ARA-C01 practice questions 121–130

1. Question 121 · 193 in bank

   The result cache is not being used on the following query: select * from ORDERS Where O_ORDERDATE = date_trunc('day', current_timestamp()); The query is run hundreds of times a day by several different Analyst teams. The team members use different roles and warehouses. The table is updated once every month. What can be done to ensure that the query uses the result cache?

   • ACreate a view on the table with the definition create view curr_orders as select * from ORDERS Where O_ORDERDATE = date_trunc ('day', current_timestamp ()); Then use the query SELECT * from curr_orders;
   • BHave all users reuse the same virtual warehouse.
   • CAssign the same user role to all Analysts.
   • DChange the query to select * from ORDERS Where O_ORDERDATE = current_date ();

   Show answer

   Answer: D

   Next question

2. Question 122 · 193 in bank

   Select 2

   Which parameters can only be set at the account level? (Choose two.)

   • ADATA_RETENTION_TIME_IN_DAYS
   • BENFORCE_SESSION_POLICY
   • CMAX_CONCURRENCY_LEVEL
   • DPERIODIC_DATA_REKEYING
   • ETIMESTAMP_INPUT_FORMAT

   Show answer

   Answer: BD

   Next question

3. Question 123 · 193 in bank

   An Architect needs to define a storage solution for data tables with the following requirements: 1. Has the ability to use Time Travel across the tables. 2. Follows Atomicity, Consistency, Isolation, and Durability (ACID) compliance requirements. 3. The table format supports various query engines. Which solution meets these requirements?

   • AExternal table with a materialized view
   • BExternal table with a public Amazon S3 storage-compatible format
   • CSnowflake permanent table format using Enterprise edition
   • DSnowflake Apache Iceberg tables in a non-external format

   Show answer

   Answer: D

   Next question

4. Question 124 · 193 in bank

   Select 2

   A company wants to allow their Data Engineers, Data Scientists, and Developers to interact with their data within Snowflake and require the following: • Build applications using Python that bring their data to life for non-technical/business users. • Build a ML model for predictions. What should an Architect recommend for use? (Choose two.)

   • ACreate visualizations and charts in Snowsight.
   • BCreate visualizations and graphs in Tableau.
   • CCreate and display interactive charts in Streamlit.
   • DConfigure a data pipeline to get the extracted data.
   • ECreate Snowpark DataFrames and perform data transformations.

   Show answer

   Answer: CE

   Next question

5. Question 125 · 193 in bank

   Three tables (A, B and C) are loaded with an INSERT statement. Each INSERT statement is executed by a separate Snowflake task: TASK_A, TASK_B, or TASK_C. All tasks are scheduled for a daily execution at the same time. TASK_C has the longest average execution duration. How can an Architect create an aggregated data object based on a join of tables A, B and C?

   • ACreate a materialized view based on a join of tables A, B and C.
   • BCreate a task, TASK_D, to join tables A, B and C and INSERT the data into a target table. Set the predecessor of TASK_D to TASK_C.
   • CCreate a task, TASK_D, to join tables A, B and C and INSERT the data into a target table. Set the predecessor of TASK_D to TASK_A, TASK_B, and TASK_C.
   • DCreate a materialized view based on a join of tables A, B and C. Create TASK_D to execute a refresh of the materialized view once each hour.

   Show answer

   Answer: C

   Next question

6. Question 126 · 193 in bank

   What symptom of a join explosion is identified in the Query Profile?

   • AThe number of rows output from a join operator is significantly more than the number of rows it consumed.
   • BThe join completes in under 1% of the execution time.
   • CThere is no join filter below the exploding join.
   • DThe number of result rows is less than the number of rows output from the exploding join.

   Show answer

   Answer: A

   Next question

7. Question 127 · 193 in bank

   An EVENT table has 150 B rows and 1.5 M micro-partitions, with the following statistics:

   

   What three clustering keys should be used, in order?

   • AC_DATE, A_ID, NAME
   • BA_ID, NAME, C_DATE
   • CC_DATE, A_ID, EVENT_ACT_0
   • DC_DATE, A_ID, EVENT_ACT_4

   Show answer

   Answer: A

   Next question

8. Question 128 · 193 in bank

   An Architect needs to improve the performance of reports that pull data from multiple Snowflake tables, join, and then aggregate the data. Users access the reports using several dashboards. There are performance issues on Monday mornings between 9:00am-11:00am when many users check the sales reports. The size of the group has increased from 4 to 8 users. Waiting times to refresh the dashboards has increased significantly. Currently this workload is being served by a virtual warehouse with the following parameters: AUTO-RESUME = TRUE - AUTO_SUSPEND = 60 - SIZE = Medium - What is the MOST cost-effective way to increase the availability of the reports?

   • AUse materialized views and pre-calculate the data.
   • BIncrease the warehouse to size Large and set AUTO_SUSPEND = 600.
   • CUse a multi-cluster warehouse in maximized mode with 2 size Medium clusters.
   • DUse a multi-cluster warehouse in auto-scale mode with 1 size Medium cluster, and set MIN_CLUSTER_COUNT = 1 and MAX_CLUSTER_COUNT = 4.

   Show answer

   Answer: D

   Next question

9. Question 129 · 193 in bank

   An Architect is having query performance issues with data stored in external tables and wants to try partitioning the data. How should the data be partitioned?

   • APartition the data based on the source file size to evenly distribute the data across multiple partitions.
   • BPartition the data based on a specific column that is frequently used in queries to reduce data scanning.
   • CPartition the data based on the file format used for storing the data in external tables.
   • DPartition the data randomly to ensure equal distribution across all available compute resources.

   Show answer

   Answer: B

   Next question

10. Question 130 · 193 in bank

    An Architect has designed a data pipeline that is receiving small CSV files from multiple sources. All of the files are landing in one location. Specific files are filtered for loading into Snowflake tables using the COPY command. The loading performance is poor. What changes can be made to improve the data loading performance?

    • AIncrease the size of the virtual warehouse.
    • BCreate a multi-cluster warehouse and merge smaller files to create bigger files.
    • CCreate a specific storage landing bucket to avoid file scanning.
    • DChange the file format from CSV to JSON.

    Show answer

    Answer: B

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

ARA-C01 practice, question 121. 193 questions available.