Snowflake DEA-C02 Certification Exam Sample Questions

DEA-C02 Braindumps, DEA-C02 Exam Dumps, DEA-C02 Examcollection, DEA-C02 Questions PDF, DEA-C02 Sample Questions, SnowPro Advanced - Data Engineer Dumps, SnowPro Advanced - Data Engineer Official Cert Guide PDF, SnowPro Advanced - Data Engineer VCE, Snowflake SnowPro Advanced - Data Engineer PDFWe have prepared Snowflake SnowPro Advanced - Data Engineer (DEA-C02) certification sample questions to make you aware of actual exam properties. This sample question set provides you with information about the SnowPro Advanced - Data Engineer exam pattern, question formate, a difficulty level of questions and time required to answer each question. To get familiar with Snowflake Certified SnowPro Advanced - Data Engineer Certification exam, we suggest you try our Sample Snowflake DEA-C02 Certification Practice Exam in simulated Snowflake certification exam environment.

To test your knowledge and understanding of concepts with real-time scenario based Snowflake DEA-C02 questions, we strongly recommend you to prepare and practice with Premium Snowflake SnowPro Advanced - Data Engineer Certification Practice Exam. The premium Snowflake SnowPro Advanced - Data Engineer certification practice exam helps you identify topics in which you are well prepared and topics in which you may need further training to achieving great score in actual Snowflake Certified SnowPro Advanced - Data Engineer Certification exam.

Snowflake DEA-C02 Sample Questions:

01. Which statement about working with Snowflake virtual warehouses is incorrect?
a) Resizing a warehouse immediately affects statements that are already 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.
 
02. A CSV file of around 1 TB is generated daily on an on-premise server. A corresponding table, internal stage, and file format have already been created in Snowflake.
How can bringing the CSV file into Snowflake be automated with the least operational overhead?
a) Create a task in Snowflake that executes once a day and runs a COPY INTO statement referencing the internal stage. The internal stage reads the file directly from the on-premise server and copies the newest file into the table.
b) On the on-premise server, schedule a SQL file to run using SnowSQL that executes a PUT to push the file to the internal stage. Create a pipe that runs a COPY INTO statement referencing the internal stage, and let Snowpipe auto-ingest load the file when it lands.
c) On the on-premise server, schedule a SQL file to run using SnowSQL that executes a PUT to push the file to the internal stage. Create a task in Snowflake that executes once a day and runs a COPY INTO statement referencing the internal stage, scheduled to start after the file lands.
d) On the on-premise server, schedule a Python file that uses the Snowpark Python library to read the CSV into a DataFrame and generate an INSERT INTO statement that loads the table directly, bypassing the internal stage.
 
03. Marko, a data engineer, is using Snowpipe to load one of the finance workloads in micro-batches. While monitoring the loads he finds that a set of files has problems, so he queries the COPY_HISTORY view and checks the STATUS column, which reports whether a particular set of files was loaded, partially loaded or failed to load.
He now wants to see all of the errors in those files alongside the load status.
How can he do this?
a) He can check the RETURN_ALL_ERROR_MESSAGE column in the COPY_HISTORY view, which provides a reason and shows all errors in the files.
b) He can view all errors in the files by executing a COPY INTO statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS.
c) He can view all errors in the files by executing a COPY INTO statement with the VALIDATION_ERROR_MODE copy option set to RETURN_ALL_PIPE_ERRORS.
d) Marko can look at the FIRST_ERROR_MESSAGE column in the COPY_HISTORY view, which provides a reason why a file partially loaded or failed, for all of the files.
 
