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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q340-Q345):

NEW QUESTION # 340
A company uses AWS Step Functions to orchestrate a data pipeline. The company has configured the Step Functions logs to push to Amazon CloudWatch Logs when the log level is FATAL The company has enabled logs for all AWS services in the pipeline.
A state named "preprocessing" invokes an AWS Lambda function named "preprocessing." The Lambda function preprocesses data before proceeding to the next state. The company needs to find error details if an error occurs during the data preprocessing.
Which CloudWatch Logs log group should the company check to find the error details?

Answer: D

Explanation:
The actual error during data preprocessing occurs inside the AWS Lambda function execution.
Lambda automatically writes detailed execution logs, including errors and stack traces, to its CloudWatch Logs log group. Therefore, checking the Lambda log group provides the precise error details for the preprocessing failure.


NEW QUESTION # 341
A company is setting up a new Amazon SageMaker Unified Studio domain. Each of the company's business units needs isolated control over its own assets, projects, and metadata.
Specific datasets must be shareable with other business units upon approval. The company also requires centralized user authentication and identity mapping. Which solution will meet these requirements?

Answer: D

Explanation:
SageMaker Unified Studio supports domain units within a single domain to provide isolation of assets, projects, and metadata while still allowing controlled sharing between units. Using AWS IAM Identity Center provides centralized authentication and identity mapping. Cross-unit sharing with approval workflows ensures datasets can be shared securely while maintaining isolation and least privilege.


NEW QUESTION # 342
A company uses Amazon Redshift as its data warehouse service. A data engineer needs to design a physical data model.
The data engineer encounters a de-normalized table that is growing in size. The table does not have a suitable column to use as the distribution key.
Which distribution style should the data engineer use to meet these requirements with the LEAST maintenance overhead?

Answer: C

Explanation:
AUTO distribution allows Amazon Redshift to automatically manage the distribution style based on the size of the table. With this option, Redshift chooses the most optimal distribution method, and as the table grows, it will adjust the distribution style to maintain performance. This minimizes maintenance overhead since the system adapts without the need for manual tuning.
ALL distribution copies the entire table to all nodes. It can be useful for small, static tables but is inefficient for large or growing tables as it increases storage and I/O costs significantly.
EVEN distribution distributes rows evenly across all nodes. While it reduces data skew, it may not be optimal for larger tables where certain queries would benefit from co-locating data to minimize cross-node traffic.
KEY distribution places data based on the values in a specific column (distribution key). Since the question states that no suitable column exists for a distribution key, this method would not be appropriate.


NEW QUESTION # 343
Snowflake computes and adds partitions based on the defined partition column expressions when an external table metadata is refreshed.
What are the Correct Statements to configure Partition metadata refresh in case of External Tables?

Answer: B,C

Explanation:
Explanation
Snowflake strongly recommend partitioning your external tables, which requires that your underly-ing data is organized using logical paths that include date, time, country, or similar dimensions in the path.
Partitioning divides your external table data into multiple parts using partition columns.
An external table definition can include multiple partition columns, which impose a multi-dimensional structure on the external data.
Partitions are stored in the external table metadata.
Benefits of partitioning include improved query performance.
Because the external data is partitioned into separate slices/parts, query response time is faster when processing a small part of the data instead of scanning the entire data set.
Based on your individual use cases, you can either:
Add new partitions automatically by refreshing an external table that defines an expression for each partition column.
Add new partitions manually.
Partition columns are defined when an external table is created, using the CREATE EXTERNAL TABLE ...
PARTITION BY syntax.
After an external table is created, the method by which partitions are added cannot be changed.
Partitions Added Automatically
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn.
A partition consists of all data files that match the path and/or filename in the expression for the partition column.
The CREATE EXTERNAL TABLE syntax for adding partitions automatically based on expres-sions is as follows:
CREATE EXTERNAL TABLE
<table_name>
( <part_col_name> <col_type> AS <part_expr> )
[ , ... ]
[ PARTITION BY ( <part_col_name> [, <part_col_name> ... ] ) ]
Snowflake computes and adds partitions based on the defined partition column expressions when an external table metadata is refreshed.
By default, the metadata is refreshed automatically when the object is created.
In addition, the object owner can configure the metadata to refresh automatically when new or up-dated data files are available in the external stage.
The owner can alternatively refresh the metadata manually by executing the ALTER EXTERNAL TABLE ...
REFRESH command.
The metadata for an external table can be refreshed automatically using the event notification ser-vice for your cloud storage service.


NEW QUESTION # 344
A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.complete_orders_history, which contains 100 columns. The query must return all columns except columns named companyId and unique_system_id. Which Amazon Redshift SQL statement will meet this requirement?

Answer: D

Explanation:
Amazon Redshift supports the SELECT * EXCLUDE syntax, which allows returning all columns from a table except the specified ones. This is the most efficient and direct way to exclude specific columns like company_id and unique_system_id without listing all remaining columns.


NEW QUESTION # 345
......

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