Practice DEA-C01 Exams - DEA-C01 New Soft Simulations

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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Pipeline Orchestration & Automation	20%	- Error handling, retry logic, and dependency management - Integrate with external orchestration tools - Build, schedule, monitor, and manage workflows
Topic 2: Data Transformation & Processing	30%	- Work with semi-structured and unstructured data - Implement data quality, validation, and schema evolution - Use streams, tasks, dynamic tables for pipelines and CDC - Design and implement transformations: SQL, stored procedures, user-defined functions
Topic 3: Performance Optimization & Scalability	15%	- Monitor and tune workloads and resource usage - Optimize warehouses, clustering, partitioning, and query performance - Design scalable compute and storage solutions
Topic 4: Data Governance, Security & Sharing	10%	- Apply access control, masking, encryption, and compliance - Share data securely across accounts and platforms - Track lineage, audit, and maintain data integrity
Topic 5: Data Ingestion	25%	- Handle file formats, compression, encoding, and schema detection - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Ingest from various sources: cloud storage, on-premises, APIs, data lakes

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
The following is returned fromSYSTEMCLUSTERING_INFORMATION () for a tablenamed orders with adate column named O_ORDERDATE:

What does the total_constant_partition_count value indicate about this table?

A. The table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the range of values in that column overlap with every other micro partition in the table.
B. The data inO_ORDERDATEhas a very low cardinality as there are 493 micro-partitions where there is only a single distinct value in that column for all rows in the micro-partition
C. The table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering
D. The data inO_ORDERDATEdoes not change very often as there are 493 micro-partitionscontaining rows where that column has not been modified since the row was created

Answer: A

Explanation:
Explanation
The total_constant_partition_count value indicates the number of micro-partitions where the clustering key column has a constant value across all rows in the micro-partition. However, this does not necessarily mean that the table is clustered well on that column, as there could be other micro-partitions where the range of values in that column overlap with each other. This is the case for the orders table, as the clustering depth is 1, which means that every micro-partition overlaps with every other micro-partition on O_ORDERDATE. This indicates that the table is not clustered well on O_ORDERDATE and could benefit from reclustering.

NEW QUESTION # 54
A company stores customer data tables that include customer addresses in an AWS Lake Formation data lake. To comply with new regulations, the company must ensure that users cannot access data for customers who are in Canada.
The company needs a solution that will prevent user access to rows for customers who are in Canada.
Which solution will meet this requirement with the LEAST operational effort?

A. Set a row-level filter to prevent user access to a row where the country is Canada.
B. Set a column-level filter to prevent user access to a row where the country is Canada.
C. Apply a tag to all rows where Canada is the country. Prevent user access where the tag is equal to "Canada".
D. Create an IAM role that restricts user access to an address where the country is Canada.

Answer: A

NEW QUESTION # 55
A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector.
Based on the user's selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long How can this situation be improved?

A. Rewrite the report to eliminate the use of the loop construct
B. Increase the number of maximum clusters of the virtual warehouse
C. Define a clustering key for the sales data table
D. Increase the size of the virtual warehouse

Answer: A

Explanation:
Explanation
This option is the best way to improve the situation, as using a loop construct to run SQL queries for each customer is very inefficient and slow. Instead, the report should be rewritten to use a single SQL query that joins the customer and sales data tables and applies the query parameters as filters. This way, the report can leverage Snowflake's parallel processing and optimization capabilities and reduce the network overhead and latency.

NEW QUESTION # 56
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)

A. Use AWS Lake Formation for centralized data governance and access control.
B. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
C. Use AWS Glue DataBrew for centralized data governance and access control.
D. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
E. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.

Answer: A,B

NEW QUESTION # 57
A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?

A. Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
B. Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
C. Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.
D. Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster.
Write the logs to a table in the Aurora MySQL database.

Answer: B

Explanation:
Amazon Redshift provides an option to log and store all user activities and connection activities for compliance and auditing purposes. Configuring logging to an Amazon S3 bucket is the recommended approach because:
Amazon S3 is cost-effective, scalable, and durable for storing large volumes of logs over time.
Amazon Redshift natively supports logging to S3, allowing user and connection logs to be easily enabled and stored directly in the specified S3 bucket.
Storing logs in S3 meets compliance needs and provides an efficient way to manage, archive, and retrieve logs for security reviews or audits.

NEW QUESTION # 58
......

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