Design and implement data storage

A company is designing a data lake solution on Azure Data Lake Storage Gen2. Data will be ingested from IoT devices at high frequency (every 5 seconds). Each device sends a JSON payload of 2 KB. The data must be stored in a hierarchical namespace and partitioned by date and device ID to optimize query performance. Which partition strategy should be used?

You are designing a near-real-time analytics pipeline for a retail company. Transaction data is generated in Azure SQL Database and must be replicated to Azure Synapse Analytics (dedicated SQL pool) with less than 5 minutes latency. The source table has 50 million rows and 200 columns, but only 30 columns are needed for analytics. Which approach should you recommend?

A data engineer needs to store semi-structured JSON log files from a web application. Each log entry is about 1 KB. The logs are rarely queried (once a month) and must be retained for 7 years for compliance. The solution must minimize storage cost. Which storage option should be used?

Which TWO of the following are supported storage options for use as a source in Azure Synapse Pipeline Copy Activity?

You are an administrator for an Azure Synapse Analytics dedicated SQL pool. You execute the T-SQL statements shown in the exhibit. The external table 'dbo.Orders' is created. Which statement about querying this external table is true?

A company is designing a data storage solution for IoT device telemetry. Each device sends a JSON payload every second. The data must be stored in a way that supports real-time dashboards and long-term analytics with low latency. Which Azure data store should be used for the ingestion layer?

A data engineer needs to store JSON documents that are frequently updated by multiple users concurrently. The solution must support optimistic concurrency control and have built-in indexing on all fields. Which Azure data store should be used?

A company is designing a data storage solution for a global application that requires low-latency reads and writes for user session data. The solution must support automatic failover across multiple Azure regions. Which TWO Azure services meet these requirements?

A company uses Azure Synapse Analytics dedicated SQL pool to store sales data. The sales table is partitioned by month and has a clustered columnstore index. Over time, the performance of queries filtering on a specific month has degraded. The data engineer suspects high rowgroup elimination. Which action should be taken to improve performance?

A data engineer needs to store CSV files containing customer data in Azure Blob Storage. The files must be encrypted at rest using a customer-managed key stored in Azure Key Vault. What should they configure?

Which TWO options are recommended bulk loading methods for Azure Synapse SQL Pool? (Choose two.)

A company is designing a data storage solution for streaming IoT telemetry data. The data is JSON-formatted, arrives at up to 10,000 events per second, and must be stored for at least 30 days for real-time dashboards and ad-hoc querying. The solution must minimize operational overhead and query latency. Which Azure service should they use?

A company is designing a data storage solution for IoT device telemetry data. The data is append-only, needs to be stored cost-effectively for long-term analytics, and must support querying by device ID and timestamp. Which Azure storage solution should they use?

A data engineering team is designing a batch processing pipeline that reads from Azure Data Lake Storage Gen2, transforms data using Azure Databricks, and writes to Azure Synapse Analytics. The pipeline must process data incrementally and handle late-arriving data up to 2 hours. Which approach should they use to track processed files?

Which Azure service provides fully managed, serverless relational database capabilities for transactional workloads in a data storage solution?

You are designing a data storage solution for a global IoT application that ingests millions of events per second. The data is write-heavy with occasional reads for real-time dashboards. Which Azure storage option and configuration would provide the lowest latency writes with high throughput?

Which of the following are valid methods to secure data at rest in Azure Data Lake Storage Gen2? (Choose two.)

You are designing a data lake architecture using Azure Data Lake Storage Gen2. You need to optimize query performance for Azure Synapse Analytics serverless SQL. Which three design considerations should you follow? (Choose three.)

You are designing a data storage solution for IoT sensor data. The data is written thousands of times per second and requires low-latency reads for real-time dashboards. Which Azure storage solution should you use?

Your company stores sensitive customer data in Azure SQL Database. You need to encrypt the data at rest and ensure that only your application can decrypt it, even from database administrators. What should you implement?

Which Azure storage solution is best suited for storing large volumes of unstructured data, such as log files and media files, and supports both hierarchical namespace and POSIX-like access control lists?

You are designing a data storage solution for a retail company that needs to store transaction data that is frequently updated and requires strong consistency. The solution must support complex queries and joins across multiple tables. Which Azure data service should you recommend?

Match each Azure service to its primary purpose in a data engineering pipeline.

Match each Azure security feature to its description.

A company wants to ingest streaming data from IoT devices into Azure for real-time analytics. The data must be available for immediate querying and also stored long-term in a cost-effective format. Which Azure service should be used as the primary ingestion endpoint?

A data engineer is designing a solution to store historical sales data for a retail company. The data is append-only and accessed infrequently for compliance reports. The solution must minimize storage costs while allowing retrieval within 24 hours. Which storage tier should be used for the data?

