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Question 21:
A manufacturing company wants to use Azure Synapse Analytics to process IoT sensor data stored in Azure Data Lake Storage. They want to use Spark notebooks to run machine learning experiments directly on the data. Which Synapse component enables this capability?
Answer:
A) Synapse SQL Serverless
B) Synapse Pipelines
C) Synapse Spark Pool
D) Synapse SQL Dedicated Pool
Answer: C
Explanation:
Synapse Spark Pool is the component inside Azure Synapse Analytics designed for distributed big data processing, data engineering, and machine learning workloads using Apache Spark. The scenario describes a manufacturing company that wants to process IoT sensor data stored in Azure Data Lake Storage and run machine learning experiments directly on the data. These tasks align perfectly with the capabilities of a Spark environment. Spark supports distributed data processing, machine learning libraries, structured streaming, and data transformations. Spark pools in Synapse allow teams to use notebooks that execute Scala, Python, SQL, and R, making it highly versatile for advanced analytics.
Synapse SQL Serverless, option A, provides on-demand SQL querying of data stored in a data lake but is not suitable for machine learning or Spark-based transformations. Serverless SQL is optimized for exploratory SQL queries rather than distributed ML workloads.
Synapse Pipelines, option B, handle orchestration and data integration tasks, such as ETL and ELT operations. They do not support distributed machine learning computations.
Synapse SQL Dedicated Pool, option D, is optimized for MPP-based analytical queries using SQL. It is ideal for structured data warehousing but not suitable for distributed machine learning tasks.
Spark Pools provide native integration with Azure Data Lake Storage, meaning that notebooks can read and write data directly from the data lake without copying or restructuring it. Spark’s MLlib library supports classification, clustering, regression, and recommendation algorithms, making it a strong fit for IoT sensor analysis.
Because the requirement emphasizes machine learning experiments on distributed data, Synapse Spark Pool is the correct answer, making option C the best choice.
Question 22:
A data analyst wants to visualize business metrics using dashboards and interactive reports. They need a service that allows the creation of charts, KPIs, and sharing insights with stakeholders. Which Azure-related tool is designed for this purpose?
Answer:
A) Power BI
B) Azure Active Directory
C) Azure Monitor
D) Azure Event Hubs
Answer: A
Explanation:
Power BI is the appropriate tool for creating business dashboards, visualizations, and interactive reports. The scenario describes a data analyst who wants to present business metrics using charts and KPIs and share insights with other stakeholders. Power BI is specifically created for business intelligence and visualization. It provides a wide range of charts, visuals, and report-building tools. It also integrates easily with Azure databases, Azure Synapse, Azure Data Lake, and other analytical services.
Azure Active Directory, option B, manages identity and access but has no visualization capabilities.
Azure Monitor, option C, focuses on performance metrics, logs, and operational telemetry. While it includes dashboards for system monitoring, it does not support custom business intelligence dashboards or KPIs.
Azure Event Hubs, option D, is a real-time data ingestion service for streaming workloads, not a visualization tool.
Power BI is a core component of Microsoft’s business intelligence ecosystem and is widely used for corporate reporting, trend analysis, executive dashboards, and KPI monitoring. It supports real-time data streaming, custom calculations using DAX, and easy publication of reports to the Power BI service.
Thus, option A is correct.
Question 23:
Your organization wants to ensure that only authorized applications can access an Azure SQL Database. They want to enforce identity-based authentication rather than username and password credentials. Which feature allows this?
Answer:
A) Transparent Data Encryption
B) Azure SQL Auditing
C) Azure AD Authentication
D) SQL Log Shipping
Answer: C
Explanation:
Azure Active Directory Authentication is the feature that enables identity-based authentication for Azure SQL Database. Instead of relying on traditional username and password combinations, Azure AD allows users, groups, and managed identities to authenticate securely. This approach improves security by providing centralized control over identities, eliminating password management for applications, and supporting conditional access policies.
Transparent Data Encryption, option A, encrypts data at rest but does not affect authentication.
Azure SQL Auditing, option B, logs access and activity but does not provide identity-based authentication mechanisms.
SQL Log Shipping, option D, is a disaster recovery feature used in SQL Server environments and is unrelated to authentication.
Azure AD Authentication supports modern security practices, including token-based authentication, MFA, and role-based access control. It enables applications to use managed identities to authenticate without storing secrets or credentials. This reduces risk and simplifies access management.
