DEA-C01 Practice Dumps - Verified By FreePdfDump Updated 132 Questions [Q14-Q37]

DEA-C01 Practice Dumps - Verified By FreePdfDump Updated 132 Questions

Updated DEA-C01 Exam Dumps - PDF Questions and Testing Engine

NEW QUESTION # 14
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. He can Process data in smaller batches to manage workload.
• B. Spilling do not have a profound effect on query performance (especially if remote disk is used for spilling).
• C. Data Sharing can be helpful to improve query performance.
• D. Jonas can recommend using a large warehouse which effectively increase the available memory/local disk space for the operations.

Answer: A,D

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 # 15
Which one is not the Core benefits of micro-partitioning

• A. Snowflake micro-partitions are derived automatically they do not need to be explicitly defined up-front or maintained by users.
• B. Columns are also compressed individually within micro-partitions.
• C. Enables extremely efficient DML and fine-grained pruning for faster queries.
• D. Columns are stored independently within micro-partitions, often referred to as colum-nar storage.
• E. Micro-partitions can overlap in their range of values, helps data skewing.

Answer: E

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 # 16
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. I
• B. III
• C. IV
• 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 # 17
Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?

• A. Python Connector
• B. Spark Connector
• C. Kafka Connector
• D. Node.js connector

Answer: C

Explanation:
Explanation
Apache Kafka software uses a publish and subscribe model to write and read streams of records, similar to a message queue or enterprise messaging system. Kafka allows processes to read and write messages asynchronously. A subscriber does not need to be connected directly to a publisher; a pub-lisher can queue a message in Kafka for the subscriber to receive later.
An application publishes messages to a topic, and an application subscribes to a topic to receive those messages. Kafka can process, as well as transmit, messages; however, that is outside the scope of this document. Topics can be divided into partitions to increase scalability.
Kafka Connect is a framework for connecting Kafka with external systems, including databases. A Kafka Connect cluster is a separate cluster from the Kafka cluster. The Kafka Connect cluster sup-ports running and scaling out connectors (components that support reading and/or writing between external systems).
The Kafka connector is designed to run in a Kafka Connect cluster to read data from Kafka topics and write the data into Snowflake tables.
Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns:
RECORD_CONTENT. This contains the Kafka message.
RECORD_METADATA. This contains metadata about the message, for example, the topic from which the message was read.

NEW QUESTION # 18
Pascal, a Data Engineer, have requirement to retrieve the 10 most recent executions of a specified task (completed, still running, or scheduled in the future) scheduled within the last hour, which of the following is the correct SQL Code ?

• A. 1.select *
  2.from table(information_schema.task_history(
  3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
  4.result_limit => 10,
  5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
• B. 1.select *
  2.from table(information_schema.task_history(
  3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
  4.result_limit => 10,
  5.task_name=>'MYTASK'));
• C. 1.select *
  2.from table(information_schema.task_history(
  3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
  4.result_limit => 11,
  5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
• D. 1.select *
  2.from table(information_schema.task_history(
  3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
  4.result_limit => 10,query_id IS NOT NULL
  5.task_name=>'MYTASK'));

Answer: B

Explanation:
Explanation
To retrieve only tasks that are completed or still running, filter the query using WHERE query_id IS NOT NULL.

NEW QUESTION # 19
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions?

• A. FALSE
• B. TRUE

Answer: B

Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions. The reason is that the owner of the data being shared does not typically control the users or roles in the account with which the view is being shared.

NEW QUESTION # 20
Search optimization works best to improve the performance of a query when the following condi-tions are true:[Select All that apply]

• A. Search Query uses Equality predicates (for example, <column_name> = <constant>) OR Predicates that use IN.
• B. The table is frequently queried on columns other than the primary cluster key.
• C. The table is not clustered.
• D. Search Query uses Sort Operations.

Answer: A,B,C

Explanation:
Explanation
Materialized Views works best for search query performance in case of Sort Operations. For Rest of the points Search optimization works best to improve query performance.

