Latest ARA-C01 PDF Questions & Passing ARA-C01 Exam is No More a Challenging Task

What's more, part of that It-Tests ARA-C01 dumps now are free: https://drive.google.com/open?id=1S9liDoaXU2Nnfj2QMONG1SbKrqSFowM8

We offer three different formats for preparing for the Snowflake ARA-C01 exam questions, all of which will ensure your definite success on your SnowPro Advanced Architect Certification (ARA-C01) exam dumps. It-Tests is there with updated ARA-C01 Questions so you can pass the SnowPro Advanced Architect Certification (ARA-C01) exam and move toward the new era of technology with full ease and confidence.

Snowflake ARA-C01 certification exam is designed to validate the advanced skills and knowledge of individuals in designing and implementing complex Snowflake data warehousing solutions. SnowPro Advanced Architect Certification certification exam is intended for Snowflake architects who have experience in designing and implementing Snowflake solutions in a variety of environments. The Snowflake ARA-C01 Certification Exam is a challenging exam that requires a good understanding of Snowflake architecture, data modeling, and performance optimization.

>> ARA-C01 PDF Questions <<

Exam Snowflake ARA-C01 Reviews, Valid ARA-C01 Test Vce

It-Tests is one of the trusted and reliable platforms that is committed to offering quick SnowPro Advanced Architect Certification (ARA-C01) exam preparation. To achieve this objective It-Tests is offering valid, updated, and real SnowPro Advanced Architect Certification (ARA-C01) exam questions. These Snowflake exam dumps will provide you with everything that you need to prepare and pass the final Snowflake ARA-C01 exam with flying colors.

Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a certification exam designed for professionals who want to demonstrate their advanced-level skills in Snowflake architecture and design. It is a comprehensive exam that covers various topics such as data modeling, data warehousing, data security, performance optimization, and Snowflake administration.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q59-Q64):

NEW QUESTION # 59
To increase performance, materialized views can be created on external table without any additional cost

A. TRUE
B. FALSE

Answer: B

NEW QUESTION # 60
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?

A. Information schema
B. Schema-on-write
C. IDEF1X
D. Schema-on-read

Answer: D

Explanation:
Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi- structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi- structured data at the same speed as performing relational queries while preserving the flexibility of schema- on-read. Snowflake's near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.
Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi- structured data, which does not have a fixed schema or structure.
Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on- write also introduces additional overhead and complexity for data transformation and validation.
Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.
References:
Semi-structured Data
Snowflake for Data Lake

NEW QUESTION # 61
A company is trying to Ingest 10 TB of CSV data into a Snowflake table using Snowpipe as part of Its migration from a legacy database platform. The records need to be ingested in the MOST performant and cost-effective way.
How can these requirements be met?

A. Use purge = TRUE in the copy into command.
B. Use FURGE = FALSE in the copy into command.
C. Use ON_ERROR = continue in the copy into command.
D. Use on error = SKIP_FILE in the copy into command.

Answer: D

Explanation:
For ingesting a large volume of CSV data into Snowflake using Snowpipe, especially for a substantial amount like 10 TB, the on error = SKIP_FILE option in the COPY INTO command can be highly effective. This approach allows Snowpipe to skip over files that cause errors during the ingestion process, thereby not halting or significantly slowing down the overall data load. It helps in maintaining performance and cost-effectiveness by avoiding the reprocessing of problematic files and continuing with the ingestion of other data.

NEW QUESTION # 62
An Architect is troubleshooting a query with poor performance using the QUERY function. The Architect observes that the COMPILATION_TIME Is greater than the EXECUTION_TIME.
What is the reason for this?

A. The query has overly complex logic.
B. The query Is reading from remote storage
C. The query is processing a very large dataset.
D. The query Is queued for execution.

Answer: A

Explanation:
* The correct answer is B because the compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. The compilation time depends on the complexity of the query, such as the number of tables, columns, joins, filters, aggregations, subqueries, etc. The more complex the query, the longer it takes to compile.
* Option A is incorrect because the query processing time is not affected by the size of the dataset, but by the size of the virtual warehouse. Snowflake automatically scales the compute resources to match the data volume and parallelizes the query execution. The size of the dataset may affect the execution time, but not the compilation time.
* Option C is incorrect because the query queue time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query waits for a warehouse slot before it starts running. The query queue time depends on the warehouse load, concurrency, and priority settings.
* Option D is incorrect because the query remote IO time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query spends reading data from remote storage, such as S3 or Azure Blob Storage. The query remote IO time depends on the network latency, bandwidth, and caching efficiency. References:
* Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time: This article explains why the total duration (compilation + execution) time is an essential metric to measure query performance in Snowflake. It discusses the reasons for the long compilation time, including query complexity and the number of tables and columns.
* Exploring Execution Times: This document explains how to examine the past performance of queries and tasks using Snowsight or by writing queries against views in the ACCOUNT_USAGE schema. It also describes the different metrics and dimensions that affect query performance, such as duration, compilation, execution, queue, and remote IO time.
* What is the "compilation time" and how to optimize it?: This community post provides some tips and best practices on how to reduce the compilation time, such as simplifying the query logic, using views or common table expressions, and avoiding unnecessary columns or joins.

NEW QUESTION # 63
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schem a. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)

A. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.
B. The tables exceed the 1 TB limit for data recovery.
C. The staging schema has not been setup for MANAGED ACCESS.
D. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
E. The staging tables are of the TRANSIENT type.

Answer: D,E

Explanation:
The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1.
The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.
Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.
Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.
Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.
Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.
Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.

NEW QUESTION # 64
......

Exam ARA-C01 Reviews: https://www.it-tests.com/ARA-C01.html

BONUS!!! Download part of It-Tests ARA-C01 dumps for free: https://drive.google.com/open?id=1S9liDoaXU2Nnfj2QMONG1SbKrqSFowM8

Warenkorb-Übersicht0

Es sind keine Produkte in deinem Warenkorb!

Weiter einkaufen