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Snowflake ARA-C01 Certification Exam consists of 60 multiple-choice questions and has a time limit of 120 minutes. ARA-C01 exam is administered online and can be taken from anywhere in the world. The passing score for the exam is 80%, and individuals who pass the exam will receive the SnowPro Advanced Architect Certification. SnowPro Advanced Architect Certification certification is recognized globally and demonstrates that the holder has the skills and knowledge to design and deploy complex Snowflake solutions that meet the needs of their organization. The SnowPro Advanced Architect Certification is a valuable credential for data architects, data engineers, and data analysts who work with Snowflake and want to advance their careers in the field of data management and analytics.
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To become certified in Snowflake ARA-C01, candidates must first meet the eligibility requirements, which include having experience in data warehousing, data modeling, and data integration. Candidates must also complete the SnowPro Core Certification Exam and pass the SnowPro Advanced Architect Certification Exam, which is a rigorous and comprehensive exam that tests advanced knowledge and skills in Snowflake architecture.
NEW QUESTION # 214
What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?
Answer: A
Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.
The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. References: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation
NEW QUESTION # 215
You have a large warehouse with auto suspend configured for 10 minutes. You submitted a query and it is going to run for more than 10 minutes.
What will happen to the query?
Answer: B
NEW QUESTION # 216
When a database gets cloned, what accesses are replicated
Answer: C
NEW QUESTION # 217
A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is configured to load data into staging database tables. Then a task runs to load the data from the staging database tables into the reporting database tables.
The company is satisfied with the availability of the data in the reporting database tables, but the reporting tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of the tables in the reporting database.
What step can be taken to improve the pruning of the reporting tables?
Answer: D
Explanation:
Effective pruning in Snowflake relies on the organization of data within micro-partitions. By using an ORDER BY clause with clustering keys when loading data into the reporting tables, Snowflake can better organize the data within micro-partitions. This organization allows Snowflake to skip over irrelevant micro-partitions during a query, thus improving query performance and reducing the amount of data scanned12.
Reference =
* Snowflake Documentation on micro-partitions and data clustering2
* Community article on recognizing unsatisfactory pruning and improving it1
NEW QUESTION # 218
Which of the below privileges are required for search optimization?
Answer: A,B
NEW QUESTION # 219
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