NEW QUESTION # 21
If you need to connect to Snowflake using a BI tool or technology, which of the following BI tools and technologies are known to provide native connectivity to Snowflake?

• A. PROTEGRITY
• B. SELECT STAR
• C. SISENSE
• D. ALATION

Answer: C

Explanation:
Explanation
SISENSE is BI tools and technologies which is known to provide native connectivity to Snowflake, Rest of the options given are security & governance tools supported by SnowFlake.
Business intelligence (BI) tools enable analyzing, discovering, and reporting on data to help execu-tives and managers make more informed business decisions. A key component of any BI tool is the ability to deliver data visualization through dashboards, charts, and other graphical output.
For More details around supported BI Tools in Snowflake Ecosystem, do refer the link below:
https://docs.snowflake.com/en/user-guide/ecosystem-bi

NEW QUESTION # 22
If the data retention period for a table is less than 90 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale?

• A. FALSE
• B. TRUE

Answer: A

Explanation:
Explanation
If the data retention period for a table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale. The period is extended to the stream's offset, up to a maximum of 14 days by default, regardless of the Snowflake edition for your account. The maximum number of days for which Snowflake can extend the data retention period is determined by the MAX_DATA_EXTENSION_TIME_IN_DAYS parameter value. When the stream is consumed, the extended data retention period is reduced to the default period for the table.

NEW QUESTION # 23
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. AZURE
• B. GCP
• C. AWS
• D. None of the Above currently provide cross cloud support for Snowpipe.

Answer: C

Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.

NEW QUESTION # 24
Michael, a Data Engineer Running a Data query to achieve Union of Data sets coming from Multi-ple data sources, later he figured out that Data processing query is taking more time than expected. He started analyzing the Query performance using query profile interface. He discovered & realized that he used UNION when the UNION ALL semantics was sufficient.
Which Extra Data Processing Operator Michael figured out while doing query profile analysis in this case which helps him to identify this performance bottlenecks?

• A. Flatten
• B. Join
• C. Aggregate
• D. UNION ALL
• E. Filter

Answer: C

Explanation:
Explanation
In SQL, it is possible to combine two sets of data with either UNION or UNION ALL constructs. The difference between them is that UNION ALL simply concatenates inputs, while UNION does the same, but also performs duplicate elimination.
A common mistake is to use UNION when the UNION ALL semantics are sufficient. These que-ries show in Query Profile as a UnionAll operator with an extra Aggregate operator on top (which performs duplicate elimination).
To Know more about Data Processing Operators, please do refer:
https://docs.snowflake.com/en/user-guide/ui-query-profile#operator-types

NEW QUESTION # 25
Mark a Data Engineer, looking to implement streams on local views & want to use change tracking metadata for one of its Data Loading use case. Please select the incorrect understanding points of Mark with respect to usage of Streams on Views?

• A. For streams on views, change tracking must be enabled explicitly for the view and un-derlying tables to add the hidden columns to these tables.
• B. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table.
• C. As an alternative to streams, Snowflake supports querying change tracking metadata for views using the CHANGES clause for SELECT statements.
• D. Enabling change tracking adds a pair of hidden columns to the table and begins storing change tracking metadata. The values in these hidden CDC data columns provide the input for the stream metadata columns. The columns consume a small amount of stor-age.
• E. Views with GROUP BY & LIMIT Clause are supported by Snowflake.