A company uses Azure Synapse Analytics dedicated SQL pool for data warehousing. They notice that queries against a large fact table are slow. The table is hash-distributed on ProductID, but many queries filter on OrderDate. What should the data engineer do to improve query performance?

A data engineer needs to store semi-structured JSON logs for analysis using Azure Synapse Serverless SQL. Which file format should be used for optimal query performance?

A company is migrating its on-premises SQL Server data warehouse to Azure Synapse Analytics. They have a fact table with 2 billion rows and 30 columns. The table is frequently joined on CustomerID and filtered on OrderDate. What is the recommended table design?

A data engineer is setting up Azure Data Lake Storage Gen2 for a new project. The security requirement is to prevent direct access to the storage account from the internet while allowing access from a specific virtual network. Which network security feature should be enabled?

Which THREE are best practices for optimizing query performance in Azure Synapse Analytics dedicated SQL pool?

You are designing a data storage solution for a retail company. The data includes transactional data that requires low-latency queries (under 10 milliseconds) and large historical data for analytics. The solution must minimize storage costs. Which approach should you recommend?

You need to store semi-structured JSON data from a web application. The data schema may change over time. The solution must support low-latency queries and be globally distributed. Which Azure data service should you use?

You are designing a data storage solution for a global e-commerce company. The company needs to store clickstream data from millions of users with high write throughput and low-latency reads for real-time analytics. The data is semi-structured and includes nested JSON objects. Which Azure data store should you recommend?

You are migrating a large on-premises SQL Server database to Azure Synapse Analytics. The database includes tables with up to 500 million rows and frequent updates. You need to minimize data movement during the migration while ensuring optimal query performance in the dedicated SQL pool. Which table design strategy should you use?

You are designing a data lake architecture using Azure Data Lake Storage Gen2. The data will be ingested from multiple sources with varying schemas. You need to organize the data in a way that supports both batch and streaming analytics while maintaining data lineage. Which folder structure convention should you use?

You are tuning a dedicated SQL pool in Azure Synapse Analytics. A query that joins two large tables (fact_sales and dim_product) is slow. The fact_sales table is hash-distributed on product_id, and dim_product is replicated. You notice that the query plan shows a shuffle move. What is the most likely cause?

Which THREE of the following are best practices for designing tables in a dedicated SQL pool in Azure Synapse Analytics?

Which TWO of the following Azure services can be used to orchestrate data pipelines that include data transformation?

You need to assign permissions to a service principal so that it can write data to a specific container in Azure Data Lake Storage Gen2, but not delete blobs. The above JSON shows the built-in role 'Storage Blob Data Contributor'. The role includes delete permission in DataActions. What should you do?

You need to store semi-structured JSON data from a web application that requires low-latency reads and writes at a global scale. The data must be indexed automatically and support SQL-like queries. Which Azure data store should you use?

You are implementing a data lake using Azure Data Lake Storage Gen2. Which THREE actions should you take to secure the data at rest and in transit?

You need to design a data storage solution for a batch processing pipeline that processes petabytes of data daily. The data is stored in Parquet format and must be accessible by both Azure Databricks and Azure Synapse Analytics. Which storage solution should you recommend?

Your team is migrating an on-premises SQL Server data warehouse to Azure Synapse Analytics. The source has a fact table with 500 million rows and several dimension tables. You need to choose the best distribution strategy for the fact table to minimize data movement during joins. Which distribution type should you use?

You need to store log files from multiple applications in a central location for long-term retention and occasional analysis. The data is rarely accessed after 30 days. Which storage solution should you use to minimize cost?

Your company is migrating an on-premises SQL Server database to Azure SQL Database. The database includes a large fact table with hourly updates. You need to minimize downtime during migration. Which Azure service should you use to replicate data continuously?

You are designing a data lake on Azure Data Lake Storage Gen2. The data includes customer PII that must be encrypted at rest using customer-managed keys. Which feature should you enable?

You are designing a data storage solution for real-time streaming data from IoT devices. The data must be stored in its original format for immediate processing and later transformed for analytics. Which Azure service should you use for raw data ingestion?

You have an Azure Synapse Analytics dedicated SQL pool with a table that uses hash distribution on CustomerID. You notice that queries joining this table with another table on OrderDate are slow. What is the most likely cause?

You need to store JSON files from an external partner in Azure Blob Storage. The files contain sensitive financial data. Which access method provides the highest security while allowing the partner to upload files?

Which TWO factors should you consider when choosing between Azure SQL Database and Azure SQL Managed Instance for migrating a legacy application? (Choose two.)

