Databricks Certified Data Analyst Associate : Databricks-Certified-Data-Analyst-Associate

Which statement about visualizations is true?

• A. There is no difference between the bar chart and a histogram in Databricks SQL.
• B. Line charts are the preferred visualization type for categorical data.
• C. Different visualizations can be used to tell different stories about the data.
• D. All visualizations must use the same data in order to be included in the same Databricks SQL dashboard.

In a healthcare provider organization using Delta Lake to store electronic health records, a data analyst needs to analyze a snapshot of the patient_records table from two weeks ago before some recent data corrections were applied.
What approach should the Data Engineer take to allow the analyst to query that specific prior version?

• 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. Use the VACUUM command to remove all versions of the table older than two weeks, then the analyst can query the remaining version.
• C. Restore the table to the version from two weeks ago using the RESTORE command, and have the analyst query the restored table.
• D. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.

Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

• A. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
• B. Option B
• C. Option C
• D. ( spark.table( " sales " ) .groupBy( " store " ) .agg(sum( " sales " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " complete " ) .table( " newSales " ))
• E. ( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option ( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
• F. Option E
• G. Option D
• H. Option A
• I. ( spark.readStream.load(rawSalesLocation) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))
• J. ( spark.table( " sales " ) .filter(col( " units " ) > 0) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

A data analyst at an e-commerce company needs to process daily sales data. The data consists of approximately 50,000 records stored in a single CSV file, totaling about 20 MB. The analyst needs to perform aggregations and generate a summary report.
Which approach could the data analyst use in this situation?

• A. Set up a Hadoop ecosystem with HDFS and MapReduce for distributed processing.
• B. Implement Apache Spark with a distributed cluster to process the data in parallel.
• C. Deploy a real-time streaming solution using Spark Streaming to process incoming data.
• D. Use a local Python script with the pandas library to read and analyze the CSV file.

A data analyst has written and saved a series of queries that reveal trends that need to be monitored by several stakeholders.
Which tool should the data analyst use to share the results of all of the queries to be viewed at once?

• A. A data visualization tab on a Query page
• B. A SQL warehouse
• C. A dashboard
• D. A Query History page