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Related Exam
Passitcerts Providing most updated Data Engineering on Microsoft Azure Certification Question Answers. Here are a few exams:
You have an Azure subscription that contains a Microsoft Purview account.You need to search the Microsoft Purview Data Catalog to identify assets that have anassetType property of Table or View Which query should you run?
A. assetType IN (Table', 'View')
B. assetType:Table OR assetType:View
C. assetType - (Table or view)
D. assetType:(Table OR View)
Answer: B
Question # 2
You have an Azure subscription that contains an Azure data factory named ADF1.From Azure Data Factory Studio, you build a complex data pipeline in ADF1.You discover that the Save button is unavailable and there are validation errors thatprevent the pipeline from being published.You need to ensure that you can save the logic of the pipeline.Solution: You enable Git integration for ADF1.
A. Yes
B. No
Answer: B
Question # 3
Note: The question is part of a series of questions that present the same scenario. Eachquestion in the series contains a unique solution that might meet the stated goals. Somequestion sets might have more than one correct solution, while others might not have acorrect solution.After you answer a question in this section, you will NOT be able to return to it As a resultthese questions will not appear in the review screen. You have an Azure Data LakeStorage account that contains a staging zone.You need to design a dairy process to ingest incremental data from the staging zone,transform the data by executing an R script, and then insert the transformed data into adata warehouse in Azure Synapse Analytics.Solution: You use an Azure Data Factory schedule trigger to execute a pipeline thatexecutes a mapping data low. and then inserts the data into the data warehouse.Does this meet the goal?
A. Yes
B. No
Answer: B
Question # 4
You are creating an Apache Spark job in Azure Databricks that will ingest JSON-formatteddata.You need to convert a nested JSON string into a DataFrame that will contain multiple rows.Which Spark SQL function should you use?
A. explode
B. filter
C. coalesce
D. extract
Answer: A
Explanation:
Convert nested JSON to a flattened DataFrame
You can to flatten nested JSON, using only $"column.*" and explode methods.
Note: Extract and flatten
Use $"column.*" and explode methods to flatten the struct and array types before
displaying the flattened DataFrame.
Scala
display(DF.select($"id" as "main_id",$"name",$"batters",$"ppu",explode($"topping")) //
Exploding the topping column using explode as it is an array type
withColumn("topping_id",$"col.id") // Extracting topping_id from col using DOT form
withColumn("topping_type",$"col.type") // Extracting topping_tytpe from col using DOT form
drop($"col")
select($"*",$"batters.*") // Flattened the struct type batters tto array type which is batter
drop($"batters")
select($"*",explode($"batter"))
drop($"batter")
withColumn("batter_id",$"col.id") // Extracting batter_id from col using DOT form
withColumn("battter_type",$"col.type") // Extracting battter_type from col using DOT form
You have an Azure Synapse Analytics dedicated SQL pool named pool1.You plan to implement a star schema in pool1 and create a new table named DimCustomerby using the following code.
You need to ensure that DimCustomer has the necessary columns to support a Type 2 slowly changing dimension (SCD). Which two columns should you add? Each correctanswer presents part of the solution. NOTE: Each correct selection is worth one point.
A. [HistoricalSalesPerson] [nvarchar] (256) NOT NULL
B. [EffectiveEndDate] [datetime] NOT NULL
C. [PreviousModifiedDate] [datetime] NOT NULL
D. [RowID] [bigint] NOT NULL
E. [EffectiveStartDate] [datetime] NOT NULL
Answer: A,B
Question # 6
You have an Azure Synapse Analytics dedicated SQL pool.You plan to create a fact table named Table1 that will contain a clustered columnstoreindex.You need to optimize data compression and query performance for Table1.What is the minimum number of rows that Table1 should contain before you createpartitions?
A. 100.000
B. 600,000
C. 1 million
D. 60 million
Answer: A
Question # 7
You have the Azure Synapse Analytics pipeline shown in the following exhibit.
You need to add a set variable activity to the pipeline to ensure that after the pipeline’s completion, the status of the pipeline is always successful.What should you configure for the set variable activity?
A. a success dependency on the Business Activity That Fails activity
B. a failure dependency on the Upon Failure activity
C. a skipped dependency on the Upon Success activity
D. a skipped dependency on the Upon Failure activity
Answer: A
Explanation: A failure dependency means that the activity will run only if the previous
activity fails. In this case, setting a failure dependency on the Upon Failure activity will
ensure that the set variable activity will run after the pipeline fails and set the status of the
pipeline to successful.
Question # 8
Note: This question is part of a series of questions that present the same scenario.Each question in the series contains a unique solution that might meet the statedgoals. Some question sets might have more than one correct solution, while othersmight not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As aresult, these questions will not appear in the review screen.You have an Azure Data Lake Storage account that contains a staging zone.You need to design a daily process to ingest incremental data from the staging zone,transform the data by executing an R script, and then insert the transformed data into adata warehouse in Azure Synapse Analytics.Solution: You schedule an Azure Databricks job that executes an R notebook, and theninserts the data into the data warehouse.Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Must use an Azure Data Factory, not an Azure Databricks job.
You are implementing a star schema in an Azure Synapse Analytics dedicated SQL pool.You plan to create a table named DimProduct. DimProduct must be a Type 3 slowly changing dimension (SCO) table that meets thefollowing requirements:• The values in two columns named ProductKey and ProductSourceID will remain thesame.• The values in three columns named ProductName, ProductDescription, and Color canchange.You need to add additional columns to complete the following table definition.
