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Latest DAA-C01 Test Objectives & DAA-C01 PDF

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
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. 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'.
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. 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
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. 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'

Answer: B

Explanation:
Snowpipe with a transformation in the COPY INTO statement offers the most efficient and scalable near real-time data ingestion solution. This approach allows filtering during the load process, avoiding the need to load all data and then filter later. Option A involves a scheduled task, which introduces latency. Option C's external table + stream approach is generally slower for continuous ingest. Option D adds the overhead and cost of a 3rd party ETL tool, while Option E's materialized view would work, it isn't the most efficient approach for the initial data filtering.

NEW QUESTION # 17
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?

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

Answer: B

Explanation:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.

NEW QUESTION # 18
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?

A. Ingest data directly into a single Snowflake table using Snowpipe with JSON data type. Create separate materialized views for real-time dashboards and historical trend analysis.
B. Employ a combination of Snowpipe for near real-time data ingestion into a raw table, and then use Snowflake's Search Optimization Service for faster queries.
C. Utilize an external stream processing engine to pre-aggregate the data into time windows (e.g., I-minute, I-hour) before ingesting into Snowflake using Snowpipe.
D. Use Snowpipe to ingest data into a raw landing table, and then use Snowflake tasks to transform and load the data into separate tables optimized for real-time dashboards and historical analysis.
E. Implement Snowpipe for initial data ingestion, complemented by Snowflake's clustering feature based on timestamp to optimize historical analysis queries. And employ a stream processing engine to perform time window pre-aggregation.

Answer: D,E

Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.

NEW QUESTION # 19
You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?

A. Create a full copy of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DE-VS. Use a 'MERGE statement to update with the cleaned data from
B. Create a zero-copy clone of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DEV'. Create a zero- copy clone of 'CUSTOMER_DATX named Update 'CUSTOMER_DATX with the cleaning logic. Point the downstream reporting to 'CUSTOMER DATA REPORTING'.
C. Create a zero-copy clone of named for testing. Clean the data in Create a separate table named 'CLEANED CUSTOMER DATA'. Insert the cleaned data from 'CUSTOMER DATA DEV' into the new 'CLEANED CUSTOMER DATA' table. Update with the cleaning logic.
D. Create a zero-copy clone of 'CUSTOMER_DATA' named for testing. Clean the data in 'CUSTOMER_DATA_DEV'. Once satisfied, update the 'CUSTOMER_DATR table directly with the cleaning logic.
E. Create a zero-copy clone of named for testing. Create another zero-copy clone of 'CUSTOMER DATA DEV' named 'CUSTOMER DATA REPORTING'. Clean the data in 'CUSTOMER DATA DENT. Point downstream reporting to 'CUSTOMER DATA REPORTING'.

Answer: B

Explanation:
Option D is the most appropriate and safely covers all aspects. Cloning to lets you experiment with cleaning. The most important part of the question is to handle the downstream reporting. So cloning 'CUSTOMER DATA' to lets you test how your new updates will affect the reports that depend on the data. Updating the 'CUSTOMER_DATR with the cleaning logic lets you apply the tested data cleaning. The other options do not protect the production reporting from potentially breaking changes during the data cleaning process. They may also directly update the production data, increasing risk. In option B, even though you are pointing to the new cloned reporting table, since that is created from DEV table it will already have changed data, and we want to report on the original, not the one with the dev changes. Option E does not discuss downstream impact on the reports, so this is not fully addressing all the impacts.

NEW QUESTION # 20
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?

A. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format it as CSV using Javascript UDF, writes the CSV to an internal stage, and then uses a Java UDF (Util.EmailSender) to send the email with the CSV attachment. Grant necessary permissions to the task's service account to access the stage and execute the UDFs.
B. Create a scheduled task in an external orchestrator (e.g., Airflow). This task uses the Snowflake Python connector to query the data, format it as CSV, writes the CSV to an external stage (e.g., AWS S3), and then uses an email service (e.g., AWS SES) to send the email with the CSV attachment. Configure appropriate IAM roles for Snowflake to access S3.
C. Use a third-party reporting tool (e.g., Tableau, Power Bl) to connect to Snowflake, create the report, and schedule it for daily email delivery with a CSV attachment. Configure the tool with the appropriate Snowflake connection details and credentials.
D. Create a Snowflake Stream on the 'WEB ACTIVITY table. When data changes are detected, trigger an external function (e.g., AWS Lambda) via a pipe. The Lambda function queries the data, formats it as CSV, uploads the file to S3, and sends an email with a download link.
E. Create a Snowflake Alert that triggers when the 'WEB ACTIVITY table is updated. The alert executes a stored procedure that queries the data, formats it as CSV using Snowflake Scripting, writes the CSV to an internal stage, and then uses a Python UDF to send the email with the CSV attachment. Grant the alert necessary privileges.

Answer: B

Explanation:
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.

NEW QUESTION # 21
......

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