Thus, option C is correct.
Question 24:
A data engineering team wants to reduce storage costs by automatically moving cold data from an Azure SQL Database to a cheaper storage tier such as Azure Blob Storage. Which Azure service facilitates this automated movement?
Answer:
A) Azure Event Grid
B) Azure Data Factory
C) Azure Cosmos DB
D) Azure Backup
Answer: B
Explanation:
Azure Data Factory is designed to orchestrate and automate data movement, including migration of cold or infrequently accessed data to cheaper storage tiers. The scenario describes a team wanting to offload older data from Azure SQL Database to Azure Blob Storage to reduce costs. Data Factory supports scheduled, event-driven, and batch-driven pipelines that can extract data from SQL databases, transform it if necessary, and load it into cost-effective storage like Blob Storage or Data Lake Storage.
Azure Event Grid, option A, handles event routing but does not manage ETL or data movement processes.
Azure Cosmos DB, option C, is a NoSQL operational database and not a pipeline-orchestration service.
Azure Backup, option D, creates backups for disaster recovery and does not orchestrate data migrations for cost management.
Data Factory supports connectors for nearly all Azure storage and compute services, as well as scheduling, parameterization, and monitoring. It is widely used for data warehouse loads, archiving, and lifecycle management. Therefore, option B is correct.
Question 25:
A company wants to enforce row-level security so different users querying the same table only see data relevant to their department. Which Azure SQL feature provides this capability?
Answer:
A) Query Store
B) Dynamic Data Masking
C) Row-Level Security
D) Always Encrypted
Answer: C
Explanation:
Row-Level Security allows organizations to enforce restrictions on which rows of data a user can query based on their identity. The scenario describes users from different departments querying the same table but needing to see only the data relevant to them. Row-Level Security achieves this by applying predicates or filters automatically during query execution. Users cannot bypass these restrictions, ensuring consistent enforcement of data governance policies.
Query Store, option A, tracks query performance but does not apply security rules.
Dynamic Data Masking, option B, hides specific fields but does not filter rows.
Always Encrypted, option D, protects sensitive data during computation but does not restrict which rows a user can access.
Row-Level Security attaches security predicates to tables, ensuring that all queries are filtered before results are returned. This helps maintain compliance, privacy, and departmental boundaries.
Thus, option C is correct.
Question 26:
A global e-commerce company wants to analyze customer purchase patterns over the last ten years using a highly scalable distributed query engine. They need the ability to run SQL queries directly on data stored in Azure Data Lake without provisioning dedicated compute resources. Which Azure service best meets this requirement?
Answer:
A) Azure SQL Database
B) Azure Synapse SQL Serverless
C) Azure Database for PostgreSQL
D) Azure Cosmos DB
Answer: B
Explanation:
Azure Synapse SQL Serverless is designed for on-demand querying of data stored directly in Azure Data Lake Storage without requiring dedicated compute provisioning. The scenario describes a global e-commerce company that wants to analyze customer purchasing patterns over a span of ten years. Large historical datasets generally reside in data lakes due to their cost efficiency and ability to store structured, semi-structured, and unstructured data. To analyze such data efficiently, organizations often benefit from a query engine that can interact with the lake without moving the data or provisioning expensive clusters. Synapse SQL Serverless supports exactly this usage pattern.
Synapse SQL Serverless allows analysts to run T-SQL queries on files such as CSV, Parquet, or JSON directly within the lake. Its architecture eliminates the overhead of managing clusters, compute nodes, or scaling decisions. Instead, the compute resources are provisioned automatically when a query is executed, and the user is charged based only on the amount of data processed. This model is extremely cost-efficient for intermittent analytics, historical analysis, or exploratory data science. It is also beneficial for organizations with large datasets but variable query workloads.
Option A, Azure SQL Database, is designed for transactional workloads and structured relational data. It cannot query raw data files stored in a data lake without ETL operations and is not optimized for large-scale distributed analytics.
Option C, Azure Database for PostgreSQL, is similarly a relational database optimized for structured OLTP scenarios. It lacks the capability to analyze data directly within a lake and does not support serverless distributed querying.
Option D, Azure Cosmos DB, is a globally distributed NoSQL database designed for operational workloads requiring low-latency reads and writes. It is not intended for large-scale historical analytics or on-demand SQL processing of external files.
