Snowflake DEA-C01 PDF Dumps Format

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q366-Q371):

NEW QUESTION # 366
An online retailer uses multiple delivery partners to deliver products to customers. The delivery partners send order summaries to the retailer. The retailer stores the order summaries in Amazon S3.
Some of the order summaries contain personally identifiable information (PII) about customers. A data engineer needs to detect PII in the order summaries so the company can redact the PII.
Which solution will meet these requirements with the LEAST operational overhead?

A. Amazon Textract
B. Amazon SageMaker Data Wrangler
C. Amazon Macie
D. Amazon S3 Storage Lens

Answer: C

Explanation:
Amazon Macie is a fully managed data security and privacy service that uses machine learning and pattern matching to discover and protect sensitive data, such as Personally Identifiable Information (PII), stored in Amazon S3. Macie can automatically scan the order summaries for PII, enabling the company to detect and then redact PII as required.

NEW QUESTION # 367
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?

A. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table
B. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
C. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage
D. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.

Answer: D

Explanation:
Explanation
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.

NEW QUESTION # 368
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?

A. eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.
B. Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.
C. Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.
D. Snowpipe uses file loading metadata associated with each pipe object to prevent reload-ing the same files (and duplicating data) in a table.

Answer: D

Explanation:
Explanation
Snowflake uses file loading metadata to prevent reloading the same files (and duplicating data) in a table.
Snowpipe prevents loading files with the same name even if they were later modified (i.e. have a different eTag).
The file loading metadata is associated with the pipe object rather than the table. As a result:
Staged files with the same name as files that were already loaded are ignored, even if they have been modified, e.g. if new rows were added or errors in the file were corrected.
Truncating the table using the TRUNCATE TABLE command does not delete the Snowpipe file loading metadata.

NEW QUESTION # 369
A data engineer maintains a materialized view that is based on an Amazon Redshift database.
The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?

A. TRUNCATE materialized_view_name
B. DELETE FROM materialized_view_name where 1=1
C. VACUUM table_name where load_date<=current_date materializedview
D. DELETE FROM materialized_view_name where load_date<=current_date

Answer: A

NEW QUESTION # 370
A stream called TRANSACTIONS_STM is created on top of a transactions table in a continuous pipeline running in Snowflake. After a couple of months, the TRANSACTIONS table is renamed transactiok3_raw to comply with new naming standards What will happen to the TRANSACTIONS _STM object?

A. TRANSACTIONS _STMwill be automatically renamedTRANSACTIONS _RAW_STM.
B. TRANSACTIONS _STMwill be stale and will need to be re-created
C. Reading from the traksactioks_3T>: stream will succeed for some time after the expected STALE_TIME.
D. TRANSACTIONS _STMwill keep working as expected

Answer: B

Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream is associated with a specific table at the time of creation, and it cannot be altered to point to a different table later. Therefore, if the source table is renamed, the stream will become stale and will need to be re-created with the new table name. The other options are not correct because:
TRANSACTIONS _STM will not keep working as expected, as it will lose track of the changes made to the renamed table.
TRANSACTIONS _STM will not be automatically renamed TRANSACTIONS _RAW_STM, as streams do not inherit the name changes of their source tables.
Reading from the transactions_stm stream will not succeed for some time after the expected STALE_TIME, as streams do not have a STALE_TIME property.

NEW QUESTION # 371
......

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