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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Real Exam Qty: | Approximately 65 |
| Passing Score: | 750 (scaled score out of 1000) |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Price: | $375 USD |
| Exam Format: | Multiple select, Multiple choice |
| Exam Duration: | 115 minutes |
| Related Certifications: | SnowPro Core Certification |
| 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/ |
>> ARA-C01 Deutsche Prüfungsfragen <<
Viele der ARA-C01 Fragenkatalog SnowPro Advanced Architect Certificationaus It-Pruefung sind in der Form von Vielfache-Wahl-Fragen. Um Ihre ARA-C01 Zertifizierungsprüfungen reibungslos zu meistern, brauchen Sie nur unsere Snowflake ARA-C01 Prüfungsfragen und Antworten (SnowPro Advanced Architect Certification) auswendigzulernen.
Die Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Zertifizierungsprüfung ist ein hochspezialisiertes Zertifizierungsprogramm, das für Fachleute entwickelt wurde, die ihr fortgeschrittenes technisches Wissen und ihre Fähigkeiten in der Gestaltung, Bereitstellung und Verwaltung von Snowflake-Lösungen demonstrieren möchten. Diese Zertifizierungsprüfung ist darauf ausgelegt, das Wissen und die Fähigkeiten zu bewerten, die auf Expertenebene benötigt werden, um eine Snowflake-Architektur aufzubauen, zu entwerfen und zu verwalten.
52. Frage
What are purposes for creating a storage integration? (Choose three.)
Antwort: A,B,F
Begründung:
A storage integration is a Snowflake object that stores a generated identity and access management (IAM) entity for an external cloud provider, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. This integration allows Snowflake to read data from and write data to an external storage location referenced in an external stage1.
One purpose of creating a storage integration is to support multiple external stages using one single Snowflake object. An integration can list buckets (and optional paths) that limit the locations users can specify when creating external stages that use the integration. Note that many external stage objects can reference different buckets and paths and use the same storage integration for authentication1. Therefore, option C is correct.
Another purpose of creating a storage integration is to avoid supplying credentials when creating a stage or when loading or unloading data. Integrations are named, first-class Snowflake objects that avoid the need for passing explicit cloud provider credentials such as secret keys or access tokens. Integration objects store an IAM user ID, and an administrator in your organization grants the IAM user permissions in the cloud provider account1. Therefore, option D is correct.
A third purpose of creating a storage integration is to store a generated IAM entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account. For example, you can create a storage integration for Amazon S3 even if your Snowflake account is hosted on Azure or Google Cloud Platform. This allows you to access data across different cloud platforms using Snowflake1. Therefore, option B is correct.
Option A is incorrect, because creating a storage integration does not control access to Snowflake data using a master encryption key. Snowflake encrypts all data using a hierarchical key model, and the master encryption key is managed by Snowflake or by the customer using a cloud provider's key management service. This is independent of the storage integration feature2.
Option E is incorrect, because creating a storage integration does not create private VPC endpoints. Private VPC endpoints are a network configuration option that allow direct, secure connectivity between VPCs without traversing the public internet. This is also independent of the storage integration feature3.
Option F is incorrect, because creating a storage integration does not manage credentials from multiple cloud providers in one single Snowflake object. A storage integration is specific to one cloud provider, and you need to create separate integrations for each cloud provider you want to access4.
53. Frage
Which of the following are characteristics of Snowflake's parameter hierarchy?
Antwort: C
Begründung:
In Snowflake's parameter hierarchy, virtual warehouse parameters take precedence over user parameters. This hierarchy is designed to ensure that settings at the virtual warehouse level, which typically reflect the requirements of a specific workload or set of queries, override the preferences set at the individual user level.
This helps maintain consistent performance and resource utilization as specified by the administrators managing the virtual warehouses.References: Snowflake documentation on parameter hierarchy, found in the SnowPro Advanced: Architect learning materials.
54. Frage
Which of the following are characteristics of Snowflake's parameter hierarchy?
Antwort: C
55. Frage
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?
Antwort: C
Begründung:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column- like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi- structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
Considerations for Semi-structured Data Stored in VARIANT
Snowflake Architect Training
Snowflake query performance on unique element in variant column
Snowflake variant performance
56. Frage
What Snowflake features should be leveraged when modeling using Data Vault?
Antwort: B
Begründung:
These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as:
* Snowflake's support of multi-table inserts into the data model's Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading data into Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12.
* Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual warehouses are a feature that allows provisioning compute resources on demand for data processing.
Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance
* and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake's architecture, which can optimize the data loading and querying34.
References:
* Snowflake Documentation: Multi-table Inserts
* Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake
* Snowflake Documentation: Virtual Warehouses
* Snowflake Blog: Building a Real-Time Data Vault in Snowflake
57. Frage
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ARA-C01 Testing Engine: https://www.it-pruefung.com/ARA-C01.html
Übrigens, Sie können die vollständige Version der It-Pruefung ARA-C01 Prüfungsfragen aus dem Cloud-Speicher herunterladen: https://drive.google.com/open?id=1WyYgQ5EUR_a79H74cDJQAC6-SsHzq0qH