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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Real Exam Qty: | Approximately 65 |
| Available Languages: | English |
| Exam Format: | Multiple select, Multiple choice |
| Certificate Validity Period: | 2 years |
| Exam Price: | $375 USD |
| Passing Score: | 750 (scaled score out of 1000) |
| Related Certifications: | SnowPro Core Certification |
| Exam Duration: | 115 minutes |
| Recommended Training: | Snowflake Training & Certification Learning Path |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | SnowPro Core Certification is strongly recommended and often expected before attempting the Advanced Architect exam |
| Official Syllabus URL: | https://www.snowflake.com/certification/ |
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NEW QUESTION # 27
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between 9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload? (Choose two.)
Answer: D,E
Explanation:
These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.
Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.
Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.
Reference:
Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
Snowflake Documentation: MAX_CONCURRENCY_LEVEL
NEW QUESTION # 28
A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.
What is the MOST cost-effective way to bring this data into a Snowflake table?
Answer: D
Explanation:
A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.
A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.
An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.
A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.
A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.
NEW QUESTION # 29
You have created a table as below
CREATE TABLE SNOWFLAKE_BLOG(BLOG_ID NUMBER, BLOG_CONTENT VARCHAR,BLOG_AUTHOR VARCHAR,BLOG_CREATE_DATE TIMESTAMP );
Now you want to cluster it by BLOG_AUTHOR and BLOG_CREATE_DATE.
Which command below will you choose?
Answer: B
NEW QUESTION # 30
Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).
Answer: B,C
NEW QUESTION # 31
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?
Answer: C
Explanation:
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake's micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. Reference:
Snowflake Connector for Kafka
Snowpipe
Snowflake Streams
Snowflake Spark Connector
NEW QUESTION # 32
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