Valid DEA-C01 Exam Dumps are Your best Choice to Pass

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October 16, 2023

If you are looking to take your career in SnowPro Advanced Certification to the next level, the DEA-C01 certification is an excellent option. To prepare for the DEA-C01 exam, you need to have a deep understanding of Snowflake products and how to configure them. The best way to prepare for the exam is by using DEA-C01 exam dumps questions, which give you a better understanding of the format of the exam. This will help you become familiar with the types of questions you can expect on the actual DEA-C01 exam, and it will give you a chance to practice your test-taking skills. Test free online DEA-C01 exam dumps questions below.

Page 1 of 4

1. 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?

Even though Source files are already compressed, Snowflake do apply default gzip2 Compression to optimize the storage cost.

Snowflake automatically detect Brotli Compression, will skip further compression of all 50 files.

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.

When staging 50 compressed files in a Snowflake stage, the files are automatically com-pressed using gzip.

2. Mark the Incorrect Statements with respect to types of streams supported by Snowflake?

Standard streams cannot retrieve update data for geospatial data.

An append-only stream returns the appended rows only and therefore can be much more performant than a standard stream for extract, load, transform (ELT).

Insert-only Stream supported on external tables only.

An insert-only stream tracks row inserts & Delete ops only

3. Which is the non-supportable JavaScript UDF data types?

Integers

String

Binary

Double

4. 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?

Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.

eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.

Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.

Snowpipe uses file loading metadata associated with each pipe object to prevent reloading the same files (and duplicating data) in a table.

5. Ryan, a Data Engineer, accidently drop the Share named SF_SHARE which results in immediate access revoke for all the consumers (i.e., accounts who have created a database from that SF_SHARE).

What action he can take to recover the dropped Share?

By Executing UNDROP command he could possibly recover the dropped Share SF_SHARE & its associated Databases for immediate consumer access.

He can recreate a share with the same name as a previous share which does restore the databases created (by any consumers) from the share SF_SHAR

A dropped share cannot be restored. The share must be created again using the CRE-ATE SHARE command and then configured using GRANT … TO SHARE and ALTER SHAR

Consumer accounts that have created databases from the share will still be able to query these databases as Share is separate securable object & it’s still accessible using time travel feature.

6. For SQL UDFs, The invoker of the function need not have access to the objects referenced in the function definition, but only needs the privilege to use the function?

TRUE

FALSE

7. By default, a newly-created Custom role is not assigned to any user, nor granted to any other role?

TRUE

FALSE

8. 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?

GCP

AWS

AZURE

None of the Above currently provide cross cloud support for Snowpipe.

9. Select the incorrect statement while working with warehouses?

Compute resources waiting to shut down are considered to be in “quiesce” mode.

Resizing a warehouse to a larger size is useful while loading and unloading significant amounts of data.

Resizing a warehouse will have any immediate impact on statements that are currently being executed by the warehouse.

Resizing a suspended warehouse does not provision any new compute resources for the warehouse.

10. Which one is not the Core benefits of micro-partitioning

Snowflake micro-partitions are derived automatically they do not need to be explicitly defined upfront or maintained by users.

Enables extremely efficient DML and fine-grained pruning for faster queries.

Micro-partitions can overlap in their range of values, helps data skewing.

Columns are stored independently within micro-partitions, often referred to as columnar storage.

Columns are also compressed individually within micro-partitions.

Page 2 of 4

11. Jonas, a Lead Performance Engineer,identifed that some of the operation of his query which functionally 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?

Jonas can recommend using a large warehouse which effectively increase the available memory/local disk space for the operations.

He can Process data in smaller batches to manage workload.

Spilling do not have a profound effect on query performance (especially if remote disk is used for spilling).

Data Sharing can be helpful to improve query performance.

12. To support Time Travel, Which of the following SQL extensions/parameters/commands have been implemented?

AT | BEFORE clause which can be specified in the CREATE … CLONE commands.

ONSET (time difference in seconds from the present time)

OFFSET (time difference in seconds from the present time)

STATEMENT_ID (identifier for statement, e.g. query ID)

STATEMENT (identifier for statement, e.g. query ID)

UNDROP command for tables, schemas, and databases.

13. In one of your created Schema, you have been required to create Internal Stages, what are the Incorrect considerations you can noticed from the below options? [Select All that Apply]

User stages can be altered or dropped just like Table Stage.

Table stage type is designed to store files that are staged and managed by one or more users but only loaded into a single table.