Answer: E

Explanation:
Explanation
A stream object records data manipulation language (DML) changes made to tables, including in-serts, updates, and deletes, as well as metadata about each change, so that actions can be taken us-ing the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at the row level, between two transac-tional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
Standard tables, including shared tables.
Views, including secure views
Directory tables
External tables
When created, a stream logically takes an initial snapshot of every row in the source object (e.g. ta-ble, external table, or the underlying tables for a view) by initializing a point in time (called an off-set) as the current transactional version of the object. The change tracking system utilized by the stream then records information about the DML changes after this snapshot was taken. Change rec-ords provide the state of a row before and after the change. Change information mirrors the column structure of the tracked source object and includes additional metadata columns that describe each change event.
Note that a stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, a pair of hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables.
Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views.
Views with the following operations are not yet supported:
GROUP BY clauses
QUALIFY clauses
Subqueries not in the FROM clause
Correlated subqueries
LIMIT clauses
Change Tracking:
Change tracking must be enabled in the underlying tables.
Prior to creating a stream on a view, you must enable change tracking on the underlying tables for the view.
Set the CHANGE_TRACKING parameter when creating a view (using CREATE VIEW) or later (using ALTER VIEW).
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.

NEW QUESTION # 26
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_ERROR_MODE copy option set to RE-TURN_ALL_PIPE_ERRORS.
• B. 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.
• C. 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.
• 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: B

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 # 27
Mark the Correct Statements:
Statement 1. Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database.
Statement 2. Data Engineer can use zero-copy cloning feature for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).

• A. Both are False.
• B. Statement 2
• C. Statement 1 & 2 are correct.
• D. Statement 1

Answer: A

Explanation:
Explanation
Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database and create a derived copy of that object which initially shares the underlying storage. This can be extremely useful for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).
For example, when a clone is created of a table, the clone utilizes no data storage because it shares all the existing micro-partitions of the original table at the time it was cloned; however, rows can then be added, deleted, or updated in the clone independently from the original table. Each change to the clone results in new micro-partitions that are owned exclusively by the clone and are protect-ed through CDP.

NEW QUESTION # 28
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.

• A. METADATA$COLUMNNAME
• B. METADATA$FILEPATH
• C. METADATA$ROW_NUMBER
• D. METADATA$FILENAME

Answer: D

Explanation:
Explanation
METADATA$FILENAME
A pseudocolumn that identifies the name of each staged data file included in the external table, in-cluding its path in the stage.
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn. A partition consists of all data files that match the path and/or filename in the expression for the parti-tion column.

NEW QUESTION # 29
Which is the non-supportable JavaScript UDF data types?

• A. Integers
• B. String
• C. Binary
• D. Double

Answer: A

NEW QUESTION # 30
Select the incorrect statements regarding Clustering depth?

• A. 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.
• 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. Clustering depth can be used for determining whether a large table would benefit from explicitly defining a clustering key.

Answer: C

Explanation:
Explanation
A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 0.

NEW QUESTION # 31
Mark the Incorrect Statements with respect to types of streams supported by Snowflake?

• A. An insert-only stream tracks row inserts & Delete ops only
• B. 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).
• C. Insert-only Stream supported on external tables only.
• D. Standard streams cannot retrieve update data for geospatial data.

Answer: A

Explanation:
Explanation
Standard Stream:
Supported for streams on tables, directory tables, or views. A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, updates, and deletes (including table trun-cates). This stream type performs a join on inserted and deleted rows in the change set to provide the row level delta. As a net effect, for example, a row that is inserted and then deleted between two transactional points of time in a table is removed in the delta (i.e. is not returned when the stream is queried).
Append-only Stream:
Supported for streams on standard tables, directory tables, or views. An append-only stream tracks row inserts only. Update and delete operations (including table truncates) are not recorded. For ex-ample, if 10 rows are inserted into a table and then 5 of those rows are deleted before the offset for an append-only stream is advanced, the stream records 10 rows.
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) and similar scenarios that depend exclu-sively on row inserts. For example, a source table can be truncated immediately after the rows in an append-only stream are consumed, and the record deletions do not contribute to the overhead the next time the stream is queried or consumed.
Insert-only Stream:
Supported for streams on external tables only. An insert-only stream tracks row inserts only; they do not record delete operations that remove rows from an inserted set (i.e. no-ops). For example, in-between any two offsets, if File1 is removed from the cloud storage location referenced by the ex-ternal table, and File2 is added, the stream returns records for the rows in File2 only. Unlike when tracking CDC data for standard tables, Snowflake cannot access the historical records for files in cloud storage.

