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DEA-C01 New Test Camp, Official DEA-C01 Practice Test

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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

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

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 5

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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q80-Q85):

NEW QUESTION # 80
A company runs a multi-tenant Amazon EMR cluster on Amazon EC2 instances. Multiple teams perform interactive query analyses and data transformations on the data in the EMR cluster. The teams can access the cluster only through EMR Studio workspaces and EMR steps.
The teams need to use EMR steps to run Apache Spark jobs to fetch data from an Amazon DynamoDB table. The DynamoDB table contains confidential data that must be accessible to only one specific team. The company needs to ensure that only the appropriate team can access the confidential data in the EMR cluster. Which solution will meet these requirements?

A. Set up IAM roles for EMR File System (EMRFS) requests.
B. Set up AWS Lake Formation permissions.
C. Set up a DynamoDB resource-based policy.
D. Set up runtime roles for EMR steps.

Answer: D

Explanation:
Runtime roles for EMR steps allow each submitted step (such as a Spark job) to assume a distinct IAM role at execution time. By granting DynamoDB read permissions only to the role used by the authorized team's steps, the confidential table data becomes accessible only to that team's Spark jobs, while other teams' steps run with roles that lack access.

NEW QUESTION # 81
A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.
A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team's data warehouse. The operational Aurora database must not be affected.
Which solution will meet these requirements with the LEAST operational effort?

A. Use AWS Glue to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.
B. Use AWS Database Migration Service (AWS DMS) Serverless to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.
C. Use the Aurora zero-ETL integration with Amazon Redshift to replicate the Orders table. Create a materialized view in Amazon Redshift to join with the Campaigns table.
D. Use federated queries to query the Orders table directly from Aurora. Create a materialized view in Amazon Redshift to join with the Campaigns table.

Answer: C

Explanation:
Aurora's zero-ETL integration with Redshift automatically and continuously streams data changes from the Orders table into a Redshift table with virtually no setup or maintenance. You can then define a materialized view in Redshift to join that replicated Orders table with Campaigns. This approach ensures minimal impact on the production Aurora workload and requires far less operational effort than building and managing your own ETL or replication pipelines.

NEW QUESTION # 82
A company manages an Amazon Redshift data warehouse. The data warehouse is in a public subnet inside a custom VPC. A security group allows only traffic from within itself. An ACL is open to all traffic.
The company wants to generate several visualizations in Amazon QuickSight for an upcoming sales event. The company will run QuickSight Enterprise edition in a second AWS account inside a public subnet within a second custom VPC. The new public subnet has a security group that allows outbound traffic to the existing Redshift cluster.
A data engineer needs to establish connections between Amazon Redshift and QuickSight.
QuickSight must refresh dashboards by querying the Redshift cluster.
Which solution will meet these requirements?

A. Create a QuickSight gateway endpoint in the Redshift VPC. Attach an endpoint policy to the gateway endpoint to ensure only specific QuickSight accounts can use the endpoint.
B. Assign Elastic IP addresses to the QuickSight visualizations. Configure the QuickSight security group to allow inbound traffic on the Redshift port from the Elastic IP addresses.
C. Configure the Redshift security group to allow inbound traffic on the Redshift port from the QuickSight security group.
D. Confirm that the CIDR ranges of the Redshift VPC and the QuickSight VPC are the same. If CIDR ranges are different, reconfigure one CIDR range to match the other. Establish network peering between the VPCs.

Answer: C

Explanation:
Because your Redshift cluster is publicly accessible in its VPC and QuickSight runs in a separate VPC and account, you simply need to allow QuickSight's outbound connections through the Redshift security group. By adding an inbound rule on the Redshift SG that permits traffic on port
5439 (or your cluster's port) from the QuickSight security group, QuickSight can refresh dashboards without any additional infrastructure changes.

NEW QUESTION # 83
Data Engineer identified use case where he decided to use materialized view for query perfor-mance. Which one is not the limitation he must be aware of before using MVs in their use case?

A. Context Functions like CURRENT_TIME or CURRENT_TIMESTAMP is not per-mitted.
B. A materialized views does not support clustering.
C. You cannot directly clone a materialized view by using the CREATE MATERIAL-IZED VIEW ...
CLONE... command.
D. A materialized view cannot include HAVING clauses OR ORDER BY clause.
E. A materialized views cannot be created on Shared Data.
F. Time Travel is not currently supported on materialized views.
G. A materialized view can query only a single table & Joins, including self-joins, are not supported.
H. Truncating a materialized view is not supported.

Answer: B,D

Explanation:
Explanation
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
If you cluster both the materialized view(s) and the base table on which the materialized view(s) are defined, you can cluster the materialized view(s) on different columns from the columns used to cluster the base table.
You can create a materialized view on shared data.
Also You can use Snowflake's data sharing feature to share a materialized view.
Rest all are correct.

NEW QUESTION # 84
Mark the incorrect statement in case Data engineer using the COPY INTO <table> command to load data from files into Snowflake tables?

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

Answer: C

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 # 85
......

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