The e-commerce company’s requirement for scalable, distributed SQL queries over long-term historical data, combined with their desire to avoid infrastructure management, aligns perfectly with Synapse SQL Serverless. This service provides high performance, serverless compute, flexible querying, and seamless integration with data lakes.
Therefore, option B is correct.
Question 27:
A financial analytics team needs to run predictive models, clean datasets, and perform large-scale transformations using Python and Spark. They also require interactive notebooks and integration with Azure Data Lake. Which Azure service should they choose?
Answer:
A) Azure Database for MySQL
B) Azure Databricks
C) Azure SQL Managed Instance
D) Azure Event Grid
Answer: B
Explanation:
Azure Databricks is the premier service for large-scale data engineering, advanced analytics, and machine learning using Apache Spark. The scenario describes a financial analytics team that needs to run predictive models, clean datasets, perform large-scale data transformations, and utilize Python along with Spark. Databricks provides a collaborative environment with interactive notebooks, Spark clusters, and integrated libraries for machine learning, such as MLlib, TensorFlow, and Scikit-learn. Its deep integration with Azure Data Lake Storage allows seamless access to raw and curated datasets, making it ideal for financial analytics and modeling.
Option A, Azure Database for MySQL, is a relational database for transactional workloads. It cannot support distributed compute, Spark processing, or machine learning pipelines.
Option C, Azure SQL Managed Instance, provides a fully managed SQL Server-compatible environment but does not support distributed processing, Spark, or notebook development.
Option D, Azure Event Grid, is an event routing service used for real-time notifications. It does not provide compute capabilities, notebook environments, or advanced analytics tools.
Azure Databricks accelerates the entire machine learning lifecycle, including data ingestion, feature engineering, model training, and operationalization. Its interactive notebooks support multiple languages, visualizations, and predictive analytics workflows. Databricks clusters are optimized for large-scale parallel processing, which is essential when running predictive financial models on large data volumes.
Thus, the correct answer is option B.
Question 28:
A company needs to retain database backups for compliance purposes. They want automatic long-term retention of full backups for up to 10 years with minimal administrative effort. Which Azure feature should they use?
Answer:
A) Azure SQL Long-Term Backup Retention
B) Azure Blob Lifecycle Management
C) Azure Site Recovery
D) Azure Data Factory Pipelines
Answer: A
Explanation:
Azure SQL Long-Term Backup Retention enables organizations to store full backups for extended periods ranging from months to several years. Compliance-driven industries such as healthcare, finance, and government often require long-term storage of backups to meet regulatory demands. This feature automates the retention of backups and ensures that the storage and management of these backups require minimal administrative oversight. Backups can be retained for up to 10 years or more depending on regulatory requirements.
Option B, Azure Blob Lifecycle Management, automates transitioning blob data between tiers but does not manage SQL backup retention policies.
Option C, Azure Site Recovery, provides disaster recovery capabilities but is not designed for long-term backup archival.
Option D, Azure Data Factory Pipelines, handles data movement and ETL processes, not database backup retention.
Long-Term Backup Retention integrates directly with Azure SQL Database and Azure SQL Managed Instance, allowing administrators to create retention policies and manage historical backups from the Azure portal. It eliminates manual backup handling and ensures compliance through automated retention schedules.
Thus, option A is the correct choice.
Question 29:
Your organization wants to detect anomalies in streaming data coming from sensors in a manufacturing plant. They require real-time insights and the ability to trigger alerts within seconds of detecting an abnormal pattern. Which Azure service is best suited for real-time anomaly detection?
Answer:
A) Azure Stream Analytics
B) Azure Data Lake Storage Gen2
C) Azure Cosmos DB
D) Azure SQL Database
Answer: A
Explanation:
Azure Stream Analytics is specifically designed for analyzing and processing real-time streaming data. The scenario describes a manufacturing environment where sensor data must be analyzed instantly to detect anomalies and trigger alerts. Stream Analytics supports real-time pattern matching, anomaly detection, temporal windows, and integration with alerting systems such as Azure Monitor, Event Grid, and Power BI.
Stream Analytics can ingest data from IoT Hub, Event Hubs, and other real-time sources. It applies SQL-like query logic to streaming data, enabling filters, aggregations, temporal insights, and real-time joins. Its low latency and high throughput make it well suited for scenarios requiring sub-second reaction times.