NEW QUESTION # 32
When created, a stream logically takes an initial snapshot of every row in the source object and the contents of a stream change as DML statements execute on the source table.
A Data Engineer, Sophie Created a view that queries the table and returns the CURRENT_USER and CURRENT_TIMESTAMP values for the query transaction. A Stream has been created on views to capture CDC.
Tony, another user inserted the data e.g.
insert into <table> values (1),(2),(3);
Emily, another user also inserted the data e.g.
insert into <table> values (4),(5),(6);
What will happened when Different user queries the same stream after 1 hour?

• A. All the Six Records would be displayed with CURRENT_USER & CUR-RENT_TIMESTAMP while querying Streams.
• B. All the 6 records would be shown with METADATA$ACTION as 'INSERT' out of which 3 records would be displayed with username 'Tony' & rest 3 records would be displayed with username 'Emily'.
• C. All the Six records would be displayed with User 'Sohpie' Who is the owner of the View.
• D. User would be displayed with the one who queried during the session, but Recorded timestamp would be of past 1 hour i.e. actual records insertion time.

Answer: A

Explanation:
Explanation
When User queries the stream, the stream returns the username for the user. The stream also returns the current timestamp for the query transaction in each row, NOT the timestamp when each row was inserted.

NEW QUESTION # 33
Which of the following security and governance tools/technologies are known to provide native connectivity to Snowflake? [Select 2]

• A. Baffle
• B. ALTR
• C. Zepl
• D. BIG Squid
• E. Dataiku

Answer: A,B

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 # 34
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?

• A. Since the initial set of data for the table (i.e. the first batch after the table was created) was loaded, we can simply use the COPY INTO command to load all the product files with the known load status irrespective of their column LAST_MODIFIED date values.
• B. To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true.
• C. The COPY command cannot definitively determine whether a file has been loaded al-ready if the LAST_MODIFIED date is older than 64 days and the initial set of data was loaded into the table more than 64 days earlier (and if the file was loaded into the table, that also occurred more than 64 days earlier). In this case, to prevent accidental reload, the command skips the product files by default.
• D. Set the FORCE option to load all files, ignoring load metadata if it exists.

Answer: B

Explanation:
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files

NEW QUESTION # 35
Which UDF programming language is not supported with Snowflake Secure Data Sharing feature?

• A. PYTHON
• B. JAVA
• C. SQL
• D. JAVASCRIPT

Answer: D

NEW QUESTION # 36
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?

• A. The micro-partition metadata maintained by Snowflake enables precise pruning of col-umns in micro-partitions at query run-time, including columns containing semi-structured data.
• B. In Snowflake, as data is inserted/loaded into a table, clustering metadata is collected and recorded for each micro-partition created during the process.
• C. Snowflake stores metadata about all rows stored in a micro-partition, including number of distinct columns.
• D. All DML operations (e.g. DELETE, UPDATE, MERGE) take advantage of the under-lying micro-partition metadata to facilitate and simplify table maintenance.
• E. 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.

Answer: C

Explanation:
Explanation
What are Micro-partitions?
All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage. Each micro-partition contains between 50 MB and 500 MB of uncompressed data (note that the actual size in Snowflake is smaller because data is always stored compressed). Groups of rows in tables are mapped into individual micro-partitions, organized in a columnar fashion. This size and structure allow for extremely granular pruning of very large tables, which can be comprised of millions, or even hundreds of millions, of micro-partitions.
Snowflake stores metadata about all rows stored in a micro-partition, including:
The range of values for each of the columns in the micro-partition.
The number of distinct values.
Additional properties used for both optimization and efficient query processing.
It Never stores number of columns as part of Metadata.
Rest of the statements are correct.

NEW QUESTION # 37
......

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