[Jan 31, 2024] Ultimate DEA-C01 Guide to Prepare Free Latest Snowflake Practice Tests Dumps
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NEW QUESTION # 12
A Data Engineer has created table t1 with datatype VARIANT:
create or replace table t1 (cl variant);
The Engineer has loaded the following JSON data set. which has information about 4 laptop models into the table:
The Engineer now wants to query that data set so that results are shown as normal structured data. The result should be 4 rows and 4 columns without the double quotes surrounding the data elements in the JSON data.
The result should be similar to the use case where the data was selected from a normal relational table z2 where t2 has string data type columns model__id. model, manufacturer, and =iccisi_r.an=. and is queried with the SQL clause select * from t2; Which select command will produce the correct results?
- A.
- B.
- C.
- D.
Answer: D
NEW QUESTION # 13
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. Snowpipe uses file loading metadata associated with each pipe object to prevent reload-ing the same files (and duplicating data) in a table.
- B. Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.
- C. eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.
- D. Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.
Answer: A
Explanation:
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.
NEW QUESTION # 14
Assuming that the session parameter USE_CACHED_RESULT is set to false, what are characteristics of Snowflake virtual warehouses in terms of the use of Snowpark?
- A. Creating a DataFrame from a staged file with the read () method will start a virtual warehouse
- B. Calling a Snowpark stored procedure to query the database with session, call () will start a virtual warehouse
- C. Creating a DataFrame from a table will start a virtual warehouse
- D. Transforming a DataFrame with methods like replace () will start a virtual warehouse -
Answer: C
Explanation:
Explanation
Creating a DataFrame from a table will start a virtual warehouse because it requires reading data from Snowflake. The other options will not start a virtual warehouse because they either operate on local data or use an existing session to query Snowflake.
NEW QUESTION # 15
UDTFs also called a table function, returns zero, one, or multiple rows for each input row?
- A. YES
- B. NO
Answer: A
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 # 16
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. Rewrite the report to eliminate the use of the loop construct
- B. Increase the number of maximum clusters of the virtual warehouse
- C. Define a clustering key for the sales data table
- D. Increase the size of the virtual warehouse
Answer: A
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 # 17
What kind of Snowflake integration is required when defining an external function in Snowflake?
- A. HTTP integration
- B. API integration
- C. Notification integration
- D. Security integration
Answer: B
Explanation:
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.
NEW QUESTION # 18
Elon, a Data Engineer, needs to Split Semi-structured Elements from the Source files and load them as an array into Separate Columns.
Source File:
1.+----------------------------------------------------------------------+
2.| $1 |
3.|----------------------------------------------------------------------|
4.| {"mac_address": {"host1": "197.128.1.1","host2": "197.168.0.1"}}, |
5.| {"mac_address": {"host1": "197.168.2.1","host2": "197.168.3.1"}} |
6.+----------------------------------------------------------------------+ Output: Splitting the Machine Address as below.
1.COL1 | COL2 |
2.|----------+----------|
3.| [ | [ |
4.| "197", | "197", |
5.| "128", | "168", |
6.| "1", | "0", |
7.| "1" | "1" |
8.| ] | ] |
9.| [ | [ |
10.| "197", | "197", |
11.| "168", | "168", |
12.| "2", | "3", |
13.| "1" | "1" |
14.| ] | ]
Which SnowFlake Function can Elon use to transform this semi structured data in the output for-mat?
- A. CONVERT_TO_ARRAY
- B. GROUP_BY_CONNECT
- C. NEST
- D. SPLIT
Answer: D
NEW QUESTION # 19
Which callback function is required within a JavaScript User-Defined Function (UDF) for it to execute successfully?
- A. handler
- B. processRow ()
- C. initialize ()
- D. finalize ()
Answer: B
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 # 20
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. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)
- C. External stage on GCP us-central1 (Iowa)
- D. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
- E. Internal stage on GCP us-central1 (Iowa)
- F. Internal stage on AWS eu-central-1 (Frankfurt)
Answer: A,C,D
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 # 21
What is a characteristic of the use of external tokenization?
- A. External tokenization allows (he preservation of analytical values after de-identification
- B. External tokenization cannot be used with database replication
- C. Pre-loading of unmasked data is supported with external tokenization
- D. Secure data sharing can be used with external tokenization
Answer: A
Explanation:
Explanation
External tokenization is a feature in Snowflake that allows users to replace sensitive data values with tokens that are generated and managed by an external service. External tokenization allows the preservation of analytical values after de-identification, such as preserving the format, length, or range of the original values.
This way, users can perform analytics on the tokenized data without compromising the security or privacy of the sensitive data.
NEW QUESTION # 22
As Data Engineer, you have been asked to access data held in AWS Glacier Deep Archive storage class for Historical Data Analysis, which one is the correct statement to recommend?
- A. You cannot access data held in archival cloud storage classes that requires restoration before it can be retrieved.
- B. We can simply access AWS Glacier Deep Archive storage External Stage data using PUT command.
- C. Loading data from AWS cloud storage services is supported regardless of the cloud platform that hosts your Snowflake account.
- D. Upload (i.e. stage) files to your cloud storage account using the tools provided by the cloud storage service.
- E. Data can be accessed from External stage using AWS Private link in this case.
Answer: A
Explanation:
Explanation
External stage
References data files stored in a location outside of Snowflake. Currently, the following cloud stor-age services are supported:
Amazon S3 buckets
Google Cloud Storage buckets
Microsoft Azure containers
The storage location can be either private/protected or public.