Azure Data Lake Storage Gen2, option B, is designed for long-term storage and batch analytics rather than real-time processing.
Azure Cosmos DB, option C, can act as a low-latency operational data store but is not a real-time stream processing engine.
Azure SQL Database, option D, is optimized for OLTP workloads and cannot process streaming data with the speed required for anomaly detection.
Therefore, Azure Stream Analytics is the correct service for real-time anomaly detection, making option A correct.
Question 30:
A company is designing a data warehouse that supports slowly changing dimensions, large historical datasets, and optimized read performance for analytical queries. What type of schema is most appropriate for their data warehouse design?
Answer:
A) Star schema
B) Key-value schema
C) Graph schema
D) Flat file schema
Answer: A
Explanation:
A star schema is the most widely used data modeling technique for data warehouses designed for analytical queries. In a star schema, fact tables store measurable business events, while dimension tables store descriptive attributes that support analysis. This structure optimizes query performance by reducing the number of necessary joins and organizing data logically around business processes.
Slowly changing dimensions, large historical datasets, and optimized read performance are hallmarks of data warehouse design. Star schemas support slowly changing dimensions through dimension modeling techniques such as Type 1, Type 2, or Type 3 changes. Analytical queries benefit from star schemas because they naturally support aggregations, drill-down analysis, and slicing by dimensions such as time, geography, product, or customer.
Key-value schemas, option B, are suitable for simple lookups but not for analytical queries or dimensional modeling.
Graph schemas, option C, support relationship-heavy queries but are not optimized for analytical workloads or dimensional reporting.
Flat file schemas, option D, have no relational structure and do not support efficient analytical querying, indexing, or dimension-driven analysis.
Star schemas align with BI tools, OLAP workloads, and data warehouse best practices. They simplify reporting, enhance query performance, and allow large-scale aggregation functions.
Thus, option A is correct.
Question 31:
Your organization collects semi-structured data from multiple applications, including logs, JSON events, and metadata records. They want to build a big data analytics solution that supports distributed processing using Spark and allows efficient reading and writing of Parquet files. Which Azure storage solution best supports this requirement?
Answer:
A) Azure SQL Database
B) Azure Data Lake Storage Gen2
C) Azure Cosmos DB
D) Azure Table Storage
Answer: B
Explanation:
Azure Data Lake Storage Gen2 is the ideal storage system for large-scale analytics involving semi-structured data such as logs, JSON files, and metadata. The scenario describes the need to run Spark-based analytics and efficiently read and write Parquet files. ADLS Gen2 is specifically optimized for distributed data processing, making it the natural choice for big data environments. Parquet files require columnar storage, compression, and optimized read/write patterns, particularly when working with Spark, Databricks, Azure Synapse Analytics, or HDInsight.
Option A, Azure SQL Database, is a relational database designed for OLTP workloads and structured data. It cannot efficiently store logs or large-scale semi-structured file-based datasets. It also does not support distributed file operations or Spark-based processing in a native manner.
Option C, Azure Cosmos DB, is designed for operational NoSQL workloads that require globally distributed low-latency access. While Cosmos DB can store JSON documents, it is not optimized for large-scale analytical workloads using Spark. Storing logs or Parquet files in Cosmos DB is impractical and expensive.
Option D, Azure Table Storage, is a key-value store designed for simple, scalable, low-cost storage of structured but non-relational data. It cannot store complex big data workloads and does not integrate natively with Spark in the same analytics-optimized manner as ADLS Gen2.
ADLS Gen2 provides hierarchical namespaces, POSIX-style access control lists, and full compatibility with analytic engines. It supports massive throughput, distributed parallel reads, and efficient compute/storage separation. The ability to store Parquet files, which Spark can process very efficiently due to columnar compression, makes ADLS Gen2 superior for this workload.
Thus, option B is correct.
Question 32:
A data team wants to migrate their on-premises SQL Server environment to Azure while maintaining full SQL Server compatibility, support for cross-database queries, SQL Agent jobs, and advanced security features. Which Azure service best meets this requirement?
