Ingest and Transform Data DP-203 Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   In Azure Synapse Analytics, which language is used to perform data transformation within the dedicated SQL pool?

   • AC#
   • BPython
   • CScala
   • DTransact-SQL (T-SQL)
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2. 27application · medium

   An e-commerce company receives order data as JSON files. Each file contains an array of orders, and each order has a nested 'items' array. The data is loaded into Azure Synapse Analytics using T-SQL. You need to shred the JSON to create a relational table with one row per order item. What should you do?

   • AUse the STRING_SPLIT function to split the items array by comma.
   • BUse the OPENJSON function with a CROSS APPLY to expand the items array.
   • CUse the JSON_QUERY function to extract the items array as a JSON fragment.
   • DUse the JSON_VALUE function to extract the items array as a string.
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4. 28application · medium

   A media company receives clickstream logs in JSON format with nested fields. They use Azure Synapse Analytics with Apache Spark pools to process the data. The data contains duplicate events and inconsistent field names (e.g., 'user_id' vs 'userId'). You need to transform the data into a clean, normalized tabular format for analysis. What should you do?

   • AUse a Synapse pipeline with a Copy activity to load the JSON as-is, then use T-SQL to query the raw JSON.
   • BUse Spark SQL to flatten the JSON, rename columns to a consistent naming convention, and use dropDuplicates() on the event ID column.
   • CUse a Data Flow in Azure Data Factory to flatten JSON and remove duplicates, but do not rename columns.
   • DUse Spark to write the JSON directly to a Parquet table without any transformations.
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5. 29foundation · easy

   Which mechanism in Azure Stream Analytics allows you to specify how long to wait for late-arriving events?

   • AInput partitioning
   • BOutput serialization
   • CLate arrival policy
   • DReference data join
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6. 30application · medium

   A data analyst needs to explore a large dataset to identify patterns and outliers before building a machine learning model. The dataset is stored in Azure Data Lake Storage Gen2. You need to perform exploratory data analysis (EDA) using Apache Spark. What should you do?

   • AUse Spark DataFrames to compute summary statistics, check for missing values, and visualize distributions.
   • BUse Azure Stream Analytics to analyze the data in real time.
   • CUse T-SQL to run aggregate queries on the data.
   • DUse Azure Data Factory to copy the data to a SQL database and then use Power BI.
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