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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Passing Score:	750 / 1000
Exam Price:	$375 USD
Available Languages:	English, Japanese
Related Certifications:	SnowPro Advanced: Administrator SnowPro Advanced: Security Engineer SnowPro Core SnowPro Advanced: Data Analyst SnowPro Advanced: Data Scientist SnowPro Advanced: Architect
Exam Duration:	115 minutes
Real Exam Qty:	65
Exam Format:	Multiple select, Multiple choice
Certificate Validity Period:	2 years
Recommended Training:	Snowflake University Training Courses SnowPro Advanced: Data Engineer Exam Study Guide
Exam Registration:	Pearson VUE Registration Snowflake Certification Portal
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE test centers
Pre Condition:	Must hold active SnowPro Core Certification; recommended 2+ years hands-on data engineering experience
Official Syllabus URL:	https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q337-Q342):

NEW QUESTION # 337
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. 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.
D. All the Six records would be displayed with User 'Sohpie' Who is the owner of the View.

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 # 338
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 # 339
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records.
The query becomes slow after the modification. A data engineer must improve the query performance.
Which solutions will meet these requirements? (Choose two.)

A. Use Amazon Redshift Advisor to review and select optimizations to implement.
B. Use the Amazon Redshift query optimizer to review and select optimizations to implement.
C. Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.
D. Use the KEY distribution style for both sales tables. Select a high cardinality column to use for the join.
E. Use the KEY distribution style for both sales tables. Select a low cardinality column to use for the join.

Answer: A,D

Explanation:
Choosing KEY distribution on both tables with a high-cardinality join column colocates matching rows across nodes and avoids data skew, improving join performance.
Redshift Advisor provides automated, actionable recommendations (e.g., distribution and sort keys, stats) to further optimize the slow query with minimal effort.

NEW QUESTION # 340
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. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
B. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
C. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
D. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data

Answer: D

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 # 341
A company runs an Apache Spark application every night in an Amazon EMR cluster. The company uses Amazon EC2 instances to supply compute capacity for the EMR cluster. The company deployed the Spark application in cluster mode.
An error occurs in the Spark application. A log for the error is stored in the application's Spark driver standard error logs. A data engineer needs to investigate the error.
Where can the data engineer find this error log?

A. The engineer can connect to the web UI on the live cluster to see the YARN ResourceManager logs.
B. The engineer can connect to the Amazon EMR console to see the Amazon EMR step logs that are archived in Amazon S3.
C. The engineer can connect to the primary node of the cluster by using SSH to see the Spark history server logs.
D. The engineer can connect to the persistent application UI to see the first YARN container log in the Spark UI.

Answer: B

Explanation:
In EMR cluster mode, the Spark driver runs inside the cluster and its standard error logs are captured as step logs. These logs are automatically archived to Amazon S3 and accessible from the Amazon EMR console under step logs. This is the correct location for investigating Spark driver errors.

NEW QUESTION # 342
......

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