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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.
Snowflake ARA-C01 Certification Exam is a globally recognized certification that demonstrates a candidate's advanced proficiency in Snowflake architecture, data modeling, and performance optimization. SnowPro Advanced Architect Certification certification is highly valued in the industry and provides a competitive advantage to individuals who hold it. The Snowflake ARA-C01 certification exam is an excellent way for Snowflake architects to demonstrate their expertise and advance their careers in the field of data warehousing.
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Snowflake ARA-C01: SnowPro Advanced Architect Certification Exam is a highly regarded certification exam in the field of data warehousing and cloud computing. It is designed to test the advanced knowledge and skills of architects who are responsible for designing and implementing complex data warehousing solutions using Snowflake's cloud data platform.
NEW QUESTION # 177
An Architect is designing Snowflake architecture to support fast Data Analyst reporting. To optimize costs, the virtual warehouse is configured to auto-suspend after 2 minutes of idle time. Queries are run once in the morning after refresh, but later queries run slowly.
Why is this occurring?
Answer: D
Explanation:
Snowflake virtual warehouses maintain a local result and data cache only while the warehouse is running.
When a warehouse is suspended-whether manually or via auto-suspend-the local cache is cleared. As a result, subsequent queries cannot benefit from cached data and must re-scan data from remote storage, leading to slower execution (Answer D).
Snowflake does maintain a global result cache at the cloud services layer, but it is only used when the exact same query text is re-executed and the underlying data has not changed. In many analytical workloads, queries vary slightly, preventing reuse of the result cache.
Warehouse size and multi-cluster configuration impact concurrency and throughput, not cache persistence.
There is no USE_CACHE parameter in Snowflake. This question tests an architect's understanding of Snowflake caching behavior and the tradeoff between aggressive auto-suspend for cost control and cache reuse for performance.
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NEW QUESTION # 178
Which system functions does Snowflake provide to monitor clustering information within a table (Choose two.)
Answer: B,E
Explanation:
Explanation
According to the Snowflake documentation, these two system functions are provided by Snowflake to monitor clustering information within a table. A system function is a type of function that allows executing actions or returning information about the system. A clustering key is a feature that allows organizing data across micro-partitions based on one or more columns in the table. Clustering can improve query performance by reducing the number of files to scan.
* SYSTEM$CLUSTERING_INFORMATION is a system function that returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns a JSON string with the clustering information. The clustering information includes the cluster by keys, the total partition count, the total constant partition count, the average overlaps, and the average depth1.
* SYSTEM$CLUSTERING_DEPTH is a system function that returns the clustering depth for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns an integer value with the clustering depth. The clustering depth is the maximum number of overlapping micro-partitions for any micro-partition in the table. A lower clustering depth indicates a better clustering2.
References:
* SYSTEM$CLUSTERING_INFORMATION | Snowflake Documentation
* SYSTEM$CLUSTERING_DEPTH | Snowflake Documentation
NEW QUESTION # 179
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?
Answer: B
Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Reference:
The copy into <table> command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
CASE_SENSITIVE: The column names in the source data must match the column names in the target table exactly, including the case. This is the default value.
CASE_INSENSITIVE: The column names in the source data must match the column names in the target table, but the case is ignored.
NONE: The column names in the source data are ignored, and the data is loaded based on the order of the columns in the target table.
The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
When using the copy into <table> command with the CSV file format, the match_by_column_name parameter behaves as follows2:
It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name. This means that the first row of the CSV file must contain the column names, and they must match the column names in the target table exactly, including the case. If the header is missing or does not match, the command will return an error.
The parameter will not be ignored, even if it is set to NONE. The command will still try to match the column names in the CSV file with the column names in the target table, and will return an error if they do not match.
The command will not return a warning stating that the file has unmatched columns. It will either load the data successfully if the column names match, or return an error if they do not match.
1: COPY INTO <table> | Snowflake Documentation
2: MATCH_BY_COLUMN_NAME | Snowflake Documentation
NEW QUESTION # 180
A company has an inbound share set up with eight tables and five secure views. The company plans to make the share part of its production data pipelines.
Which actions can the company take with the inbound share? (Choose two.)
Answer: D,E
Explanation:
Explanation
These two actions are possible with an inbound share, according to the Snowflake documentation and the web search results. An inbound share is a share that is created by another Snowflake account (the provider) and imported into your account (the consumer). An inbound share allows you to access the data shared by the provider, but not to modify or delete it. However, you can perform some actions with the inbound share, such as:
* Clone a table from a share. You can create a copy of a table from an inbound share using the CREATE TABLE ... CLONE statement. The clone will contain the same data and metadata as the original table, but it will be independent of the share. You can modify or delete the clone as you wish, but it will not reflect any changes made to the original table by the provider1.
* Create additional views inside the shared database. You can create views on the tables or views from an inbound share using the CREATE VIEW statement. The views will be stored in the shared database, but they will be owned by your account. You can query the views as you would query any other view in your account, but you cannot modify or delete the underlying objects from the share2.
The other actions listed are not possible with an inbound share, because they would require modifying the share or the shared objects, which are read-only for the consumer. You cannot grant modify permissions on the share, create a table from the shared database, or create a table stream on the shared table34.
References:
* Cloning Objects from a Share | Snowflake Documentation
* Creating Views on Shared Data | Snowflake Documentation
* Importing Data from a Share | Snowflake Documentation
* Streams on Shared Tables | Snowflake Documentation
NEW QUESTION # 181
A Snowflake Architect is designing a multiple-account design strategy.
This strategy will be MOST cost-effective with which scenarios? (Select TWO).
Answer: D,E
Explanation:
B: When dealing with PCI DSS compliance, having separate accounts can be beneficial because it enables strong isolation of environments that handle sensitive data from those that do not. By segregating the compliant from non-compliant resources, an organization can limit the scope of compliance, thus making it a cost-effective strategy.D. Different Active Directory instances can be managed more effectively and securely when separated into different accounts. This approach allows for distinct identity and access management policies, which can enforce security requirements and minimize the risk of access policy errors between environments.
NEW QUESTION # 182
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