A named internal stage type can store files that are staged and managed by one or more users and loaded into one or more tables.

A table stage is available for each table created in Snowflake.

14. You can execute zero, one, or more transactions inside a stored procedure?

TRUE

FALSE

15. While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. generated by UUID_STRING) instead of sequence-generated values?

TRUE (Correct)

FALSE

16. What are Invalid rules applicable when using stored procedure contains transaction?

A transaction can be inside a stored procedure, or a stored procedure can be inside a transaction.

You cannot start a transaction before calling the stored procedure, then complete the transaction inside the stored procedure.

You cannot start a transaction inside the stored procedure, then complete the transac-tion after returning from the procedure.

If a transaction is started inside a stored procedure and is still active when the stored procedure finishes, then an error occurs, and the transaction is rolled back.

A transaction inside a stored procedure can include a call to another stored procedure that contains a transaction.

All Rules are Applicable.

17. Which ones are the false statements about Materialized Views?

Snowflake does not allow standard DML (e.g. INSERT, UPDATE, DELETE) on materialized views.

Snowflake does not allow users to truncate materialized views.

Materialized views are first-class account objects.

A materialized view can also be used as the data source for a subquery.

Materialized views can be secure views.

Clustering a subset of the materialized views on a table tends to be more cost-effective than clustering the table itself.

18. Ira a Data Engineer with TESLA IT systems, looking out to Compare Traditional Partitioning vs Snowflake micro-partitions for one of the Snowflake Project implementations.

Which one of the following is incorrect understanding of Ira about Micro Partitioning?

All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage compared to traditional partitioning where specialized DDL required.

All DML operations (e.g. DELETE, UPDATE, MERGE) take advantage of the under-lying micro-partition metadata to facilitate and simplify table maintenance.

Snowflake stores metadata about all rows stored in a micro-partition, including number of distinct columns.

The micro-partition metadata maintained by Snowflake enables precise pruning of columns in micro-partitions at query run-time, including columns containing semi-structured data.

In Snowflake, as data is inserted/loaded into a table, clustering metadata is collected and recorded for each micro-partition created during the process.

19. Data Engineer looking out for quick tool for understanding the mechanics of queries & need to know more about the performance or behaviour of a particular query.

He should go to which feature of snowflake which can help him to spot typical mistakes in SQL query expressions to identify potential performance bottlenecks and improvement opportunities?

Query Optimizer

Performance Metadata table

Query Profile

Query Designer

20. print(cur.sfqid)

B. When he used the Snowflake Connector for Python to execute a query, he can access the query ID through the pyqueryid attribute in the Cursor object.

C. He needs to query history views to get the queryID as best practices.

D. Using python connector, snowflake does not support queryID retrieval for both syn-chronous & asynchronous query.

Page 3 of 4

21. 1.Streams cannot be created to query change data on which of the following objects? [Select All that Apply]

Standard tables, including shared tables.

Views, including secure views

Directory tables

Query Log Tables

External tables

22. file_format = (type = csv trim_space=true field_optionally_enclosed_by = '0x22');

23. PARTITION_TYPE = USER_SPECIFIED must be used when you prefer to add and remove partitions selectively rather than automatically adding partitions for all new files in an external storage location that match an expression?

TRUE

FALSE

24. Data Engineer, ran the below clustering depth analysis function:

select system$clustering_depth('TPCH_CUSTOMERS', '(C1, C6)', 'C9 = 30'); on TPCH_CUSTOMERS table, will return which of the following?

An error: this function does not accept lists of columns as a third parameter.

An error: this function does not accept predicates ('C9 = 30') as parameter.

Calculate the clustering depth for a table using mentioned columns in the table.

Calculate the clustering depth for a table using the clustering key defined for the table.

25. To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?

SYSTEM$CLUSTERING_DEPTH_KEY

SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)

SYSTEM$CLUSTERING_DEPTH

SYSTEM$CLUSTERING_INFORMATION (including clustering depth)

26. Which of the following statements is/are incorrect regarding Fail-safe data recovery?

Data stored in temporary tables is not recoverable after the table is dropped as they do not have fail-safe.

Historical data in transient tables can be recovered by Snowflake due to Operation failure after the Time Travel retention period ends using Fail-safe.

Long-lived tables, such as fact tables, should always be defined as permanent to ensure they are fully protected by Fail-safe.

Short-lived tables (i.e. <1 day), such as ETL work tables, can be defined as transient to eliminate Fail-safe costs.

