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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	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	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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
A finance company uses Amazon Redshift as a data warehouse. The company stores the data in a shared Amazon S3 bucket. The company uses Amazon Redshift Spectrum to access the data that is stored in the S3 bucket. The data comes from certified third-party data providers. Each third-party data provider has unique connection details.
To comply with regulations, the company must ensure that none of the data is accessible from outside the company's AWS environment.
Which combination of steps should the company take to meet these requirements? (Choose two.)

A. Create an AWS CloudHSM hardware security module (HSM) for each data provider. Encrypt each data provider's data by using the corresponding HSM for each data provider.
B. Replace the existing Redshift cluster with a new Redshift cluster that is in a private subnet. Use an interface VPC endpoint to connect to the Redshift cluster. Use a NAT gateway to give Redshift access to the S3 bucket.
C. Use federated queries to access the data from each data provider. Do not upload the data to the S3 bucket. Perform the federated queries through a gateway VPC endpoint.
D. Turn on enhanced VPC routing for the Amazon Redshift cluster. Set up an AWS Direct Connect connection and configure a connection between each data provider and the finance company's VPC.
E. Define table constraints for the primary keys and the foreign keys.

Answer: B,D

NEW QUESTION # 71
A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
B. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
C. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.
D. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.

Answer: C

Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/cbac-tutorial.html

NEW QUESTION # 72
David, a Lead Data engineer with XYZ company looking out to improve query performance & oth-er benefits while working with Tables, Regular Views, MVs and Cached Results.
Which one of the following does not shows key similarities and differences between tables, regular views, cached query results, and materialized views while choosing any of them by David?

A. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.
B. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
C. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
D. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
E. Regular views do not cache data, and therefore cannot improve performance by cach-ing.

Answer: A

Explanation:
Explanation
Materialized Views, like other database objects (tables, views, UDFs, etc.), are owned by a role and have privileges that can be granted to other roles.
You can grant the following privileges on a materialized view:
SELECT
As with non-materialized views, a materialized view does not automatically inherit the privileges of its base table. You should explicitly grant privileges on the materialized view to the roles that should use that view.
As with non-materialized views, a user who wishes to access a materialized view needs privileges only on the view, not on the underlying object(s) that the view references.
Rest is correct.

NEW QUESTION # 73
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 persistent application UI to see the first YARN container log in the Spark UI.
B. The engineer can connect to the primary node of the cluster by using SSH to see the Spark history server logs.
C. The engineer can connect to the web UI on the live cluster to see the YARN ResourceManager logs.
D. The engineer can connect to the Amazon EMR console to see the Amazon EMR step logs that are archived in Amazon S3.

Answer: D

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 # 74
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. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
C. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
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 # 75
......

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