DAA-C01 SAMPLE TEST ONLINE & DAA-C01 VALID EXAM TEST

DAA-C01 Sample Test Online & DAA-C01 Valid Exam Test

DAA-C01 Sample Test Online & DAA-C01 Valid Exam Test

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.

  • A. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.
  • B. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.
  • C. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.
  • D. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.
  • E. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.

Answer: A,B

Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.


NEW QUESTION # 49
You are tasked with identifying potential data sources for a new marketing analytics dashboard. The dashboard needs to provide insights into customer behavior across various touchpoints. Which of the following would be the MOST appropriate data sources to consider?

  • A. Website clickstream data stored in AWS S3 buckets in Parquet format.
  • B. Salesforce data containing customer interactions and sales opportunities.
  • C. Social media activity data ingested via a third-party API and stored in a relational database.
  • D. IoT sensor data containing temperature readings.
  • E. Database containing HR employee data.

Answer: A,B,C

Explanation:
Options A, B, and C are the most relevant data sources for a marketing analytics dashboard focused on customer behavior. Website clickstream data (A) provides insights into user interactions on the website. Social media activity data (B) offers insights into customer sentiment and engagement. Salesforce data (C) provides information about customer interactions and sales opportunities. HR employee data (D) and IoT sensor data (E) are less relevant to customer behavior and marketing analytics.


NEW QUESTION # 50
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?

  • A. Option A
  • B. Option B
  • C. Option C
  • D. Option E
  • E. Option D

Answer: D

Explanation:
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.


NEW QUESTION # 51
Your organization is migrating a large on-premise data warehouse (100 TB) to Snowflake. The existing data warehouse contains complex data transformations implemented in stored procedures. During the migration, you need to minimize downtime and accurately estimate the data volume transferred to Snowflake. You decide to use a hybrid approach with both batch and incremental loading. Which of the following strategies would be MOST appropriate?

  • A. Utilize Snowflake Connectors (e.g., Kafka connector) to stream data directly from the on-premise data warehouse to Snowflake. Implement a data replication tool to handle the initial data load and ongoing synchronization.
  • B. Use Snowflake's Data Exchange to directly replicate the on-premise data warehouse to Snowflake. Rely on Data Exchange's built-in monitoring to track the data volume transferred.
  • C. Perform a full batch load of the historical data using Snowpipe from a cloud storage location. After the initial load, implement a change data capture (CDC) solution using a third-party tool (e.g., Debezium, Qlik Replicate) to incrementally load changes into Snowflake.
  • D. Create external tables pointing to the on-premise data warehouse. Run queries directly against the external tables to transform and load the data into Snowflake. Once all data is loaded, drop the external tables. Use 'TABLE_SIZE function for sizing calculation.
  • E. Implement a custom ETL pipeline using Apache Spark to extract data from the on-premise data warehouse, perform transformations, and load the data into Snowflake using the Snowflake JDBC driver. Continuously run Spark jobs to keep the data synchronized.

Answer: C

Explanation:
Option B is the most appropriate strategy. It allows for a faster initial load using Snowpipe, leveraging the scalability of cloud storage. Implementing a CDC solution minimizes downtime and ensures data synchronization after the initial load. The combined approach addresses both the large data volume and the need for continuous updates.


NEW QUESTION # 52
You are tasked with creating a data pipeline that ingests data from various sources, including a Snowflake Marketplace data share, and prepares it for analysis. The pipeline involves several transformations and enrichments. Which of the following methods offer the BEST approach to manage data lineage and auditability within this pipeline, considering the shared data from the Marketplace?

  • A. Replicate the data share's tables into your own database and track changes on the replicated tables.
  • B. Implement a custom logging system that records each transformation step and data source, including the data share details.
  • C. Create a series of temporary tables at each stage of the pipeline to store intermediate results and track data lineage.
  • D. Use Snowflake's 'SYSTEM$GET_PREDECESSORS' and functions combined with a metadata repository to capture and visualize data lineage.
  • E. Rely solely on Snowflake's query history and table metadata to track data lineage.

Answer: D

Explanation:
Option C is the best approach. Snowflake's built-in functions like 'SYSTEM$GET_PREDECESSORS' and allow you to programmatically trace the dependencies and data flow within your Snowflake environment, including data accessed from shares. Combining this information with a metadata repository provides a robust and auditable data lineage solution. Option A is insufficient as it doesn't provide a structured and easily navigable lineage. Option B is viable but requires significant manual effort to maintain and scale. Option D creates unnecessary storage overhead and doesn't inherently improve data lineage tracking. Option E is not recommended as replicating shared data goes against the purpose of data sharing and can lead to synchronization issues.


NEW QUESTION # 53
......

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