try to run undrop table command on Child tables to recover them on the 50th day as-suming SCV_SCHEMA is already dropped on 45th day?
- A. To honor the data retention period for child tables, She will ab able to recover the child tables on 50th day as DATA_RETENTION_TIME_IN_DAYS is explicitly set with higher retention value.
- B. Child tables can be recovered using Fail-Safe SQL commands.
- C. Data Engineer needs to first recover the Schema & then Child tables will automatically be recovered irrespective of Retention Inheritance.
- D. When a schema is already dropped, the data retention period for child tables, if explicit-ly set to be different from the retention of the schema, is not honoured. So UNDROP command will fail to run on
50th day for Child tables recovery.
Answer: D
Explanation:
Explanation
Dropped Containers and Object Retention Inheritance
Currently, when a database is dropped, the data retention period for child schemas or tables, if ex-plicitly set to be different from the retention of the database, is not honored. The child schemas or tables are retained for the same period of time as the database.
Similarly, when a schema is dropped, the data retention period for child tables, if explicitly set to be different from the retention of the schema, is not honored. The child tables are retained for the same period of time as the schema.
To honor the data retention period for these child objects (schemas or tables), drop them explicitly before you drop the database or schema.
NEW QUESTION # 27
Regular views do not cache data, and therefore cannot improve performance by caching?
- A. TRUE
- B. FALSE
Answer: A
Explanation:
Explanation
Regular views do not cache data, and therefore cannot improve performance by caching.
NEW QUESTION # 28
Which one is not the Core benefits of micro-partitioning
- A. Columns are stored independently within micro-partitions, often referred to as colum-nar storage.
- B. Columns are also compressed individually within micro-partitions.
- C. Enables extremely efficient DML and fine-grained pruning for faster queries.
- D. Micro-partitions can overlap in their range of values, helps data skewing.
- E. Snowflake micro-partitions are derived automatically they do not need to be explicitly defined up-front or maintained by users.
Answer: D
Explanation:
Explanation
The benefits of Snowflake's approach to partitioning table data include:
In contrast to traditional static partitioning, Snowflake micro-partitions are derived automatically; they don't need to be explicitly defined up-front or maintained by users.
As the name suggests, micro-partitions are small in size (50 to 500 MB, before compression), which enables extremely efficient DML and fine-grained pruning for faster queries.
Micro-partitions can overlap in their range of values, which, combined with their uniformly small size, helps prevent skew.
Columns are stored independently within micro-partitions, often referred to as columnar storage. This enables efficient scanning of individual columns; only the columns referenced by a query are scanned.
Columns are also compressed individually within micro-partitions. Snowflake automatically de-termines the most efficient compression algorithm for the columns in each micro-partition.
NEW QUESTION # 29
In Which Data Modelling Technique, Data Engineer generally refer the terms Hubs & Satellites?
- A. Star Schema
- B. Data Vault
- C. Snowflake Schema
- D. Data Hub
Answer: B
Explanation:
Explanation
In Data Vault modelling, Hubs are entities of interest to the business.
They contain just a distinct list of business keys and metadata about when each key was first loaded and from where.
In Data Vault modelling, Satellites connect to Hubs or Links. They are Point in Time: so we can ask and answer the question, "what did we know when?" Satellites contain data about their parent Hub or Link and metadata about when the data was load-ed, from where, and a business effectivity date.
NEW QUESTION # 30
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. GCP
- B. None of the Above currently provide cross cloud support for Snowpipe.
- C. AZURE
- D. AWS
Answer: D
Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.
NEW QUESTION # 31
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. I
- C. III
- D. II
Answer: D
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 # 32
Select the correct usage statements with regards to SQL UDF?
- A. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
- B. You can include only one 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 # 33
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 number of maximum clusters of the virtual warehouse
- B. Define a clustering key for the sales data table
- C. Increase the size of the virtual warehouse
- D. Rewrite the report to eliminate the use of the loop construct
Answer: D
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 # 34
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 only views and secure stored procedures can be shared
- D. An error will be returned because the Engineer cannot share data that has already been shared
Answer: D
Explanation:
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.
NEW QUESTION # 35
Let us say you have List of 50 Source files, which needs to be loaded into Snowflake internal stage. All these Source system files are already Brotli-compressed files. Which statement is correct with respect to Compression of Staged Files?
- A. Even though Source files are already compressed, Snowflake do apply default gzip2 Compression to optimize the storage cost.
- B. When staging 50 compressed files in a Snowflake stage, the files are automatically com-pressed using gzip.
- C. Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.
- D. Snowflake automatically detect Brotli Compression, will skip further compression of all 50 files.
Answer: C
Explanation:
Explanation
Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.
To Know more about Compression of Staged Files, please refer the link:
https://docs.snowflake.com/en/user-guide/intro-summary-loading.html#compression-of-staged-files
NEW QUESTION # 36
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. UDF definition or text, is visible to users via Query Profile (in the web interface).
- B. SHOW FUNCTIONS Commands
- C. Declaring a UDF as "secure" hide the definition from Bob & all the required Definition commands will throw error.
- D. He can get the secure UDF definition using GET_DDL utility function.
Answer: C
NEW QUESTION # 37
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