Azure Data Engineer (D305)

Azure Data Engineer (D305)

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Free Azure Data Engineer (D305) Questions

1.

 As a data engineer tasked with managing data ingestion in Azure cloud platforms, which data processing approach is most suitable for handling large volumes of data efficiently

  • Online analytical processing (OLAP)

  • Extract, transform, and load (ETL)

  • Extract, load, and transform (ELT)

  • Batch processing

Explanation

Correct Answer C. Extract, load, and transform (ELT)

Explanation

The ELT approach is often the most suitable for handling large volumes of data efficiently in cloud environments. This approach involves extracting data, loading it into a target system (such as a data lake or data warehouse), and then applying transformations within the target system. Cloud platforms, such as Azure Synapse Analytics, are optimized for large-scale data processing, allowing for fast, parallelized transformations after the data is loaded, which is ideal for big data workflows.

Why other options are wrong

A. Online analytical processing (OLAP)

OLAP is more focused on multidimensional analysis and reporting, and it is not a data processing technique for handling large volumes of data. While useful for querying and analyzing data after it is processed, OLAP doesn't efficiently handle the raw data ingestion or transformation process itself.

B. Extract, transform, and load (ETL)

ETL involves extracting, transforming, and then loading data. While it works for many use cases, it is generally less efficient for large volumes of data in cloud environments compared to ELT. ELT takes advantage of the cloud's processing power to handle transformations more efficiently after loading the data, rather than transforming it first.

D. Batch processing

Batch processing can be useful for handling large volumes of data, but ELT generally offers better performance when working in cloud platforms because transformations can be done on the fly once the data is in the cloud storage or database, rather than needing to batch the data for transformation before loading.


2.

 Which SQL command is used to update existing records and insert new records in a Databricks Delta table based on a specified condition

  • INSERT INTO

  • UPDATE

  • MERGE INTO

  • UPSERT INTO

Explanation

Correct Answer C. MERGE INTO

Explanation

The MERGE INTO command in Databricks Delta is used to perform an upsert operation, which means it will update existing records based on a specified condition and insert new records if they do not exist. This command is essential for handling complex updates and inserts in Delta Lake tables in a single, atomic operation.

Why other options are wrong

A. INSERT INTO

The INSERT INTO command is used to add new rows to a table, but it does not update existing records. It cannot perform an upsert, which is a combination of insert and update.

B. UPDATE

The UPDATE command is used to modify existing records in a table. It does not insert new records if they do not exist.

D. UPSERT INTO

While "upsert" is a common term for combining insert and update operations, UPSERT INTO is not a valid SQL command in Databricks. The correct command for this purpose in Databricks is MERGE INTO.


3.

 You plan to ingest streaming social media data by using Azure Stream Analytics. The data will be stored in files in Azure Data Lake Storage, and then consumed by using Azure Databricks and PolyBase in Azure Synapse Analytics. What should you recommend to ensure that the queries from Databricks and PolyBase against the files encounter the fewest possible errors, allow quick querying, and retain data type information

  • JSON

  • Parquet

  • CSV

  • Avro

Explanation

Correct Answer B. Parquet

Explanation

Parquet is a columnar storage format that is optimized for querying large datasets. It retains schema information, is highly compressed, and is efficient for querying in both Databricks and PolyBase in Azure Synapse Analytics. This format is particularly well-suited for big data processing and analytics scenarios, ensuring quick queries and minimal errors.

Why other options are wrong

A. JSON

While JSON is widely used for data interchange, it is not as optimized for performance in large-scale querying scenarios. Its flexibility can introduce issues with schema consistency, and querying JSON files can be slower compared to columnar formats like Parquet.

C. CSV

CSV files are simple to work with but do not retain data types or schema information, which can lead to errors and inefficiencies in processing and querying. CSV files are also less efficient for large datasets compared to columnar formats like Parquet.

D. Avro

Avro is a good format for streaming and is schema-based, but it does not offer the same level of performance optimization for querying as Parquet. Parquet is typically preferred for scenarios that involve heavy querying and analytics.


4.

 You create an Azure storage account that contains a table and blob container. You want to allow 2 IP addresses the ability to read from the table. The users of the IP addresses must not be able to modify or delete the account storage. They also must not be able to read blobs in the blob container. What should you provide

  • Service shared access signature (SAS)

  • Account shared access signature (SAS)

  • Primary access key

  • Secondary access key

Explanation

Correct Answer A. Service shared access signature (SAS)

Explanation

A Service SAS provides restricted access to a specific service (such as a table in your storage account) within the storage account, allowing you to specify granular permissions like read-only access to the table data. This solution is appropriate when you need to restrict access to only certain services (in this case, the table) and limit access to specific IP addresses. The Service SAS ensures that the users can only read from the table, and it prevents them from modifying or deleting data, as well as accessing blobs.

Why other options are wrong

B. Account shared access signature (SAS) – While an Account SAS provides access to all services (tables, blobs, queues, and files), this is too broad for the requirements. You need to limit access to just the table service, not the entire storage account.

C. Primary access key – The primary access key grants full access to all services in the storage account, including write and delete permissions. It’s not appropriate for limiting access to only specific actions or services.

D. Secondary access key – Like the primary access key, the secondary access key also grants full access to all services in the storage account. It is not for resuitable stricting actions on specific services.


5.

You need to write a query within Azure Data Explorer. Which query language should you use

  •  KQL

  • Gremlin

  • FetchXML

  • SQL

Explanation

Correct Answer A. KQL

Explanation

Azure Data Explorer (ADX) uses Kusto Query Language (KQL) to interact with and query data. KQL is designed for fast data exploration and retrieval, making it the correct query language for working with Azure Data Explorer.