Answer:
A) Azure SQL Managed Instance
B) Azure SQL Database
C) Azure Database for MySQL
D) Azure Database for PostgreSQL
Answer: A
Explanation:
Azure SQL Managed Instance is designed to provide near-100% compatibility with on-premises SQL Server environments. It supports features not available in Azure SQL Database, such as SQL Server Agent, cross-database queries, database mail, service broker, CLR integration, and compatibility with native SQL Server backup/restore operations. The scenario describes an organization that wants to maintain SQL Server compatibility while moving to a managed Azure solution. Managed Instance is the best fit because it is intended as a modernization path for companies migrating from on-prem SQL Server with minimal code or architecture changes.
Option B, Azure SQL Database, is a fully managed PaaS relational service. However, it does not support all on-prem SQL Server features and lacks multi-database support, SQL Agent jobs, and some advanced SQL Server functionality.
Option C, Azure Database for MySQL, is unrelated to SQL Server workloads and cannot support SQL Server features or migration processes.
Option D, Azure Database for PostgreSQL, similarly cannot support SQL Server compatibility since it runs a different database engine entirely.
SQL Managed Instance provides a robust, scalable environment that functions almost identically to a SQL Server instance, making migrations simpler, reducing refactoring requirements, and preserving enterprise-grade workloads. This makes option A the correct choice.
Question 33:
Your analytics team wants to run ad-hoc SQL queries over large CSV and Parquet datasets stored in Azure Data Lake without creating tables or loading data into a database. They need instant querying capabilities with pay-per-query billing. Which Azure service is designed for this?
Answer:
A) Azure SQL Database
B) Azure Cosmos DB
C) Azure Synapse SQL Serverless
D) Azure Data Factory
Answer: C
Explanation:
Azure Synapse SQL Serverless is built specifically for ad-hoc, on-demand SQL queries over data stored in a data lake. It allows querying CSV, Parquet, and JSON files without requiring ETL processes, table creation, or data ingestion into a database. This makes it ideal for rapid data exploration, reporting, and validation tasks. The pay-per-query billing model ensures cost efficiency, as the organization only pays for the amount of data scanned.
Azure SQL Database, option A, requires structured tables and data ingestion. It is not optimized for querying raw files stored in a data lake.
Azure Cosmos DB, option B, is not a data lake query engine and does not process external files with SQL queries.
Azure Data Factory, option D, handles data pipelines and orchestration but does not provide on-demand SQL querying functionality.
Synapse SQL Serverless integrates seamlessly with ADLS, automatically infers schema during query execution, supports parallel reads, and allows analysts to run SQL-based analytics without infrastructure management. This perfectly matches the scenario.
Thus, option C is correct.
Question 34:
A business wants to use Azure SQL Database for an e-commerce application. They need to ensure minimal downtime and automatic failover in case the primary region becomes unavailable. Which deployment option provides built-in high availability using multiple regions?
Answer:
A) Single database
B) Active geo-replication
C) Elastic pool
D) Serverless compute tier
Answer: B
Explanation:
Active geo-replication enables Azure SQL Database to replicate data asynchronously to secondary databases located in different Azure regions. These readable replicas can be used for load balancing or business continuity. In case the primary region becomes unavailable, the secondary database can be promoted to the primary role, ensuring minimal downtime and continuity for the e-commerce application.
Option A, single database, provides high availability within a region but does not replicate data to other regions.
Option C, elastic pool, is a cost-effective resource-sharing model for multiple databases but does not inherently provide multi-region redundancy.
Option D, the serverless compute tier, provides automatic scaling and pause/resume functionality but does not offer multi-region failover.
Active geo-replication supports up to four readable secondaries, manual failover, and robust business continuity planning, making it ideal for critical systems like e-commerce platforms. The multi-region resiliency ensures availability even during regional outages.
Thus, option B is correct.
Question 35:
A data analytics department needs to create a semantic layer that includes measures, hierarchies, calculated columns, and relationships between tables. They want to use this layer for dashboard-building in Power BI. Which component should they create?
Answer:
A) Power BI Dataset
B) Azure SQL Managed Instance
C) Azure Event Hub
D) Azure Monitor Workspace
Answer: A
Explanation:
Power BI Datasets act as the semantic model for Power BI. They allow analysts to define calculated measures, hierarchies, relationships, KPIs, and calculated columns. This semantic layer provides a unified analytical model that business users can consume across dashboards, reports, and data visualizations. Power BI datasets can import data or connect live to external sources, and they support DAX for advanced calculations.
