[Mar-2024] Snowflake DEA-C01 Dumps – Reduce Your Chance of Failure in DEA-C01 Exam
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NEW QUESTION # 10
Which of the following security and governance tools/technologies are known to provide native connectivity to Snowflake? [Select 2]
- A. Dataiku
- B. Zepl
- C. ALTR
- D. Baffle
- E. BIG Squid
Answer: C,D
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 # 11
Jackie, a Data engineer advised to his data team members about one of the Role highlighting fol-lows points:
1. Avoid Using the <?> Role for Automated Scripts
2. Avoid Using the <?> Role to Create Objects
Which System defined or Custom Role She is mentioning?
- A. ACCOUNTADMIN
- B. SECURITYADMIN
- C. USERADMIN
- D. CUSTOM Role
- E. SYSADMIN
Answer: A
NEW QUESTION # 12
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. GROUP_BY_CONNECT
- B. CONVERT_TO_ARRAY
- C. NEST
- D. SPLIT
Answer: D
NEW QUESTION # 13
To troubleshoot data load failure in one of your Copy Statement, Data Engineer have Executed a COPY statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS with reference to the set of files he had attempted to load. Which below function can facilitate analysis of the problematic records on top of the Results produced? [Select 2]
- A. RESULT_SCAN
- B. LAST_QUERY_ID
- C. Rejected_record
- D. LOAD_ERROR
Answer: A,B
Explanation:
Explanation
LAST_QUERY_ID() Function
Returns the ID of a specified query in the current session. If no query is specified, the most recently executed query is returned.
RESULT_SCAN() Function
Returns the result set of a previous command (within 24 hours of when you executed the query) as if the result was a table.
The following example validates a set of files (SFfile.csv.gz) that contain errors. To facilitate analy-sis of the errors, a COPY INTO <location> statement then unloads the problematic records into a text file so they could be analyzed and fixed in the original data files. The statement queries the RESULT_SCAN table.
1.#copy into Snowtable
2.from @SFstage/SFfile.csv.gz
3.validation_mode=return_all_errors;
4.#set qid=last_query_id();
5.#copy into @SFstage/errors/load_errors.txt from (select rejected_record from ta-ble(result_scan($qid))); Note: Other options are not valid functions.
NEW QUESTION # 14
A Data Engineer is evaluating the performance of a query in a development environment.
Based on the Query Profile what are some performance tuning options the Engineer can use? (Select TWO)
- A. Move the query to a larger virtual warehouse
- B. Use a multi-cluster virtual warehouse with the scaling policy set to standard
- C. Add a LIMIT to the ORDER BY If possible
- D. Create indexes to ensure sorted access to data
- E. Increase the max cluster count
Answer: A,C
Explanation:
Explanation
The performance tuning options that the Engineer can use based on the Query Profile are:
Add a LIMIT to the ORDER BY If possible: This option will improve performance by reducing the amount of data that needs to be sorted and returned by the query. The ORDER BY clause requires sorting all rows in the input before returning them, which can be expensive and time-consuming. By adding a LIMIT clause, the query can return only a subset of rows that satisfy the order criteria, which can reduce sorting time and network transfer time.
Create indexes to ensure sorted access to data: This option will improve performance by reducing the amount of data that needs to be scanned and filtered by the query. The query contains several predicates on different columns, such as o_orderdate, o_orderpriority, l_shipmode, etc. By creating indexes on these columns, the query can leverage sorted access to data and prune unnecessary micro-partitions or rows that do not match the predicates. This can reduce IO time and processing time.
The other options are not optimal because:
Use a multi-cluster virtual warehouse with the scaling policy set to standard: This option will not improve performance, as the query is already using a multi-cluster virtual warehouse with the scaling policy set to standard. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on the load. Changing the warehouse size or the number of clusters will not affect the performance of this query, as it is already using the optimal resources.
Increase the max cluster count: This option will not improve performance, as the query is not limited by the max cluster count. The max cluster count is a parameter that specifies the maximum number of clusters that a multi-cluster virtual warehouse can scale up to. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on theload. The default max cluster count for a 2XL warehouse is 10, which means that the warehouse can scale up to 10 clusters if needed. However, the query does not need more than 4 clusters, as it is not CPU-bound or memory-bound. Increasing the max cluster count will not affect the performance of this query, as it will not use more clusters than necessary.
NEW QUESTION # 15
A Data Engineer wants to check the status of a pipe named my_pipe. The pipe is inside a database named test and a schema named Extract (case-sensitive).
Which querywill provide the status of the pipe?
- A. SELE2T * FROM SYSTEM$PIPE_STATUS < ' test. "Extract", my_pipe');
- B. SELECT FROM SYSTEM$PIPE_STATUS (,test.,,Extracr,,.ny_pipe, i I
- C. SELECT FROM SYSTEM$PIPE_STATUS (''test.'extract'.my_pipe"i:
- D. SELECT * FROM SYSTEM$PIPE_STATUS ("test. 'extract' .my_pipe"};
Answer: A
Explanation:
Explanation
The query that will provide the status of the pipe is SELECT * FROM
SYSTEM$PIPE_STATUS('test."Extract".my_pipe');. The SYSTEM$PIPE_STATUS function returns information about a pipe, such as its name, status, last received message timestamp, etc. The function takes one argument: the pipe name in a qualified form. The pipe name should include the database name, the schema name, and the pipe name, separated by dots. If any of these names are case-sensitive identifiers, they should be enclosed in double quotes. In this case, the schema name Extract is case-sensitive and should be quoted. The other options are incorrect because they do not follow the correct syntax for the pipe name argument. Option A and B use single quotes instead of double quotes for case-sensitive identifiers. Option D uses double quotes instead of single quotes for non-case-sensitive identifiers.
