You have source data in a folder on a local computer.You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:Support the use of dataflows to load and append data to the data store. Ensure that Delta tables are V-Order optimized and compacted automatically.Which two types of data stores should you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
Answer(s): A,C
Delta Lake table format interoperabilityIn Microsoft Fabric, the Delta Lake table format is the standard for analytics. Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to big data and analytics workloads.All Fabric experiences generate and consume Delta Lake tables, driving interoperability and a unified product experience. Delta Lake tables produced by one compute engine, such as *Synapse Data warehouse* or Synapse Spark, can be consumed by any other engine, such as Power BI. When you ingest data into Fabric, Fabric stores it as Delta tables by default. You can easily integrate external data containing Delta Lake tables by using OneLake shortcuts.The following matrix shows key Delta Lake features and their support on each Fabric capability.Etc.
https://learn.microsoft.com/en-us/fabric/get-started/delta-lake-interoperability
HOTSPOT (Drag and Drop is not supported)You have a Fabric tenant that contains a lakehouse.You are using a Fabric notebook to save a large DataFrame by using the following code.df.write.partitionBy(“year”, “month”, “day”).mode(“overwrite”).parquet(“Files/ SalesOrder”)For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: YesPartitionBy segregates data into folders.NOTE: PySpark partitionBy() is a function of pyspark.sql.DataFrameWriter class which is used to partition the large dataset (DataFrame) into smaller files based on one or multiple columns while writing to disk-Box 2: YesBox 3: No
https://sparkbyexamples.com/pyspark/pyspark-partitionby-example/
You have a Fabric tenant that contains a data pipeline.You need to ensure that the pipeline runs every four hours on Mondays and Fridays. To what should you set Repeat for the schedule?
Answer(s): C
DRAG DROP (Drag and Drop is not supported)You are creating a data flow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.NOTE: Each correct selection is worth one point.Select and Place:
Box 1: ValueQuery folding on native queries Use Value.NativeQuery functionThe goal of this process is to execute the following SQL code, and to apply more transformations with Power Query that can be folded back to the source.SELECT DepartmentID, Name FROM HumanResources.Department WHERE GroupName = 'Research and Development'The first step was to define the correct target, which in this case is the database where the SQL code will be run. Once a step has the correct target, you can select that step—in this case, Source in Applied Steps—and then select the fx button in the formula bar to add a custom step. In this example, replace the Source formula with the following formula:Value.NativeQuery(Source, "SELECT DepartmentID, Name FROM HumanResources.Department WHERE GroupName = 'Research and Development'Box 2: NativeQueryBox 3: EnableFoldingThe most important component of this formula is the use of the optional record for the forth parameter of the function that has the EnableFolding record field set to true.
https://learn.microsoft.com/en-us/power-query/native-query-folding
HOTSPOT (Drag and Drop is not supported)You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following table:You create a Fabric notebook and attach it to Lakehouse1.You need to use PySpark code to transform the data. The solution must meet the following requirements:Add a column named pickupDate that will contain only the date portion of pickupDateTime.Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.How should you complete the code? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: df.withColumnRenamedAdd a column named pickupDate that will contain only the date portion of pickupDateTime.withColumnRenamed(existing, new)[source]Returns a new DataFrame by renaming an existing column. This is a no-op if schema doesn’t contain the given column name.Parameters:existing – string, name of the existing column to rename. col – string, new name of the column.>>> df.withColumnRenamed('age', 'age2').collect() [Row(age2=2, name='Alice'), Row(age2=5, name='Bob')]Incorrect:* df.withColumn withColumn(colName, col)[source]Returns a new DataFrame by adding a column or replacing the existing column that has the same name. The column expression must be an expression over this DataFrame; attempting to add a column from someother dataframe will raise an error.Parameters:colName – string, name of the new column. col – a Column expression for the new column.>>> df.withColumn('age2', df.age + 2).collect()[Row(age=2, name='Alice', age2=4), Row(age=5, name='Bob', age2=7)]Box 2: cast('date') cast(dataType)[source]Convert the column into type dataType.>>> df.select(df.age.cast("string").alias('ages')).collect() [Row(ages='2'), Row(ages='5')]>>> df.select(df.age.cast(StringType()).alias('ages')).collect() [Row(ages='2'), Row(ages='5')]Box 3: .filter("fareAmount > 0 AND fareAmount < 100"Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.filter(condition)[source]Filters rows using the given condition. where() is an alias for filter().>>> df.filter(df.age > 3).collect() [Row(age=5, name='Bob')]>>> df.where(df.age == 2).collect() [Row(age=2, name='Alice')]>>> df.filter("age > 3").collect() [Row(age=5, name='Bob')]>>> df.where("age = 2").collect() [Row(age=2, name='Alice')]Incorrect:*.whereIsin will not give the desired result.NOTE: In Apache Spark, the where() function can be used to filter rows in a DataFrame based on a given condition. The condition is specified as a string that is evaluated for each row in the DataFrame. Rows for which the condition evaluates to True are retained, while those for which it evaluates to False are removed.isin(*cols)[source]A boolean expression that is evaluated to true if the value of this expression is contained by the evaluated values of the arguments.>>> df[df.name.isin("Bob", "Mike")].collect() [Row(age=5, name='Bob')]>>> df[df.age.isin([1, 2, 3])].collect() [Row(age=2, name='Alice')]
https://spark.apache.org/docs/2.3.0/api/python/pyspark.sql.html
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