Free Access Snowflake.DAA-C01.v2025-12-20.q172 Practice Test (Page 35)

Question 166

You are tasked with loading a large CSV file containing website traffic data into Snowflake. The CSV file has the following characteristics: Header row is present. Fields are enclosed in double quotes. The delimiter is a pipe (l) character. One column, 'timestamp' , is stored as milliseconds since the epoch and needs to be converted to a Snowflake TIMESTAMP NTZ. Which of the following COPY INTO statement options would correctly load the data, handle the delimiter and quotes, and convert the 'timestamp' column?

A.Option A

B.Option B

C.Option C

D.Option D

E.Option E

Question 167

You are designing a data ingestion pipeline to collect clickstream data from a high-traffic e-commerce website. You anticipate a daily data volume that fluctuates significantly, ranging from 5 TB to 20 TB. To optimize Snowflake costs and ensure efficient data loading, which combination of Snowflake features and ingestion methods would be MOST effective for identifying the accurate data volume before the data is fully loaded into a production table?

A.Use Snowflake's Resource Monitors to track credit usage during data loading. Configure a data pipeline using Snowpipe with auto-ingest enabled. Rely solely on Snowflake's cost estimation tools to predict volume.

B.Utilize a cloud-based message queue (e.g., AWS SQS, Azure Queue Storage) to buffer incoming data. Implement a separate Snowflake task to periodically query the message queue's approximate message count and size. Combine this with Snowsight monitoring of storage costs for the raw data stage.

C.Implement Snowpipe with pre-validation using a data transformation tool like dbt Core. Create an external function to execute a sampling query against a percentage of files in cloud storage and estimate the total volume. Load a smaller sample data to a staging table and then use 'TABLE SIZE' to get the actual size and scale it up.

D.Use Snowflake Tasks to schedule a Python script that reads file metadata (size, number of files) from the cloud storage location using the cloud provider's SDK (e.g., bot03 for AWS S3, azure-storage-blob for Azure Blob Storage). Store the collected metadata in a Snowflake table. Then, use SQL queries to calculate the total data volume.

E.Rely solely on Snowsight's query history and data loading metrics after the data has been loaded into Snowflake, adjusting future ingestion parameters based on historical data.

Question 168

How do secure views contribute to data analysis practices in terms of access control and data security?

A.Secure views enhance data security while allowing selective data access.

B.They prevent the creation of materialized views.

C.They restrict data access but don't impact data security.

D.Secure views limit data accessibility for improved security.

Question 169

You are tasked with building a near real-time data pipeline that ingests streaming data from Kafka into Snowflake. The data is semi- structured JSON. The goal is to transform and load only records with a 'status' field equal to 'active' into a table named 'ACTIVE RECORDS'. Which approach provides the most efficient and scalable solution, minimizing latency and Snowflake resource consumption?

A.Use Snowflake's Kafka connector to load all data into a staging table, then create a scheduled task that runs every minute to transform and load the 'active' records into 'ACTIVE RECORDS

B.Implement a Snowpipe with a continuous ingest endpoint. Use a transformation defined within the COPY INTO statement to filter for 'status' = 'active' during the load process.

C.Create an external table pointing to the Kafka topic. Use a Snowflake stream on the external table to track changes, and a task to transform and load 'active' records into 'ACTIVE RECORDS'

D.Use a third-party ETL tool to consume the Kafka stream, filter for 'status' = 'active', and then load the data directly into 'ACTIVE RECORDS' using the Snowflake JDBC driver.

E.Ingest all data into a raw table using Snowpipe. Create a materialized view on top of the raw table, filtering for 'status' = 'active', and store the results in 'ACTIVE RECORDS'.

Question 170

Your company uses Snowflake to store sales data'. A dashboard reporting weekly sales trends is performing poorly. The underlying table, 'SALES DATA, contains billions of rows with columns like 'SALE DATE, 'PRODUCT ID', 'CUSTOMER D', and 'SALE AMOUNT. The dashboard queries use 'SALE DATE for filtering and grouping. The query execution plan shows full table scans. You need to optimize the dashboard's performance with minimal impact on data loading processes. Which of the following strategies should you implement FIRST to improve query performance?

A.Create a clustered table on 'SALE DATE' in the 'SALES DATA' table.

B.Create a materialized view that aggregates sales data weekly by 'PRODUCT_ID and 'CUSTOMER ID

C.Increase the warehouse size to X-LARGE.

D.Create a search optimization on 'SALES DATA' table for 'SALE DATE.

E.Partition the 'SALES_DATX table by 'SALE_DAT

Premium Bundle

Newest DAA-C01 Exam PDF Dumps shared by BraindumpsPass.com for Helping Passing DAA-C01 Exam! BraindumpsPass.com now offer the updated DAA-C01 exam dumps, the BraindumpsPass.com DAA-C01 exam questions have been updated and answers have been corrected get the latest BraindumpsPass.com DAA-C01 pdf dumps with Exam Engine here:

Access DAA-C01 Premium Version

(67 Q&As Dumps, 40%OFF Special Discount: Exam-Tests)

Latest Upload: 447PaloAltoNetworks.NGFW-Engineer.v2026-05-01.q43; 582Nokia.4A0-113.v2026-05-01.q69; 609EC-COUNCIL.312-49v11.v2026-04-30.q214; 584Microsoft.MB-820.v2026-04-30.q101; 437Salesforce.MC-202.v2026-04-30.q57; 479BICSI.INSTC_V8.v2026-04-29.q53; 579NMLS.MLO.v2026-04-28.q82; 403NCARB.Project-Management.v2026-04-28.q27; 756EMC.D-AV-DY-23.v2026-04-27.q184; 1979ServiceNow.CSA.v2026-04-27.q483