NEW QUESTION # 16
What is a characteristic of the operations of streams in Snowflake?
- A. Each committed and uncommitted transaction on the source table automatically puts a change record in the stream.
- B. Querying a stream returns all change records and table rows from the current offset to the current time.
- C. Whenever a stream is queried, the offset is automatically advanced.
- D. When a stream is used to update a target table the offset is advanced to the current time.
Answer: B
Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream has an offset, which is a point in time that marks the beginning of the change records to be returned by the stream. Querying a stream returns all change records and table rows from the current offset to the current time. The offset is not automatically advanced by querying the stream, but it can be manually advanced by using the ALTER STREAM command. When a stream is used to update a target table, the offset is advanced to the current time only if the ON UPDATE clause is specified in the stream definition. Each committed transaction on the source table automatically puts a change record in the stream, but uncommitted transactions do not.
NEW QUESTION # 17
Which are the Cloud Platforms that Support Calling an External Function?
- A. AWS & AZURE
- B. AWS,GCP,AZURE
- C. GCP
- D. AWS only
Answer: B
NEW QUESTION # 18
1.+--------------------------------------------------------------+
2.| SYSTEM$CLUSTERING_INFORMATION('SF_DATA', '(COL1, COL3)') |
3.|--------------------------------------------------------------|
4.| { |
5.| "cluster_by_keys" : "(COL1, COL3)", |
6.| "total_partition_count" : 1156, |
7.| "total_constant_partition_count" : 0, |
8.| "average_overlaps" : 117.5484, |
9.| "average_depth" : 64.0701, |
10.| "partition_depth_histogram" : { |
11.| "00000" : 0, |
12.| "00001" : 0, |
13.| "00002" : 3, |
14.| "00003" : 3, |
15.| "00004" : 4, |
16.| "00005" : 6, |
17.| "00006" : 3, |
18.| "00007" : 5, |
19.| "00008" : 10, |
20.| "00009" : 5, |
21.| "00010" : 7, |
22.| "00011" : 6, |
23.| "00012" : 8, |
24.| "00013" : 8, |
25.| "00014" : 9, |
26.| "00015" : 8, |
27.| "00016" : 6, |
28.| "00032" : 98, |
29.| "00064" : 269, |
30.| "00128" : 698 |
31.| } |
32.| } |
33.+--------------------------------------------------------------+
The Above example indicates that the SF_DATA table is not well-clustered for which of following valid reasons?
- A. ALL of the above
- B. High average of overlapping micro-partitions.
- C. High average of overlap depth across micro-partitions.
- D. Most of the micro-partitions are grouped at the lower-end of the histogram, with the majority of micro-partitions having an overlap depth between 64 and 128.
- E. Zero (0) constant micro-partitions out of 1156 total micro-partitions.
Answer: A
NEW QUESTION # 19
Which Role that is dedicated to user and role management only?
- A. USERADMIN
- B. ORGADMIN
- C. PUBLIC
- D. SECURITYADMIN
- E. SYSADMIN
Answer: A
NEW QUESTION # 20
For enabling non-ACCOUNTADMIN Roles to Perform Data Sharing Tasks, which two glob-al/account privileges snowflake provide?
- A. OPERATE
- B. CREATE SHARE
- C. REFERENCE USAGE
- D. IMPORT SHARE
Answer: B,D
Explanation:
Explanation
CREATE SHARE
In a provider account, this privilege enables creating and managing shares (for sharing data with consumer accounts).
IMPORT SHARE
In a consumer account, this privilege enables viewing the inbound shares shared with the account. Also enables creating databases from inbound shares; requires the global CREATE DATABASE privilege.
By default, these privileges are granted only to the ACCOUNTADMIN role, ensuring that only ac-count administrators can perform these tasks. However, the privileges can be granted to other roles, enabling the tasks to be delegated to other users in the account.
NEW QUESTION # 21
To help manage STAGE storage costs, Data engineer recommended to monitor stage files and re-move them from the stages once the data has been loaded and the files which are no longer needed. Which option he can choose to remove these files either during data loading or afterwards?
- A. He can choose to remove stage files during data loading (using the COPY INTO <table> command).
- B. Files no longer needed, can be removed using the REMOVE command.
- C. Files no longer needed, can be removed using the PURGE=TRUE command.
- D. Script can be used during data loading & post data loading with DELETE command.
Answer: A,C
Explanation:
Explanation
Managing Data Files
Staged files can be deleted from a Snowflake stage (user stage, table stage, or named stage) using the following methods:
Files that were loaded successfully can be deleted from the stage during a load by specifying the PURGE copy option in the COPY INTO <table> command.
After the load completes, use the REMOVE command to remove the files in the stage.
Removing files ensures they aren't inadvertently loaded again. It also improves load performance, because it reduces the number of files that COPY commands must scan to verify whether existing files in a stage were loaded already.
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. Rewrite the report to eliminate the use of the loop construct
- C. Define a clustering key for the sales data table
- D. Increase the number of maximum clusters of the virtual warehouse
Answer: B
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
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: B
NEW QUESTION # 24
Which Snowflake feature facilitates access to external API services such as geocoders. data transformation, machine Learning models and other custom code?
- A. External functions
- B. Java User-Defined Functions (UDFs)
- C. Security integration
- D. External tables
Answer: A
Explanation:
Explanation
External functions are Snowflake functions that facilitate access to external API services such as geocoders, data transformation, machine learning models and other custom code. External functions allow users to invoke external services from within SQL queries and pass arguments and receive results as JSON values. External functions require creating an API integration object and an external function object in Snowflake, as well as deploying an external service endpoint that can communicate with Snowflake via HTTPS.
NEW QUESTION # 25
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