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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Available Languages:	English
Exam Price:	$375 USD
Real Exam Qty:	approximately 50 questions
Exam Duration:	90 minutes
Exam Format:	Multiple Choice, Multiple Select
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Certificate Validity Period:	2 years (recertification required)
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored exam (via Pearson VUE) or in-person testing center
Pre Condition:	SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential.
Official Syllabus URL:	https://www.snowflake.com/certification/

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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 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 3	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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q376-Q381):

NEW QUESTION # 376
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 keep working as expected
B. Reading from the traksactioks_3T>: stream will succeed for some time after the expected STALE_TIME.
C. TRANSACTIONS _STMwill be automatically renamedTRANSACTIONS _RAW_STM.
D. TRANSACTIONS _STMwill be stale and will need to be re-created

Answer: D

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 # 377
How can the following relational data be transformed into semi-structured data using the LEAST amount of operational overhead?

A. Use the PAESE_JSON function to produce a variant value
B. Use the to_json function
C. Use the TO_VARIANT function to convert each of the relational columns to VARIANT.
D. Use the OBJECT_CONSTRUCT function to return a Snowflake object

Answer: D

Explanation:
Explanation
This option is the best way to transform relational data into semi-structured data using the least amount of operational overhead. The OBJECT_CONSTRUCT function takes a variable number of key-value pairs as arguments and returns a Snowflake object, which is a variant type that can store JSON data. The function can be used to convert each row of relational data into a JSON object with the column names as keys and the column values as values.

NEW QUESTION # 378
A company runs multiple applications on AWS. The company configured each application to output logs. The company wants to query and visualize the application logs in near real time.
Which solution will meet these requirements?

A. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an Amazon S3 bucket. Create an AWS Lambda function that runs on a schedule to export the required log groups to the S3 bucket. Use Amazon Athena to query the log data in the S3 bucket.
B. Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data.
C. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data.
D. Update the application code to send the log data to Amazon QuickSight by using Super-fast, Parallel, In-memory Calculation Engine (SPICE).Create the required analyses and dashboards in QuickSight.

Answer: B

Explanation:
By streaming your CloudWatch Logs log groups directly into an Amazon OpenSearch Service domain via subscription filters, you get near-real-time indexing and can build Kibana dashboards for querying and visualization with virtually no batching delays. This fully managed pipeline minimizes custom glue code and meets your requirements with low operational overhead.

NEW QUESTION # 379
Mark the incorrect statement in case Data engineer using the COPY INTO <table> command to load data from files into Snowflake tables?

A. For Data loading of files with semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-16.
B. For loading data from all semi-structured supported file formats (JSON, Avro, etc.), as well as unloading data, UTF-8 is the only supported character set.
C. For Local environment, Files are first copied ("staged") to an internal (Snowflake) stage, then loaded into a table.
D. UTF-32 & UTF-16 both encoding character sets supported for loading data from de-limited files (CSV, TSV, etc.)

Answer: A

Explanation:
Explanation
For Data Loading of delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets, you must explicitly specify the encoding to use for loading.
For semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-8.
Rest of the statements are correct.

NEW QUESTION # 380
A Data Engineer needs to ingest invoice data in PDF format into Snowflake so that the data can be queried and used in a forecasting solution.
..... recommended way to ingest this data?

A. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
B. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data
C. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
D. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table

Answer: B

Explanation:
Explanation
The recommended way to ingest invoice data in PDF format into Snowflake is to create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data. This option allows for more flexibility and control over how the PDF data is extracted and transformed. The other options are not suitable for ingesting PDF data into Snowflake. Option A and B are incorrect because Snowpipe and COPY INTO commands can only ingest files that are in supported file formats, such as CSV, JSON, XML, etc. PDF files are not supported by Snowflake and will cause errors or unexpected results.
Option C is incorrect because external tables can only query files that are in supported file formats as well.
PDF files cannot be parsed by external tables and will cause errors or unexpected results.

NEW QUESTION # 381
......

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