Which THREE considerations are important when designing a table distribution strategy for an Azure Synapse Analytics dedicated SQL pool? (Choose three.)

Which TWO features are available in Azure Data Lake Storage Gen2 but not in Azure Blob Storage? (Choose two.)

You are designing a data storage solution for a retail company that expects high volumes of small, time-series sensor data from thousands of IoT devices. The data must be stored cost-effectively and queried by time range with low latency. Which Azure data store should you recommend?

A multinational bank needs to store customer transaction records for 10 years to meet regulatory compliance. The data is rarely accessed after the first year. The solution must minimize storage costs while allowing queries on recent data with low latency. Which tiering strategy should you implement?

A healthcare organization needs to store electronic health records (EHR) in a format that supports schema flexibility and complex nested data. The solution must allow fast queries by patient ID and enable analytics with Azure Synapse. Which data store should you choose?

A logistics company needs to store delivery tracking data that is updated frequently by multiple services. The solution must support transactions across multiple documents and provide real-time analytics. Which Azure service should you recommend?

Which TWO Azure services can be used to implement a polyglot persistence architecture for an e-commerce application that requires both a relational database for orders and a document database for product catalogs?

A data engineer needs to store semi-structured JSON logs from IoT devices. The data will be queried using SQL and must support high-throughput writes. Which Azure data store is most appropriate?

Which TWO options are valid methods to load data from on-premises SQL Server into Azure Synapse Analytics?

Refer to the exhibit. An Azure Policy is defined to enforce network security on storage accounts. What does this policy do?

Refer to the exhibit. A Bicep file is used to deploy an Azure Synapse Analytics workspace. What is the purpose of the 'purviewConfiguration' property?

A data engineer needs to store log data from multiple applications in Azure. The data is append-only, heavily compressed, and queried infrequently. Cost minimization is critical. Which storage solution is best?

A data engineering team uses Azure Data Factory to load data from Azure SQL Database to Azure Data Lake Storage Gen2. They notice that the pipeline runs fail intermittently due to transient errors. They need to implement a retry policy with exponential backoff. What is the most efficient way to achieve this?

Which TWO strategies can be used to optimize storage costs for historical data in Azure Data Lake Storage Gen2?

Refer to the exhibit. An ARM template deploys an Azure Synapse Analytics workspace. What is the purpose of the 'managedVirtualNetwork' property set to 'default'?

You are designing a data storage solution for real-time analytics on IoT telemetry. The system must ingest 10,000 events per second and support sub-second query latency. Which Azure data store should you use?

A company uses Azure Synapse Analytics serverless SQL pool to query data in ADLS Gen2. Users report that queries against Parquet files are slow. What should you recommend to improve query performance?

A data engineer needs to store semi-structured JSON logs from multiple sources in Azure. The logs must be queryable using T-SQL and support schema-on-read. Which Azure service should be used?

You are designing a data lake architecture for a healthcare company. The solution must support fine-grained access control at the file level, encryption at rest and in transit, and integration with Microsoft Purview for data lineage. Which storage solution should you recommend?

A company uses Azure Synapse Analytics dedicated SQL pool. They notice that some queries are slow due to high data movement. What should you do to minimize data movement for queries that join large fact tables?

You need to store historical sales data for 10 years with infrequent queries. The storage cost must be minimized while retaining the ability to query using Azure Synapse serverless SQL pool. Which storage tier should you use?

A financial services company needs to store transaction data for audit purposes. The data must be immutable and cannot be modified or deleted for 7 years. Which Azure storage feature should be used?

Which TWO actions should you take to optimize query performance in Azure Synapse Analytics dedicated SQL pool when working with large fact tables?

You are examining a T-SQL script that creates an external table in Azure Synapse serverless SQL pool. The query SELECT * FROM dbo.Sales returns zero rows, but the folder /year=2024/ in ADLS Gen2 contains Parquet files. What is the most likely cause?

A company is designing a data lake in Azure Data Lake Storage Gen2 (ADLS Gen2) to store IoT sensor data from millions of devices. The data is ingested in Parquet format, partitioned by date and device ID. The analytics team frequently queries the last 30 days of data for specific device types. Which partition strategy minimizes query cost and optimizes performance?

You are designing a solution to store large amounts of log data that is written once and accessed rarely. The data must be retained for 7 years for compliance. After 30 days, the data should be moved to a lower-cost storage tier. After 1 year, the data should be archived. Which Azure Storage lifecycle management policy should you implement for an Azure Data Lake Storage Gen2 account?

Your company uses Azure Cosmos DB for NoSQL to store user profiles. The application frequently reads profiles by user ID (the partition key). Occasionally, the application needs to query by email address, which is not part of the partition key. What should you do to optimize the occasional queries by email?

