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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Real Exam Qty:	approximately 50 questions
Available Languages:	English
Exam Format:	Multiple Choice, Multiple Select
Exam Duration:	90 minutes
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Certificate Validity Period:	2 years (recertification required)
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Exam Price:	$375 USD
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	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 2	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 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	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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?

A. Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.
B. Transition the data to Amazon S3 Glacier Flexible Retrieval.
C. Transition the data from Amazon S3 to an Amazon Aurora cluster.
D. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.

Answer: D

Explanation:
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.

NEW QUESTION # 44
A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format:
s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?

A. Run the MSCK REPAIR TABLE command from the AWS Glue console.
B. Manually run the AWS Glue CreatePartition API twice each day.
C. Schedule an AWS Glue crawler to run every morning.
D. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.

Answer: D

Explanation:
Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.
This approach ensures that the Data Catalog is updated as soon as new data is written to S3, providing the least latency in reflecting new partitions.

NEW QUESTION # 45
Data Engineer is performing below steps in sequence while working on Stream s1 created on table t1.
Step 1: Begin transaction.
Step 2: Query stream s1 on table t1.
Step 3: Update rows in table t1.
Step 4: Query stream s1.
Step 5: Commit transaction.
Step 6: Begin transaction.
Step 7: Query stream s1.
Mark the Incorrect Operational statements:

A. if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the be-ginning time of Transaction 2 and would not see any changes committed by Transac-tion 1.
B. For Step 7, Results do include table changes committed by Transaction 1.
C. For Step 5, If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.
D. For Step 4, Returns the CDC data records by streams with updated rows happened in the Step 3 because Streams works in Repeated committed mode in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
E. For Step 2, The stream returns the change data capture records between the current position to the Transaction 1 start time. If the stream is used in a DML statement, the stream is then locked to avoid changes by concurrent transactions.

Answer: D

Explanation:
Explanation
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise, it stays at the same position.
Within Transaction 1, all queries to stream s1 see the same set of records. DML changes to table t1 are recorded to the stream only when the transaction is committed.
In Transaction 2, queries to the stream see the changes recorded to the table in Transaction 1. Note that if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the beginning time of Transaction 2 and would not see any changes committed by Transaction 1.

NEW QUESTION # 46
A company has implemented a lake house architecture in Amazon Redshift. The company needs to give users the ability to authenticate into Redshift query editor by using a third-party identity provider (IdP).
A data engineer must set up the authentication mechanism.
What is the first step the data engineer should take to meet this requirement?

A. Register the third-party IdP as an identity provider in the configuration settings of the Redshift cluster.
B. Register the third-party IdP as an identity provider from within Amazon Redshift.
C. Register the third-party IdP as an identity provider for AWS Certificate Manager (ACM). Configure Amazon Redshift to use ACM to manage user credentials.
D. Register the third-party IdP as an identity provider for AVS Secrets Manager. Configure Amazon Redshift to use Secrets Manager to manage user credentials.

Answer: A

Explanation:
To enable users to authenticate into the Amazon Redshift query editor using a third-party identity provider (IdP), the data engineer must first register that IdP within the configuration settings of the Redshift cluster itself.
Amazon Redshift natively supports integrating with external identity providers to manage user authentication. By registering the third-party IdP directly in the Redshift cluster settings, it establishes the trust relationship needed for Redshift to rely on that IdP for authenticating users when they log into the query editor.

NEW QUESTION # 47
An ecommerce company collects daily customer transaction logs in CSV format and stores the logs in Amazon S3. The company uses Amazon Athena to scan a subset of attributes from the logs on the same day the company receives each log.
Query times are increasing because of increasing transaction volume. The company wants to improve query performance.
Which solution will meet these requirements with the SHORTEST query times?

A. Convert the CSV logs into multiple ORC files for better parallelism in Athena. Partition by date in Amazon S3. Use columnar pushdown filters.
B. Convert the CSV logs to Avro. Partition by date in Amazon S3. Use Athena with projection-based partitioning.
C. Convert the CSV logs to a single Apache Parquet file for each day Partition the data by date in Amazon S3. Use Athena with predicate pushdown filters.
D. Convert the CSV logs to JSON. Partition by date in Amazon S3. Use Athena with dynamic filtering to reduce data scans.

Answer: C

Explanation:
Partitioning by date limits each query to only the relevant day's data, and Parquet enables efficient columnar reads and predicate pushdown for scanning only the needed columns and row groups. Writing a single Parquet file per day minimizes file listing and small-file overhead, typically delivering the shortest Athena query times for same-day, subset-column analytics.

NEW QUESTION # 48
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