DP-203 Free Practice Test

- (Exam Topic 3)
You are developing a solution using a Lambda architecture on Microsoft Azure. The data at test layer must meet the following requirements:
Data storage:
•Serve as a repository (or high volumes of large files in various formats.
•Implement optimized storage for big data analytics workloads.
•Ensure that data can be organized using a hierarchical structure. Batch processing:
•Use a managed solution for in-memory computation processing.
•Natively support Scala, Python, and R programming languages.
•Provide the ability to resize and terminate the cluster automatically. Analytical data store:
•Support parallel processing.
•Use columnar storage.
•Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Solution:
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage. References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You are creating an Azure Data Factory data flow that will ingest data from a CSV file, cast columns to specified types of data, and insert the data into a table in an Azure Synapse Analytics dedicated SQL pool. The CSV file contains columns named username, comment and date.
The data flow already contains the following:
• A source transformation
• A Derived Column transformation to set the appropriate types of data
• A sink transformation to land the data in the pool
You need to ensure that the data flow meets the following requirements;
• All valid rows must be written to the destination table.
• Truncation errors in the comment column must be avoided proactively.
• Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point

1. A. Add a select transformation that selects only the rows which will cause truncation errors.
2. B. Add a sink transformation that writes the rows to a file in blob storage.
3. C. Add a filter transformation that filters out rows which will cause truncation errors.
4. D. Add a Conditional Split transformation that separates the rows which will cause truncation errors.

Correct Answer: BD

- (Exam Topic 3)
You plan to create an Azure Data Factory pipeline that will include a mapping data flow. You have JSON data containing objects that have nested arrays.
You need to transform the JSON-formatted data into a tabular dataset. The dataset must have one tow for each item in the arrays.
Which transformation method should you use in the mapping data flow?

1. A. unpivot
2. B. flatten
3. C. new branch
4. D. alter row

Correct Answer: B
Use the flatten transformation to take array values inside hierarchical structures such as JSON and unroll them into individual rows. This process is known as denormalization.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-flatten

- (Exam Topic 3)
You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool named SQLPool1.
SQLPool1 is currently paused.
You need to restore the current state of SQLPool1 to a new SQL pool. What should you do first?

1. A. Create a workspace.
2. B. Create a user-defined restore point.
3. C. Resume SQLPool1.
4. D. Create a new SQL pool.

Correct Answer: B
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-restore-active

- (Exam Topic 3)
You are designing an anomaly detection solution for streaming data from an Azure IoT hub. The solution must meet the following requirements:
Send the output to Azure Synapse.
Identify spikes and dips in time series data.
Minimize development and configuration effort. Which should you include in the solution?

1. A. Azure Databricks
2. B. Azure Stream Analytics
3. C. Azure SQL Database

Correct Answer: B
You can identify anomalies by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics. Reference:
https://docs.microsoft.com/en-us/learn/modules/data-anomaly-detection-using-azure-iot-hub/