최신Databricks Certified Data Analyst Associate - Databricks-Certified-Data-Analyst-Associate무료샘플문제

A. tag:confidential

B. confidential:true

C. search tag = ' confidential '

D. tag=confidential

A. Identify the version number corresponding to two weeks ago from the Delta transaction log, share that version number with the analyst to query using VERSION AS OF syntax, or export that version to a new Delta table for the analyst to query.

B. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.

C. Restore the table to the version from two weeks ago using the RESTORE command, and have the analyst query the restored table.

D. Use the VACUUM command to remove all versions of the table older than two weeks, then the analyst can query the remaining version.

A. The command fails because it is written incorrectly.

B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.

C. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.

D. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.

E. The suppliers table now contains only the data from the new suppliers table.

A. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.

B. All files with the .customers extension are deleted.

C. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.

D. Running SELECT * FROM accounts.customers will return all rows in the table.

E. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.

A. Automating complex notebook-based workflows with multiple tasks

B. Testing the quality of data as it is imported from a source

C. Segmenting customers into like groups using a clustering algorithm

D. Tracking usage of feature variables for machine learning projects

E. Combining two data sources into a single, comprehensive dataset

A. ( spark.readStream.load(rawSalesLocation) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

B. Option A

C. Option C

D. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

E. Option B

F. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

G. Option E

H. ( spark.table( " sales " ) .groupBy( " store " ) .agg(sum( " sales " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " complete " ) .table( " newSales " ))

I. ( spark.table( " sales " ) .filter(col( " units " ) > 0) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

J. Option D

A. Edit the Owner field in the table page by selecting the Admins group

B. Edit the Owner field in the table page by selecting the new owner ' s account

C. Edit the Owner field in the table page by removing their own account

D. Edit the Owner field in the table page by selecting All Users

E. Edit the Owner field in the table page by removing all access

A. Line charts are the preferred visualization type for categorical data.

B. All visualizations must use the same data in order to be included in the same Databricks SQL dashboard.

C. There is no difference between the bar chart and a histogram in Databricks SQL.

D. Different visualizations can be used to tell different stories about the data.

A. A dashboard

B. A data visualization tab on a Query page

C. A Query History page

D. A SQL warehouse