DP-700 Real Exam Questions

Which two components should you include in the workflow? Each correct answer presents part of the solution.

You need to recommend a solution for handling old files. The solution must meet the technical requirements. What should you include in the recommendation?

Your organization is tasked with transforming a dataset containing semi-structured JSON data into a structured tabular format. The workflow includes: - Parsing the nested JSON data into individual columns. - Applying advanced transformations such as pivoting rows into columns and calculating aggregated metrics. - Writing custom Python logic to handle outliers and fill missing values. Which tool in Microsoft Fabric would be the most appropriate for this transformation task?

You have an Azure event hub. Each event contains the following fields: - BikepointID - Street - Neighbourhood - Latitude - Longitude - No_Bikes - No_Empty_Docks You need to ingest the events. The solution must only retain events that have a Neighbourhood value of Chelsea, and then store the retained events in a Fabric lakehouse. What should you use?

You have the following code segment: def loading_pattern_sample(df_source): try: deltatable = DeltaTable.forName(spark, target_table) except Exception as e: df_source.write.format('delta').mode('overwrite').saveAsTable(f'{target_table}') except Exception as e: print(f'load for table {target_table} failed with error: {str(e)}') return try: change_detection_columns = [col for col in df_source.columns if col not in candidate_key] match_condition = ' AND '.join([f'target.{col} = source.{col}' for col in candidate_key]) update_condition = ' OR '.join([f'target.{col} != source.{col}' for col in change_detection_columns]) update_expr = {col: f'source.{col}' for col in df_source.columns} merge_operation = deltatable.alias('target').merge( source=df_source.alias('source'), condition=match_condition ).whenMatchedUpdate( condition=update_condition, set=update_expr ).whenNotMatchedInsertAll() merge_operation.execute() except Exception as e: print(f'insert operation for table {target_table} failed with error: {str(e)}') return Based on the code, does the loading pattern support both full and incremental loading requirements?