Azure SQL Managed Instance, option B, stores relational data but does not create semantic analytical models for reporting.
Azure Event Hub, option C, is designed for real-time event ingestion and not for modeling semantic layers.
Azure Monitor Workspace, option D, stores telemetry and monitoring data but is not used for building analytical models.
Datasets serve as the core of Power BI’s modeling capabilities, supporting large analytical models, incremental refresh, and the ability to define business logic separate from raw tables. Therefore, option A is correct.
Question 36:
Your company is building a real-time analytics dashboard that must display incoming data from IoT sensors within seconds. They need a fully managed solution capable of ingesting millions of events per second while allowing downstream systems to process this data for analytics and storage. Which Azure service is best suited for ingesting this real-time telemetry?
Answer:
A) Azure Data Factory
B) Azure Event Hubs
C) Azure Data Lake Storage Gen2
D) Azure Synapse Dedicated SQL Pool
Answer: B
Explanation:
Azure Event Hubs is the appropriate Azure service for ingesting large volumes of real-time telemetry data. Event Hubs is designed specifically for highly scalable event streaming and supports ingestion of millions of events per second. In the scenario described, IoT sensors generate continuous streams of telemetry that need to be processed within seconds to power a real-time analytics dashboard. Event Hubs provides the ingestion backbone for this type of pipeline, enabling downstream processing engines such as Azure Stream Analytics, Azure Functions, Databricks, and Synapse Spark to consume and analyze the event stream.
Azure Data Factory, listed as option A, is used for ETL and ELT workflows, typically in batch mode. It does not support real-time streaming ingestion. While Data Factory pipelines can handle large datasets, they cannot ingest millions of events per second in real time.
Azure Data Lake Storage Gen2, option C, is ideal for storing large volumes of analytical data but is not suited for real-time ingestion. It is typically used for batch or near-batch analytics, long-term storage, machine learning datasets, and archival. It lacks the event ingestion capabilities necessary for immediate dashboard updates.
Azure Synapse Dedicated SQL Pool, option D, is a massively parallel processing data warehouse solution that excels at structured analytics but cannot ingest millions of events per second directly. Loading data into a dedicated SQL pool requires staging or batch processes, which prevents real-time analytics.
Event Hubs supports partitioning for parallel ingestion, checkpointing, replay capability, and durable storage of events. It also integrates seamlessly with Azure IoT Hub, which itself uses Event Hubs technology to manage device-to-cloud communication. Event Hubs supports consumer groups that allow different services to read the same event stream independently. This is important in real-time data pipelines where one consumer may process alerts, another may store raw telemetry, and another may feed a dashboard.
Event Hubs’ high throughput, low latency, and compatibility with streaming analytics services make it the industry-standard solution for real-time data ingestion scenarios. It supports AMQP, Kafka protocols, and automatic scaling, making it versatile for a wide range of streaming architectures.
Because the problem requires real-time ingestion, massive scalability, and downstream analytical processing, Azure Event Hubs is the clear and correct choice. Therefore, option B is correct.
Question 37:
A large enterprise wants to ensure that sensitive information such as credit card numbers is always encrypted during query processing inside an Azure SQL Database. They need a solution that guarantees data remains encrypted not only at rest and in transit, but also during computation. Which feature fulfills this requirement?
Answer:
A) Transparent Data Encryption
B) Always Encrypted
C) Dynamic Data Masking
D) SSL/TLS Encryption
Answer: B
Explanation:
Always Encrypted is the Azure SQL feature that ensures sensitive data remains encrypted not only at rest and in transit, but also during computation. The scenario describes a large enterprise handling sensitive information such as credit card numbers. Ensuring that data stays encrypted while being processed is essential for meeting strict security standards such as PCI-DSS. Always Encrypted uses client-side encryption keys, meaning the database engine cannot decrypt the data. This prevents unauthorized access not only from malicious actors but also from administrators who might have direct access to the server.
Transparent Data Encryption, option A, encrypts data at rest but decrypts it for processing. This means the database engine still has access to plaintext data during operations, which does not meet the requirement.
Dynamic Data Masking, option C, only masks sensitive data for users without proper permissions. It does not encrypt data or provide security during computation.