You cannot access data held in archival cloud storage classes that requires restoration before it can be retrieved. These archival storage classes include, for example, the Amazon S3 Glacier Flexible Retrieval or Glacier Deep Archive storage class, or Microsoft Azure Archive Storage.
NEW QUESTION # 23
Select the incorrect statement while working with warehouses?
- A. Resizing a warehouse will have any immediate impact on statements that are currently being executed by the warehouse.
- B. Resizing a warehouse to a larger size is useful while loading and unloading significant amounts of data.
- C. Compute resources waiting to shut down are considered to be in "quiesce" mode.
- D. Resizing a suspended warehouse does not provision any new compute resources for the warehouse.
Answer: A
Explanation:
Explanation
Resizing a warehouse doesn't have any impact on statements that are currently being executed by the warehouse. When resizing to a larger size, the new compute resources, once fully provisioned, are used only to execute statements that are already in the warehouse queue, as well as all future statements submitted to the warehouse.
NEW QUESTION # 24
Jonas, a Lead Performance Engineer,identifed that some of the operation of his query which func-tionally remove the duplicates from huge data set is spilling the data to remote disk. How can he alleviate spilling to a remote disk for better query performance?
- A. Jonas can recommend using a large warehouse which effectively increase the available memory/local disk space for the operations.
- B. He can Process data in smaller batches to manage workload.
- C. Data Sharing can be helpful to improve query performance.
- D. Spilling do not have a profound effect on query performance (especially if remote disk is used for spilling).
Answer: A,B
Explanation:
Explanation
For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the compute resources used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to remote disks.
This spilling can have a profound effect on query performance (especially if remote disk is used for spilling).
To alleviate this, It is recommend that:
Using a larger warehouse (effectively increasing the available memory/local disk space for the op-eration), and/or Processing data in smaller batches.
NEW QUESTION # 25
Select the incorrect statements regarding Clustering depth?
- A. Clustering depth can be used for determining whether a large table would benefit from explicitly defining a clustering key.
- B. It helps Monitoring the clustering "health" of a large table, particularly over time as DML is performed on the table.
- C. A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 1.
(Correct) - D. The clustering depth for a populated table measures the average depth (1 or greater) of the overlapping micro-partitions for specified columns in a table. The smaller the aver-age depth, the better clustered the table is with regards to the specified columns.
Answer: C
Explanation:
Explanation
A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 0.
NEW QUESTION # 26
External Function is a type of UDF & can be Scaler or Tabular?
- A. TRUE
- B. FALSE
Answer: B
Explanation:
Explanation
External functions must be scalar functions. A scalar external function returns a single value for each input row.
NEW QUESTION # 27
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. Child tables can be recovered using Fail-Safe SQL commands.
- B. Data Engineer needs to first recover the Schema & then Child tables will automatically be recovered irrespective of Retention Inheritance.
- C. 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. - D. 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.
Answer: C
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 # 28
Mark the Correct Statements for the VALIDATION_MODE option used by Data Engineer for Da-ta loading operations in his/her COPY INTO <table> command:
- A. VALIDATION_MODE instructs the COPY command to validate the data files instead of loading them into the specified table; i.e., the COPY command tests the files for er-rors but does not load them.
- B. VALIDATION_MODE only support Data loading operation i.e., do not work while da-ta unloading.
- C. VALIDATION_MODE option supported these values:
RETURN_n_ROWS,
RETURN_ERRORS,
RETURN_ALL_ERRORS - D. VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error.
Answer: A,C,D
Explanation:
Explanation
All the Statements are correct except the statement saying VALIDATION_MODE only support Data loading operation.
VALIDATION_MODE can be used with COPY INTO <location> command as well i.e for data unloading operation.
VALIDATION_MODE = RETURN_ROWS can be used at the time of Data unloading.
This option instructs the COPY command to return the results of the query in the SQL statement instead of unloading the results to the specified cloud storage location. The only supported valida-tion option is RETURN_ROWS. This option returns all rows produced by the query.
When you have validated the query, you can remove the VALIDATION_MODE to perform the unload operation.
NEW QUESTION # 29
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. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
- C. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
- D. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
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 # 30
As part of Table Designing, Data Engineer added a timestamp column that inserts the current timestamp as the default value as records are loaded into a table. The intent is to capture the time when eachrecord was loaded into the table; however, the timestamps are earlier than the LOAD_TIME column values returned by COPY_HISTORY view (Account Usage). What could be reason of this issue?
- A. LOAD_TIME column values returned by COPY_HISTORY view (Account Usage) gives the same time as returned by CURRENT_TIMESTAMP.
- B. CURRENT_TIMESTAMP values might be different due to query gets executed in warehouse located in different region.
- C. It might be possible that Cloud Provider hosted on Snowflake belongs to region having server time zone lagging Cluster time zone of warehouse where queries get processed & committed.
- D. The reason is, CURRENT_TIMESTAMP is evaluated when the load operation is com-piled in cloud services rather than when the record is inserted into the table (i.e. when the transaction for the load operation is committed).
Answer: D
Explanation:
Explanation
The reason timestamps are earlier than the LOAD_TIME column values which is returned by COPY_HISTORY view (Account Usage) is that CURRENT_TIMESTAMP is evaluated when the load operation is compiled in cloud services rather than when the record is inserted into the table (i.e. when the transaction for the load operation is committed).
NEW QUESTION # 31
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?
- A. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage
- B. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
- C. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table
- D. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.
Answer: D
Explanation:
Explanation
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.
NEW QUESTION # 32
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