Databricks Certified Data Analyst Associate - Databricks-Certified-Data-Analyst-Associate 模擬練習

A. Predicting customer churn by automatically developing time-series models

B. Monitoring the performance of deployed machine learning solutions

C. Performing analysis-specific ETL on gold-layer tables

D. Testing the latency of real-time streaming workloads

A. Option A

B. Option C

C. SELECT region, product, RANK() OVER ( PARTITION BY region ) AS rankFROM sales_table;

D. SELECT region, product, RANK() OVER ( PARTITION BY region ORDER BY sales DESC ) AS rankFROM sales_table;

E. SELECT region, product, RANK() OVER ( ORDER BY sales DESC ) AS rankFROM sales_table;

F. Option B

G. SELECT region, product, RANK() OVER ( PARTITION BY product ORDER BY sales DESC ) AS rankFROM sales_table;

H. Option D

A. Scalable storage

B. Flexible schemas

C. Data deletion

D. ACID transactions

E. Open-source formats

A. Ability to scale storage

B. Cloud-specific integrations

C. Avoiding vendor lock-in

D. Ability to scale workloads

E. Simplified governance

A. Queries that use query parameters cannot be used with Alerts.

B. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.

C. Queries that return results based on dates cannot be used with Alerts.

D. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.

E. Alerts don ' t work with queries that access tables.

A. They will need to copy the Query and create one data visualization per query.

B. They will need to add two separate visualizations to the dashboard based on the same Query.

C. They will need to create two separate dashboards.

D. They will need to alter the Query to return two separate sets of results.

E. They will need to decide on a single data visualization to add to the dashboard.

A. spark.delta.table( " sales " )

B. SELECT * FROM sales

C. spark.sql( " sales " )

D. spark.table( " sales " )

A. Option A

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

C. Option C

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

E. Option E

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

G. Option B

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

I. Option D

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

A. Use a local Python script with the pandas library to read and analyze the CSV file.

B. Set up a Hadoop ecosystem with HDFS and MapReduce for distributed processing.

C. Deploy a real-time streaming solution using Spark Streaming to process incoming data.

D. Implement Apache Spark with a distributed cluster to process the data in parallel.

A. dates_df.withColumn( " date " , f.to_date( " timestamp " )).show()

B. dates_df.withColumn( " date " , f.unix_timestamp( " timestamp " )).show()

C. dates_df.withColumn( " date " , f.from_unixtime( " timestamp " )).show()

D. dates_df.withColumn( " date " , f.date_format( " timestamp " , " yyyy-MM-dd " )).show()