SSL/TLS Encryption, option D, encrypts data in transit between the client and server but does not encrypt the data during processing.
Always Encrypted ensures that even during queries, the database engine cannot see the decrypted data. Only the client driver that holds the encryption keys can decrypt it. This protects sensitive fields and enforces strict separation of duties within an organization.
Thus, option B is the correct answer.
Question 38:
Your analytics department uses Power BI but needs to handle extremely large datasets beyond the import model limits. They want to query data directly in the data source while still benefiting from Power BI’s modeling capabilities. Which Power BI feature best supports this?
Answer:
A) Power BI Import Mode
B) Power BI DirectQuery
C) Power BI Desktop
D) Power Query Editor
Answer: B
Explanation:
Power BI DirectQuery is the appropriate feature when handling extremely large datasets that cannot be imported into Power BI. DirectQuery allows the report to query the underlying data source directly, enabling near real-time data access without the need to copy data into Power BI storage. This mode is designed for large-scale enterprise analytics where data volumes exceed the import limits or where the organization requires near real-time reporting.
Power BI Import Mode, option A, loads data into the Power BI dataset at refresh time. While it provides fast performance for reporting, it is limited by dataset size constraints and memory requirements. It is not suitable for massive datasets.
Power BI Desktop, option C, is the development tool for building reports but does not determine how data is stored or queried.
Power Query Editor, option D, is used for transforming and shaping data but not for querying live datasets at scale.
DirectQuery maintains a semantic model within Power BI, supports relationships, measures, and hierarchies, and provides modeling flexibility while delegating storage and processing to the source system. It is essential for scenarios with large or real-time data sources such as Azure Synapse, SQL Database, or Oracle systems.
Thus, option B is correct.
Question 39:
An organization needs to migrate hundreds of on-premises databases to Azure SQL Database with minimal downtime. They require continuous data replication before cutover. Which Azure tool best supports this scenario?
Answer:
A) Azure Data Factory
B) Azure Migrate
C) Azure Database Migration Service
D) Azure Synapse Pipelines
Answer: C
Explanation:
Azure Database Migration Service (DMS) is the most appropriate tool for migrating databases from on-premises systems to Azure SQL Database with minimal downtime. DMS supports continuous replication from the source system to the target Azure SQL instance, enabling organizations to perform a seamless cutover once synchronization is complete. This is essential when migrating hundreds of databases, especially those that support critical business applications.
Azure Data Factory, option A, is capable of moving data but is designed for ETL/ELT workflows rather than full database migrations with minimal downtime.
Azure Migrate, option B, is used for server and VM migrations but does not specialize in database migration with transactional consistency.
Azure Synapse Pipelines, option D, function similarly to Data Factory pipelines and are unsuited for transactional replication or minimal-downtime database migration.
DMS supports multiple source systems including SQL Server, Oracle, MySQL, and PostgreSQL. It handles schema conversion, transactional consistency, and real-time replication. This reduces migration risk and downtime, making it essential for large-scale enterprise transitions.
Thus, option C is the correct answer.
Question 40:
A company needs a scalable solution to identify trends in large historical datasets stored in Azure Data Lake. They want to use SQL-based queries and benefit from MPP (Massively Parallel Processing). Which component of Azure Synapse Analytics is designed for this?
Answer:
A) Synapse Serverless SQL
B) Synapse Pipelines
C) Synapse Dedicated SQL Pool
D) Synapse Spark Pool
Answer: C
Explanation:
Synapse Dedicated SQL Pool is designed for large-scale analytical workloads using MPP. The scenario describes analyzing large historical datasets in Azure Data Lake using SQL-based queries. Dedicated SQL Pool distributes data across multiple compute nodes and processes queries in parallel, significantly reducing execution time for large and complex analytical operations. It is ideal for data warehouses, star-schema models, and large batch analytical workloads.
Synapse Serverless SQL, option A, is excellent for ad-hoc exploration but does not provide the consistent high-performance MPP architecture required for enterprise-scale analytics.
Synapse Pipelines, option B, orchestrate data movement but cannot perform distributed SQL analytics.
Synapse Spark Pool, option D, is designed for big data analytics and machine learning, but not for warehouse-style SQL workloads.
Dedicated SQL Pool is the central MPP engine in Synapse Analytics that provides exceptional performance for large-scale structured analytics.
Thus, option C is correct.
