Quiz Snowflake - DEA-C01 - SnowPro Advanced: Data Engineer Certification Exam Pass-Sure Practice Test

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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	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	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.
Topic 5	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.

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Most Recent Snowflake DEA-C01 Exam Questions – Verified By Snowflake Experts

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q334-Q339):

NEW QUESTION # 334
A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.complete_orders_history, which contains 100 columns. The query must return all columns except columns named companyId and unique_system_id. Which Amazon Redshift SQL statement will meet this requirement?

Answer: C

Explanation:
Amazon Redshift supports the SELECT * EXCLUDE syntax, which allows returning all columns from a table except the specified ones. This is the most efficient and direct way to exclude specific columns like company_id and unique_system_id without listing all remaining columns.

NEW QUESTION # 335
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. 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)
B. Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.
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. field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.

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 # 336
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?

A. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.
B. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

Answer: D

Explanation:
Amazon Managed Service for Apache Flink provides real-time stream processing capabilities, which can process sensor data with low latency. By using Apache Flink connectors, the processed data can be efficiently written to Amazon Timestream, which is optimized for time- series data storage and querying.

NEW QUESTION # 337
A company uses Amazon Redshift to store order transactions from the current day. The company has an orders table that contains the previous order data. The company also has a staging table that contains new or updated order records.
The company needs to remove stale records from the orders table and insert the most recent data in the orders table from the staging table. Several downstream applications need the orders table to display up-to-date information.
Which solution will meet these requirements?

A. Unload the orders table and the staging table to Amazon S3. Delete stale orders table data and insert new staging table data in Amazon S3 by using Amazon Athena. Copy the orders S3 table to the orders Amazon Redshift table.
B. Write an Amazon Redshift stored procedure that deletes the stale records from the orders table and inserts new records from the staging table.
C. Use Amazon Redshift Spectrum to delete stale records from the orders table and insert records from the staging table into the orders table.
D. Use Amazon Athena federated queries to read stale records from the orders table. Delete the stale records and insert the records from the staging table into the orders table.

Answer: B

Explanation:
An Amazon Redshift stored procedure can perform the delete-and-insert logic directly inside the warehouse in a single controlled operation. This is the most appropriate way to remove stale rows from the target table and load the latest records from the staging table while keeping the orders table current for downstream applications, without introducing unnecessary data movement or external processing.

NEW QUESTION # 338
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)

A. Use AWS Lake Formation for centralized data governance and access control.
B. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
C. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
D. Use AWS Glue DataBrew for centralized data governance and access control.
E. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.

Answer: A,C

NEW QUESTION # 339
......

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