A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
F. Option F
Answer: A,B,C
Question # 10
You plan to use an Apache Spark pool in Azure Synapse Analytics to load data to an AzureData Lake Storage Gen2 account.You need to recommend which file format to use to store the data in the Data Lake Storageaccount. The solution must meet the following requirements:• Column names and data types must be defined within the files loaded to the Data LakeStorage account.• Data must be accessible by using queries from an Azure Synapse Analytics serverlessSQL pool.• Partition elimination must be supported without having to specify a specific partition.What should you recommend?
A. Delta Lake
B. JSON
C. CSV
D. ORC
Answer: D
Question # 11
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains atable named Sales. Sales has row-level security (RLS) applied. RLS uses the followingpredicate filter.
A user named SalesUser1 is assigned the db_datareader role for Pool1. Which rows in theSales table are returned when SalesUser1 queries the table?
A. only the rows for which the value in the User_Name column is SalesUser1
B. all the rows
C. only the rows for which the value in the SalesRep column is Manager
D. only the rows for which the value in the SalesRep column is SalesUser1
Answer: A
Question # 12
You are designing 2 solution that will use tables in Delta Lake on Azure Databricks.You need to minimize how long it takes to perform the following:*Queries against non-partitioned tables* Joins on non-partitioned columnsWhich two options should you include in the solution? Each correct answer presents part ofthe solution.(Choose Correct Answer and Give Explanation and References to Support the answersbased from Data Engineering on Microsoft Azure)
A. Z-Ordering
B. Apache Spark caching
C. dynamic file pruning (DFP)
D. the clone command
Answer: A,C
Explanation: According to the information I found on the web, two options that you should
include in the solution to minimize how long it takes to perform queries and joins on nonpartitioned
tables are:
Z-Ordering: This is a technique to colocate related information in the same set of
files. This co-locality is automatically used by Delta Lake in data-skipping
algorithms. This behavior dramatically reduces the amount of data that Delta Lake
on Azure Databricks needs to read123.
Apache Spark caching: This is a feature that allows you to cache data in memory
or on disk for faster access. Caching can improve the performance of repeated
queries and joins on the same data. You can cache Delta tables using the CACHE
TABLE or CACHE LAZY commands. To minimize the time it takes to perform queries against non-partitioned tables and joins on
non-partitioned columns in Delta Lake on Azure Databricks, the following options should be
included in the solution:
A. Z-Ordering: Z-Ordering improves query performance by co-locating data that share the
same column values in the same physical partitions. This reduces the need for shuffling
data across nodes during query execution. By using Z-Ordering, you can avoid full table
scans and reduce the amount of data processed.
B. Apache Spark caching: Caching data in memory can improve query performance by
reducing the amount of data read from disk. This helps to speed up subsequent queries
that need to access the same data. When you cache a table, the data is read from the data
source and stored in memory. Subsequent queries can then read the data from memory,
Note: This question is part of a series of questions that present the same scenario.Each question in the series contains a unique solution that might meet the statedgoals. Some question sets might have more than one correct solution, while othersmight not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As aresult, these questions will not appear in the review screen.You are designing an Azure Stream Analytics solution that will analyze Twitter data.You need to count the tweets in each 10-second window. The solution must ensure thateach tweet is counted only once.Solution: You use a tumbling window, and you set the window size to 10 seconds.Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time
intervals. The following diagram illustrates a stream with a series of events and how they
You have an Azure subscription that contains an Azure Blob Storage account namedstorage1 and an Azure Synapse Analytics dedicated SQL pool named Pool1.You need to store data in storage1. The data will be read by Pool1. The solution must meetthe following requirements:Enable Pool1 to skip columns and rows that are unnecessary in a query.Automatically create column statistics.Minimize the size of files.Which type of file should you use?
A. JSON
B. Parquet
C. Avro
D. CSV
Answer: B
Explanation:
Automatic creation of statistics is turned on for Parquet files. For CSV files, you need to
create statistics manually until automatic creation of CSV files statistics is supported.
You have an Azure Databricks workspace that contains a Delta Lake dimension tablenamed Tablet. Table1 is a Type 2 slowly changing dimension (SCD) table. You need toapply updates from a source table to Table1. Which Apache Spark SQL operation shouldyou use?
A. CREATE
B. UPDATE
C. MERGE
D. ALTER
Answer: C
Explanation:
The Delta provides the ability to infer the schema for data input which further reduces the
effort required in managing the schema changes. The Slowly Changing Data(SCD) Type 2
records all the changes made to each key in the dimensional table. These operations
require updating the existing rows to mark the previous values of the keys as old and then
inserting new rows as the latest values. Also, Given a source table with the updates and
the target table with dimensional data, SCD Type 2 can be expressed with the merge.
Example:
// Implementing SCD Type 2 operation using merge function
customersTable
as("customers")
merge(
stagedUpdates.as("staged_updates"),
"customers.customerId = mergeKey")
whenMatched("customers.current = true AND customers.address <>
Thank you so much PassITCerts for helped me to clear the DP-203 exam. The course material was extensive and covered all the major topics. And I was well-prepared for the test and passed with a decent score.
Emma Roberts
Apr 22, 2025
Thank you so much PassITCerts for helped me to clear the DP-203 exam. The course material was extensive and covered all the major topics. And I was well-prepared for the test and passed with a decent score.