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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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	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 (Q359-Q364):

NEW QUESTION # 359
A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.
The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.
How should the data engineer resolve the throttling issue?

A. Increase the number of shards in the data stream. Distribute the records across the shards.
B. Use a random partition key to distribute the ingested records.
C. Limit the number of records that are sent each second by the producer to match the capacity of the stream.
D. Decrease the size of the records that the producer sends to match the capacity of the stream.

Answer: A

Explanation:
Amazon Kinesis Data Streams distribute data across multiple shards, with each shard having its own capacity for read and write operations. Throttling occurs when one or more shards, referred to as "hot shards," receive significantly more data than they can handle. To resolve this, increasing the number of shards in the data stream and redistributing the records across the shards is the appropriate solution. This approach ensures that the workload is spread more evenly, thereby preventing throttling on individual shards.

NEW QUESTION # 360
A company stores customer data in an Amazon S3 bucket. Multiple teams in the company want to use the customer data for downstream analysis. The company needs to ensure that the teams do not have access to personally identifiable information (PII) about the customers.
Which solution will meet this requirement with LEAST operational overhead?

A. Use an AWS Glue DataBrew job to store the PII data in a second S3 bucket. Perform analysis on the data that remains in the original S3 bucket.
B. Use S3 Object Lambda to access the data, and use Amazon Comprehend to detect and remove PII.
C. Use Amazon Data Firehose and Amazon Comprehend to detect and remove PII.
D. Use Amazon Macie to create and run a sensitive data discovery job to detect and remove PII.

Answer: B

NEW QUESTION # 361
Mark a Data Engineer, looking to implement streams on local views & want to use change tracking metadata for one of its Data Loading use case. Please select the incorrect understanding points of Mark with respect to usage of Streams on Views?

A. Enabling change tracking adds a pair of hidden columns to the table and begins storing change tracking metadata. The values in these hidden CDC data columns provide the input for the stream metadata columns. The columns consume a small amount of stor-age.
B. For streams on views, change tracking must be enabled explicitly for the view and un-derlying tables to add the hidden columns to these tables.
C. Views with GROUP BY & LIMIT Clause are supported by Snowflake.
D. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table.
E. As an alternative to streams, Snowflake supports querying change tracking metadata for views using the CHANGES clause for SELECT statements.

Answer: C

Explanation:
Explanation
A stream object records data manipulation language (DML) changes made to tables, including in-serts, updates, and deletes, as well as metadata about each change, so that actions can be taken us-ing the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at the row level, between two transac-tional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
Standard tables, including shared tables.
Views, including secure views
Directory tables
External tables
When created, a stream logically takes an initial snapshot of every row in the source object (e.g. ta-ble, external table, or the underlying tables for a view) by initializing a point in time (called an off-set) as the current transactional version of the object. The change tracking system utilized by the stream then records information about the DML changes after this snapshot was taken. Change rec-ords provide the state of a row before and after the change. Change information mirrors the column structure of the tracked source object and includes additional metadata columns that describe each change event.
Note that a stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, a pair of hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables.
Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views.
Views with the following operations are not yet supported:
GROUP BY clauses
QUALIFY clauses
Subqueries not in the FROM clause
Correlated subqueries
LIMIT clauses
Change Tracking:
Change tracking must be enabled in the underlying tables.
Prior to creating a stream on a view, you must enable change tracking on the underlying tables for the view.
Set the CHANGE_TRACKING parameter when creating a view (using CREATE VIEW) or later (using ALTER VIEW).
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.

NEW QUESTION # 362
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.
B. Create an AWS Lambda function to check data quality and to raise exceptions in the code. Run the function when data is added to the S3 bucket. Create an Amazon CloudWatch alarm for exceptions in the code.
C. Create AWS Lambda functions to evaluate data quality rules. Use AWS Step Functions to orchestrate a workflow that publishes notifications when the data fails to meet data quality rules.
D. Create an Amazon EMR provisioned cluster. Add a Python open source data quality package to the EMR cluster. Use the Python package to write code for data quality rules and to copy the data from the S3 bucket to the EMR cluster. Copy the data from the S3 bucket to the EMR cluster. Run the data quality rules.

Answer: A

Explanation:
AWS Glue DataBrew is a fully managed, serverless service with built-in data quality rulesets and profiling jobs that run directly on S3 data. Triggering the profile job via EventBridge on object creation applies rules with minimal code and management overhead.

NEW QUESTION # 363
When processing large datasets using distributed computing frameworks, uneven distribution of data can lead to processing delays. What is this phenomenon commonly known as?

A. Data partitioning
B. Data skew
C. Data fragmentation
D. Data shuffling

Answer: B

NEW QUESTION # 364
......

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