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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Pipelines and Transformation	- ETL/ELT design patterns in Snowflake 1. Data orchestration concepts 2. Staging and transformation layers - Streams and Tasks 1. Change Data Capture (CDC) with Streams 2. Automating pipelines with Tasks
Topic 2: Security, Governance, and Data Sharing	- Access control 1. Object-level permissions 2. Role-based access control (RBAC) - Data governance 1. Secure data sharing concepts 2. Data masking policies
Topic 3: Data Loading and Integration	- Snowpipe and continuous ingestion 1. Event-driven loading 2. Automated ingestion workflows - Bulk data loading 1. COPY INTO command usage 2. File format handling
Topic 4: Performance Optimization	- Query performance tuning 1. Clustering and micro-partitions 2. Caching mechanisms - Warehouse optimization 1. Scaling strategies 2. Concurrency management
Topic 5: Semi-Structured and Advanced Data Handling	- Semi-structured data processing 1. VARIANT data type usage 2. JSON, Avro, Parquet handling - Advanced transformation techniques 1. Flattening nested data 2. Complex transformations in SQL
Topic 6: Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Storage and compute separation 2. Virtual warehouses and compute scaling - Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q181-Q186):

NEW QUESTION # 181
External Function is a type of UDF & can be Scaler or Tabular?

A. TRUE
B. FALSE

Answer: B

Explanation:
Explanation
External functions must be scalar functions. A scalar external function returns a single value for each input row.

NEW QUESTION # 182
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. Regular views do not cache data, and therefore cannot improve performance by cach-ing.
B. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
C. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
D. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.
E. 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.

Answer: D

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 # 183
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The company runs the data stream in an account named Account A. The company wants to use an AWS Lambda function in an account named Account B to process the data from the data stream. The company creates a Lambda execution role in Account B that has permissions to access data from the data stream in Account A.
What additional step must the company take to meet this requirement?

A. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account A.
B. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B.
C. Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.
D. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.

Answer: C

Explanation:
To enable cross-account Lambda processing of Kinesis Data Streams, the stream in Account A must explicitly allow the Lambda execution role from Account B. This is done by adding a resource-based policy on the Kinesis data stream to grant kinesis:SubscribeToShard and related read permissions to the cross-account role. Without this resource-based policy, the Lambda in Account B cannot consume the data.

NEW QUESTION # 184
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account.
A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow.
Which log type should the data engineer use to diagnose the cause of the failure?

A. YourEnvironmentName-Task
B. YourEnvironmentName-Scheduler
C. YourEnvironmentName-WebServer
D. YourEnvironmentName-DAGProcessing

Answer: A

Explanation:
When a workflow fails to run in Amazon MWAA, the task logs (YourEnvironmentName-Task) are the most relevant for diagnosing the issue. Task logs contain detailed information about the execution of individual tasks within the workflow, including any error messages or stack traces that can help pinpoint the cause of the failure.

NEW QUESTION # 185
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. 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.
B. 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.
C. 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.
D. Views with GROUP BY & LIMIT Clause are supported by Snowflake.
E. As an alternative to streams, Snowflake supports querying change tracking metadata for views using the CHANGES clause for SELECT statements.

Answer: D

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 # 186
......

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