04. Charles, a lead data engineer using the ACCOUNTADMIN role, sets MIN_DATA_RETENTION_TIME_IN_DAYS to 79 at the account level. He then finds that DATA_RETENTION_TIME_IN_DAYS is already set to 81 at the account level.
What is the effective minimum data retention period for an object in this account?
a) 90
b) 79
c) 81
d) There is no such MIN_DATA_RETENTION_TIME_IN_DAYS parameter
 
05. The following command creates a clone of a database named snowmy_db:
CREATE DATABASE pods_db CLONE snowmy_db BEFORE (STATEMENT => '7e5d0cb9-005e-94e6-b058-k8f5b37c5725');
What are possible reasons for this cloning operation to fail?
(Select TWO.)
a) A SQL compilation error, because BEFORE is not valid syntax in a CREATE DATABASE statement.
b) The Time Travel point named by the statement is at or before the moment the object was created.
c) A SQL compilation error, because CREATE DATABASE does not support the STATEMENT keyword.
d) The Time Travel point is beyond the retention period of a current child object of the database, such as a table.
 
06. How can a data engineer monitor files that are staged internally during a continuous data loading process?
(Select THREE.)
a) The DATA_LOAD_HISTORY Information Schema view can be used to retrieve the history of data loaded into tables using the COPY INTO command.
b) The DATA_VALIDATE function can be used to validate the data files that were loaded and to retrieve any errors encountered during the load.
c) The files can be monitored through the metadata Snowflake maintains for the stage, such as file name and last modified date.
d) Snowflake retains historical data for COPY INTO commands executed within the previous 14 days.
e) The status of each COPY INTO command can be monitored on the History page of the web interface.
 
07. Ryan, a data engineer, wants to improve the performance of large, complex queries against large data sets, and has decided to scale up the underlying warehouse.
Which statements about resizing a Snowflake warehouse are correct?
(Select THREE.)
a) Resizing can help reduce the queuing that occurs if a warehouse does not have enough compute resources to process all the queries that are submitted concurrently.
b) Scaling up is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload, or use a multi-cluster warehouse.
c) A warehouse must be suspended before its size can be changed, so Ryan should suspend it, resize it and then resume it.
d) Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
e) Reducing the size of a running warehouse immediately cancels any statements still executing on the compute resources that are removed.
 
08. Each option below pairs a characteristic of bulk data loading with the corresponding characteristic of Snowpipe.
Which pairing is not correct?
a) Bulk data load: load history is stored in the metadata of the target table for 365 days.
Snowpipe: load history is stored in the metadata of the pipe for 64 days.
b) Bulk data load: loads are always performed in a single transaction.
Snowpipe: loads are combined or split into one or several transactions based on the number and size of the rows in each data file.
c) Bulk data load: requires a user-specified warehouse to execute COPY statements.
Snowpipe: uses Snowflake-supplied compute resources.
d) Bulk data load: billed for the amount of time each virtual warehouse is active.
Snowpipe: billed according to the compute resources used in the Snowpipe warehouse while loading the files.
 
09. Stuart, a lead data engineer, has created streams on a set of tables. He has been asked to keep those streams readable for 90 days, which means Snowflake must be allowed to hold the source tables' change data that long rather than letting the streams go stale.
Which parameter does he set?
a) DATA_RETENTION_TIME_IN_DAYS
b) MAX_DATA_EXTENSION_TIME_IN_DAYS
c) DATA_EXTENSION_TIME_IN_DAYS
d) STREAM_RETENTION_TIME_IN_DAYS
 
10. Which of the following products are security and governance tools that offer native connectivity to Snowflake?
(Select TWO.)
a) ALTR
b) BigSquid
c) Dataiku
d) Zepl
e) Baffle

Answers:

Question: 01
Answer: a		 Question: 02
Answer: c		 Question: 03
Answer: b		 Question: 04
Answer: c		 Question: 05
Answer: b, d
Question: 06
Answer: c, d, e		 Question: 07
Answer: a, b, d		 Question: 08
Answer: a		 Question: 09
Answer: b		 Question: 10
Answer: a, e

Note: Please update us by writing an email on feedback@vmexam.com for any error in Snowflake Certified SnowPro Advanced - Data Engineer Certification certification exam sample questions

Rating: 5 / 5 (82 votes)