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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
How do diverse chart types (e.g., bar charts, scatter plots, heat grids) contribute to effective data presentation and visualization in reports and dashboards?

A. They limit data representation options for simplicity.
B. Diverse chart types restrict data exploration in reports and dashboards.
C. Different chart types offer varied data representation for better analysis.
D. Charts don't impact data visualization in reports or dashboards.

Answer: C

Explanation:
Different chart types offer varied data representation, aiding better analysis in reports and dashboards.

NEW QUESTION # 46
Your organization stores clickstream data in Parquet files in an external stage 's3://your-bucket/clickstreamP. The data includes nested JSON structures representing user activity. You need to create a Snowflake table to query this data efficiently, extracting specific fields from the nested JSON. The challenge is to optimize query performance by leveraging Parquet's columnar storage and schema evolution capabilities. Which of the following approaches offers the BEST combination of performance and flexibility for querying the data in Snowflake, considering potential schema changes in the Parquet files over time?

A. Create an external table pointing to the Parquet files. Define a materialized view on top of the external table, extracting the required fields. Refresh the materialized view periodically.
B. Load the Parquet data into a VARIANT column in a Snowflake table using 'COPY INTO'. Create a view on top of this table, using JSON EXTRACT_PATH_TEXT to extract specific fields into separate columns.
C. Create an external table with 'AUTO_REFRESH = TRUE and specify a schema on read using 'FILE_FORMAT = (TYPE = PARQUET, STRIP OUTER ARRAY = TRUE, = TRUE)'. Query the external table directly.
D. Create a Snowflake table with clearly defined columns matching the Parquet schema. Use 'COPY INTO' with a transformation to load the data. Update the table definition whenever the Parquet schema changes.
E. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly using to access nested fields. Create a view on top of the external table to simplify queries.

Answer: A

Explanation:
Materialized Views offer the best performance as they pre-compute and store the results, leveraging Snowflake's caching. They also adapt to schema changes in underlying Parquet files (within limits). External tables alone can be slow because of on-the-fly processing. Loading into VARIANT loses the advantage of Parquet's columnar structure. Predefined columns are rigid and don't handle schema evolution well. Creating standard views on external tables also does not provide the pre-computed benefits of the materialized views.

NEW QUESTION # 47
What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)

A. Speed of data retrieval
B. Frequency of data analysis
C. Available storage capacity
D. Data redundancy requirements

Answer: B,C

Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.

NEW QUESTION # 48
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: A,B

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 # 49
You are tasked with ingesting data from a REST API that provides customer order information in JSON format. The API returns a nested JSON structure with an array of orders, each containing customer details and order items. The data volume is expected to be high. You need to efficiently load this data into Snowflake. Which of the following approaches would be MOST efficient, considering cost and performance, and taking into account potential data quality issues?

A. Use a third-party ETL tool to extract, transform, and load the data into Snowflake.
B. Use an external function to call a Python script that parses the JSON, transforms the data into a flat relational structure, and then uses the Snowflake Python Connector to insert the data directly into relational tables.
C. Use Snowpipe with a stream and task to continuously ingest the JSON data into a staging table with a VARIANT column. Then, create a separate task to flatten the data into relational tables using SQL transformations after ingestion.
D. Load the data into a temporary stage, manually parse it with SQL statements, and then load it into your target tables. Schedule the task for periodic execution.
E. Load the JSON data directly into a VARIANT column, then create a view to flatten the data as needed. Use Snowflake's JSON functions for querying and transformation.

Answer: C

Explanation:
Snowpipe with a stream and task offers continuous, near real-time ingestion. Using a staging table with a VARIANT column allows for handling the complex JSON structure initially. Subsequent tasks can then transform and flatten the data into relational tables. This approach combines efficiency, automation, and scalability, minimizing manual intervention and maximizing performance for high-volume data. Other options are less efficient or scalable: A requires constant parsing during queries, C introduces external dependencies and overhead, D adds complexity and cost of a third-party tool, and E is manual and not scalable.

NEW QUESTION # 50
......

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