Simulation DEA-C01 Questions | DEA-C01 Valid Test Notes

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 2	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
Topic 3	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 4	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 5	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.

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DEA-C01 Valid Test Notes, DEA-C01 Fresh Dumps

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
Mark the Incorrect Statements with respect to types of streams supported by Snowflake?

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

Answer: B

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 # 18
Select the Correct statements with regard to using Federated authentication/SSO?

A. Snowflake supports multiple audience values (i.e. Audience or Audience Restriction Fields) in the SAML 2.0 assertion from the identity provider to Snowflake.
B. Snowflake supports using SSO with organizations, and you can use the corresponding URL in the SAML2 security integration.
C. Snowflake supports SSO with Private Connectivity to the Snowflake Service for Snow-flake accounts on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.
D. Snowflake supports using MFA in conjunction with SSO to provide additional levels of security.

Answer: A,B,C,D

NEW QUESTION # 19
If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]

A. field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.
B. Snowflake reads the leading space rather than the opening quotation character as the beginning of the field and the quotation characters are interpreted as string data.
(Correct)
C. COPY command trims the leading space and removes the quotation marks enclosing each field
1.copy into SFtable
2.from @%SFtable
3.file_format = (type = csv trim_space=true field_optionally_enclosed_by = '0x22');
D. Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.

Answer: C

Explanation:
Explanation
If your external software exports fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, Snowflake reads the leading space rather than the open-ing quotation character as the beginning of the field. The quotation characters are interpreted as string data.
Use the TRIM_SPACE file format option to remove undesirable spaces during the data load.

NEW QUESTION # 20
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?

A. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
B. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
C. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
D. Use Amazon Athena Federated Query to join the data from all data sources.

Answer: D

Explanation:
You can query these sources by using Federated Queries, which is a native feature of Athena.
The other options may increase costs and operational overhead, as they use more than one service to achieve the same result.
https://docs.aws.amazon.com/athena/latest/ug/connectors-available.html

NEW QUESTION # 21
A SQL UDF evaluates an arbitrary SQL expression and returns the result(s) of the expression. Which value type it can returns?

A. Scaler or Tabular depend on input SQL expression
B. Regex
C. A Set of Rows
D. Single Value

Answer: A

NEW QUESTION # 22
......

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SIMULATION DEA-C01 QUESTIONS | DEA-C01 VALID TEST NOTES

Simulation DEA-C01 Questions | DEA-C01 Valid Test Notes