If downtime and the time required to reload lost data are factors, permanent tables, even with their added Fail-safe costs, may offer a better overall solution than transient tables.

27. task_name=>'MYTASK'));

28. Dominic, a Data Engineer wants to resume the pipe named stalepipe3 which got stale after 14 days. To do the same, he called the SYSTEM$PIPE_FORCE_RESUME function

select system$pipe_force_resume('snowmydb.mysnowschema.stalepipe3','staleness_check_override');

Let’s say If the pipe is resumed 16 days after it was paused, what will happened to the event notification that were received on the first and second days after the pipe was paused?

Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused.

Pipe maintains Metadata history of files for 64 days, so in this scenarios Snowpipe processed all the event notifications that were received for 16 days or so.

Once the Pipe got stale, all the events got purged automatically & pipe needs to be recreated with modified properties.

All the events get processed from day 1 if the PURGE properties in the PIPE object definition set to be FALSE initially.

29. Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?

OPEARTE

MANAGE GRANTS

OWNERSHIP

MODIFY

30. Which Role that is dedicated to user and role management only?

ORGADMIN

SECURITYADMIN

USERADMIN

SYSADMIN

PUBLIC

Page 4 of 4

31. A SQL UDF evaluates an arbitrary SQL expression and returns the result(s) of the expression.

Which value type it can returns?

Single Value

A Set of Rows

Scaler or Tabular depend on input SQL expression

Regex

32. Data Engineer decided to call the public REST endpoints to load data and retrieve load history re-ports.

Which of the following REST endpoints and a Snowflake Information Schema table function for viewing your load history can be used by her? [Select All that apply]

REST endpoints: loadinsertReport or HistoryScan

Information Schema table function: COPY_HISTORY

Account Usage view:COPY_HISTORY

REST endpoints: insertReport or loadHistoryScan

Information Schema table function: LOAD_HISTORY

33. For enabling non-ACCOUNTADMIN Roles to Perform Data Sharing Tasks, which two glob-al/account privileges snowflake provide?

CREATE SHARE

IMPORT SHARE

REFERENCE USAGE

OPERATE

34. Robert, A Data Engineer, found that Pipe become stale as it was paused for longer than the limited retention period for event messages received for the pipe (14 days by default) & also the previous pipe owner transfers the ownership of this pipe to Robert role while the pipe was paused.

How Robert in this case, Resume this stale pipe?

PIPE needs to recreate in this scenario, as pipe already past 14 days of period & stale.

He can apply System function SYSTEM$PIPE_STALE_RESUME with ALTER PIPE statement.

Robert can use SYSTEM$PIPE_FORCE_RESUME function to resume this stale pipe.

select sys-tem$pipe_force_resume ('mydb.myschema.stalepipe','staleness_check_override, ownership_transfer_check_override');

ALTER PIPE

.. RESUME statement will resume the pipe.

35. Which Scenario Data engineer decide Materialized views are not useful. Select All that apply.

Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).

Query results contain results that require significant processing.

The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.

The view’s base table change frequently.

36. Which of the below concepts/functions helps while implementing advanced Column-level Security?

CURRENT_ROLE

INVOKER_ROLE

Role Hierarchy

CURRENT_CLIENT

37. Mark the correct statements about Cache?

Materialized views are more flexible than, but typically slower than, cached results.

Materialized views are faster than tables because of their “cache” (i.e. the query results for the view); in addition, if data has changed, they can use their “cache” for data that hasn’t changed and use the base table for any data that has changed.

For persisted query results of all sizes, the cache expires after 24 hours.

The size of the warehouse cache is determined by the compute resources in the ware-house.

Warehouse cache is dropped when the warehouse is suspended, which may result in slower initial performance for some queries after the warehouse is resumed.

38. Partition columns optimize query performance by pruning out the data files that do not need to be scanned (i.e. partitioning the external table).

Which pseudocolumn of External table evaluate as an expression that parses the path and/or filename information.

METADATA$ROW_NUMBER

METADATA$COLUMNNAME

METADATA$FILEPATH

METADATA$FILENAME

39. The smaller the average depth, the better clustered the table is with regards to the specified column?

TRUE

FALSE

40. Search optimization works best to improve the performance of a query when the following conditions are true: [Select All that apply]

The table is not clustered.

The table is frequently queried on columns other than the primary cluster key.

Search Query uses Equality predicates (for example, = ) OR Predicates that use I

Search Query uses Sort Operations.

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