AZ-305 exam dumps: questions 101 to 200 of 243

Correct answer: A

• A. Correct.

  Azure Data Factory with a self-hosted Integration Runtime is the correct choice because it supports secure, large-scale data movement and transformation across hybrid environments. The self-hosted IR can connect to on-premises or private network sources, then orchestrate and schedule transformation pipelines to move the data into Azure Data Lake Storage.

• B. Incorrect.

  Azure Logic Apps is designed primarily for workflow automation and event-based triggers, rather than large-scale data orchestration. While it can connect to various data endpoints, it’s more suited for business process automation or simple real-time integrations rather than heavy ETL/ELT workloads.

• C. Incorrect.

  Azure Database Migration Service is focused on migrating entire databases to Azure rather than orchestrating ongoing data integration pipelines. It’s not intended for continuous transformations and scheduled integration tasks.

• D. Incorrect.

  Azure Event Grid is used to manage event-driven communication between services. It’s well-suited for broadcasting and reacting to events in real time, but it’s not designed for orchestrating large-scale, scheduled data imports or transformations.

Correct answers: A, C

• A. Correct.

  Correct. Azure Data Factory can connect to on-premises data sources securely by installing and configuring a self-hosted Integration Runtime. It allows you to schedule data ingestion, perform data transformations, and load the results into Azure services such as Data Lake Storage.

• B. Incorrect.

  Incorrect. Although Azure Logic Apps can automate workflows and integrate with various services, it is not designed primarily for large-scale data ingestion and transformation scenarios. Logic Apps can complement data pipelines but is not the most effective solution for orchestrating complex ETL or ELT processes from on-premises systems.

• C. Correct.

  Correct. Azure Synapse Pipelines (part of Azure Synapse Analytics) offer similar orchestration and data transformation capabilities to Azure Data Factory, but within an integrated analytics environment. It can ingest and process data from on-premises sources and then store or further analyze the data in Azure Data Lake or a Synapse SQL pool.

• D. Incorrect.

  Incorrect. Azure Event Hubs is best suited for streaming data ingestion such as event logs, IoT device telemetry, or real-time data feeds. For batch-style data movement and transformation from on-premises SQL databases or similar transactional systems, Event Hubs is not the optimal choice.

Correct answer: A

• A. Correct.

  Option 1 is correct. Azure Data Factory provides a comprehensive solution for orchestrating data pipelines, executing transformations with Mapping Data Flows, and scheduling workflows. It allows you to ingest data from multiple sources with minimal coding and apply transformations before storing the data in Azure Data Lake.

• B. Incorrect.

  Option 2 is incorrect. While you can store data in Azure Blob Storage and automate scripts, this approach is more manual and lacks the robust orchestration and transformation capabilities of a dedicated service like Azure Data Factory. It also requires more maintenance for scheduling and error handling.

• C. Incorrect.

  Option 3 is incorrect. Azure Analysis Services is primarily used for semantic modeling and creating analytical models rather than orchestrating data ingestion and transformation. It doesn’t natively handle ETL/ELT workloads the way Azure Data Factory does.

• D. Incorrect.

  Option 4 is incorrect. Azure Event Hubs is a highly scalable event ingestor designed for streaming scenarios. It doesn’t provide built-in data transformation or orchestration features and is generally used for large-scale event collection rather than structured data transformation.

Correct answer: A

• A. Correct.

  Correct. Azure Databricks offers a fully managed Spark environment with auto-scaling clusters, which simplifies both batch and real-time analytics. Its integrated environment and near real-time streaming support are well-suited for this scenario.

• B. Incorrect.

  Incorrect. While Azure HDInsight can run Spark, it requires more hands-on cluster management. It doesn’t offer the same level of simplified, fully managed service that Azure Databricks provides.

• C. Incorrect.

  Incorrect. Azure SQL Database can handle structured data, but it’s not ideal for large-scale unstructured or semi-structured analytics scenarios, and it lacks native Spark support for distributed processing.

• D. Incorrect.

  Incorrect. An on-premises Hadoop cluster would add significant overhead for capacity management and maintenance, and it doesn’t leverage the benefits of a fully managed Azure service for near real-time analytics.

Correct answer: B

• A. Incorrect.

  Option 1: Azure Databricks is a strong platform for big data processing, data science, and machine learning using Apache Spark. However, it does not natively include all the integrated features of data warehousing, SQL queries, and pipeline orchestration within a single service. You would typically still need an additional service or integration for a fully managed data warehouse.

• B. Correct.

  Option 2: Azure Synapse Analytics is designed to provide an end-to-end analytics platform. It includes data ingestion, data transformation using Apache Spark or Data Flows, a fully managed SQL-based data warehouse, serverless SQL pools, and orchestration with Synapse Pipelines. This makes it suitable for an integrated analytics solution, handling both structured and unstructured data in a single environment.

• C. Incorrect.

  Option 3: Azure HDInsight is a managed open-source analytics service offering clusters for Hadoop, Spark, and other frameworks. While it can process large datasets, it does not include a fully integrated SQL data warehousing component or the unified environment that Synapse offers. It often requires additional services for data orchestration and warehousing.

• D. Incorrect.

  Option 4: Azure Data Lake Storage provides a highly scalable and cost-effective data lake but does not itself offer computational or orchestration capabilities. You would need to combine Data Lake Storage with other analytics services to perform transformations, analysis, or warehouse operations.