Why other options are wrong

B. Gremlin – Gremlin is a graph traversal language used for querying graph databases. It is not used for Azure Data Explorer.

C. FetchXML – FetchXML is a query language used for querying Microsoft Dynamics 365 and other Microsoft services. It is not applicable to Azure Data Explorer.

D. SQL – SQL is a widely used query language for relational databases, but it is not the primary query language for Azure Data Explorer. ADX uses KQL.


6.

Which of the following languages is primarily used for writing stored procedures and functions in PostgreSQL

  •  PL/SQL

  • PL/pgSQL

  • T-SQL

  • SQL/PSM

Explanation

Correct Answer B. PL/pgSQL

Explanation

PL/pgSQL (Procedural Language/PostgreSQL) is the language used for writing stored procedures, functions, and triggers in PostgreSQL. It is a procedural extension of SQL designed to support control-flow logic like loops and conditionals.

Why other options are wrong

A. PL/SQL

PL/SQL is used in Oracle databases, not PostgreSQL. While both PL/SQL and PL/pgSQL serve similar purposes in their respective databases, PL/pgSQL is specific to PostgreSQL.

C. T-SQL

T-SQL (Transact-SQL) is used for stored procedures and functions in Microsoft SQL Server, not PostgreSQL.

D. SQL/PSM

SQL/PSM (SQL/Procedural Specification) is an ANSI standard for procedural SQL. While it provides a framework for procedural SQL, PL/pgSQL is the specific implementation for PostgreSQL.


7.

What is used to define a query in a stream processing job in Azure Stream Analytics

  • YAML

  • KQL

  • SQL

  • XML

Explanation

Correct Answer C. SQL

Explanation

Azure Stream Analytics uses SQL-based queries to define stream processing jobs. The query language follows a SQL-like syntax, which allows users to perform data transformations, filtering, and aggregations on incoming data streams in real-time.

Why other options are wrong

A. YAML

YAML is a data serialization language commonly used for configuration files, but it is not used to define queries in Azure Stream Analytics.

B. KQL

KQL (Kusto Query Language) is used for querying data in Azure Data Explorer (ADX) and is not the primary query language for Azure Stream Analytics.

D. XML

XML is a markup language used for data representation but is not used to define queries in Azure Stream Analytics.


8.

A group of IoT sensors is sending streaming data to a Cloud Pub/Sub topic. A Cloud Dataflow service pulls messages from the topic and reorders the messages sorted by event time. A message is expected from each sensor every minute. If a message is not received from a sensor, the stream processing application should use the average of the values in the last four minutes. What kind of window would you use to implement the missing data logic

  • Sliding window

  • Tumbling window

  • Extrapolation window

  • Crossover window

Explanation

Correct Answer A. Sliding window

Explanation

A sliding window allows the system to calculate the average over a dynamic set of events, continuously updating as new data arrives. In the scenario described, it will ensure that the last four minutes of data are always used to fill in missing data from the sensors, by looking at the most recent set of messages that fall within the window.

Why other options are wrong

B. Tumbling window

Tumbling windows are fixed, non-overlapping windows, which would not allow the continuous updating needed to calculate the average over the last four minutes. They would be more suited for cases where the data is processed in discrete, non-overlapping chunks.

C. Extrapolation window

Extrapolation windows are typically used to predict or extend data, not for calculating averages or filling in missing data based on the last known values. This approach would not be appropriate for the described use case.

D. Crossover window

Crossover windows are used in complex event processing scenarios, where multiple conditions need to be met to trigger actions across overlapping windows of events. This is not the right fit for the need to average past data in the absence of new incoming data.


9.

 What languages are used in the Databricks Notebook

  •  R and COBOL

  • Python and SQL

  • HTML and XML

  • JSON and Java

Explanation

Correct Answer B. Python and SQL

Explanation

Databricks Notebooks support languages such as Python, SQL, Scala, and R. Python and SQL are commonly used for data processing, analysis, and querying, making them key languages for Databricks notebooks.

Why other options are wrong

A. R and COBOL

While R is supported in Databricks, COBOL is not commonly used in Databricks notebooks. COBOL is an older language for business and transaction processing, not typically for modern data engineering tasks.

C. HTML and XML

HTML and XML are markup languages used for structuring content and data, not for data processing or querying in Databricks notebooks.

D. JSON and Java

JSON is a data format and Java is a programming language, but they are not the primary languages for writing Databricks notebooks. Java can be used in some contexts, but it is not the primary language used for data analysis in Databricks notebooks.


10.

Which of the following is required for an extract, load, and transform (ELT) process

  • A data pipeline that includes a transformation engine

  • A separate transformation engine

  • A target data store powerful enough to transform the data

  • Data that is fully processed before being loaded to the target data store

Explanation

Correct Answer A. A data pipeline that includes a transformation engine

Explanation

In the ELT process, data is first extracted and loaded into the target data store before transformation. The transformation typically occurs within the data store using the built-in capabilities of the platform. A data pipeline that includes a transformation engine is essential for executing transformations after data has been loaded.

Why other options are wrong

B. A separate transformation engine – While some ELT processes might use a separate transformation engine, it's not a requirement. Many modern data stores have built-in transformation capabilities that can handle transformations directly.

C. A target data store powerful enough to transform the data – While the target data store should be capable of handling transformations, it doesn't necessarily need to be powerful enough to perform them by itself. A transformation engine can be integrated as part of the data pipeline.

D. Data that is fully processed before being loaded to the target data store – This is the opposite of ELT. In ELT, data is loaded into the target system first, and transformation happens afterward. Therefore, data does not need to be fully processed before loading.


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