Which THREE statements are true about partitioning in Azure Synapse Analytics dedicated SQL pool?

You are a data engineer at a financial services company. The company uses Azure Cosmos DB for NoSQL to store customer transaction data. The data is partitioned by customerId. The application team needs to run analytical queries that aggregate transactions by date across all customers. These queries are currently slow and consume high RUs. You need to enable faster analytical queries without impacting the transactional workload. What should you do?

Your team is migrating an on-premises SQL Server data warehouse to Azure Synapse Analytics. The source data includes fact tables and dimension tables with complex relationships. You need to design the storage in Azure Synapse to minimize query latency for star schema queries. Which distribution and index strategy should you use for the fact table?

You need to design a storage solution for IoT device telemetry data that will be queried by time range. The data is append-only and arrives at high velocity. Which TWO features should you use to optimize query performance and reduce costs?

You are a data engineer for a multinational e-commerce company. The company uses Azure Synapse Analytics as its data warehouse. The current fact table, SalesFact, is distributed using hash distribution on the CustomerID column. It has 2 billion rows and is 2 TB in size. Recently, the business team has been running many queries that aggregate sales by product category and date, and these queries are experiencing high data movement and long execution times. The product dimension table (ProductDim) has 100,000 rows and is 100 MB. The date dimension table (DateDim) has 5,000 rows and is 5 MB. You need to redesign the storage to minimize data movement for these aggregation queries. You cannot change the fact table distribution key to ProductID because of other critical queries that rely on CustomerID. What should you do?

You are designing a data storage solution for a marketing analytics platform. The platform collects clickstream data from websites and needs to store it for both real-time dashboards and historical analysis. The data is semi-structured (JSON) and arrives at a rate of 10,000 events per second. You need to choose an Azure storage solution that can handle the ingestion rate, support schema-on-read, and integrate with Azure Databricks for advanced analytics. The solution must also be cost-effective for long-term storage. What should you use?

You are a data engineer for a financial services company. The company uses Azure Data Lake Storage Gen2 as its data lake. You have a directory structure where each customer has a folder containing transaction files in CSV format. The security team requires that each customer's data be accessible only to that customer's users. You need to implement fine-grained access control using Azure Data Lake Storage Gen2's POSIX-like ACLs. However, you have thousands of customers, and managing ACLs individually is not feasible. What should you do?

You are tasked with designing a data storage solution for a social media analytics company. They need to store user profile data (JSON) and social media posts (text and images). The data is used for machine learning models that require fast random access to individual user profiles and the ability to run analytical queries over posts. The solution must provide low-latency reads for user profiles (milliseconds) and support for large-scale analytics on posts. Which combination of Azure data services should you recommend?

You are designing a storage solution for a financial analytics platform. The data consists of large Parquet files stored in Azure Data Lake Storage Gen2. Analysts run complex queries that scan entire partitions, but only a subset of columns is needed for each query. You need to minimize the amount of data read from storage and improve query performance. What should you do?

You are designing a data storage solution for a healthcare analytics platform. The solution must store patient records in Azure SQL Database and allow point-in-time restore for any time within the last 35 days. The data must be encrypted at rest using customer-managed keys (CMK) stored in Azure Key Vault. You need to configure the Azure SQL Database to meet these requirements. What should you do?

A company uses Azure Synapse Analytics dedicated SQL pool to store sales data. The fact table is partitioned by date and distributed by product ID. Queries often join the fact table with a small dimension table on product ID. You notice that these joins cause significant data movement. You need to minimize data movement for these joins. What should you do?

You manage an Azure Synapse Analytics dedicated SQL pool that stores a 4 TB fact table named FactSales. The table is currently distributed using ROUND_ROBIN and has a clustered columnstore index. Most analytical queries join FactSales to a much smaller dimension table DimProduct on ProductKey and then filter by DateKey. You need to redesign the physical storage to minimize data movement during these joins and improve query performance. What should you do?

You are designing a storage solution in Azure Synapse Analytics for a financial services company. The company ingests trade data into a dedicated SQL pool. The data is partitioned by trade date and queried primarily by date ranges. To improve query performance and reduce data movement, you need to choose an appropriate distribution type for the fact table. The table is large (over 2 billion rows) and frequently joined with a smaller dimension table on a non-distributed key. Which distribution type should you use?

You are designing a storage solution for a healthcare analytics platform. The platform ingests large volumes of structured patient records stored as Parquet files in Azure Data Lake Storage Gen2. Analysts query this data using Azure Synapse Analytics serverless SQL pools. To minimize query cost and improve performance, you need to choose an appropriate file organization and table type. What should you do?

You are designing a storage solution for a financial services company. The solution must store large volumes of semi-structured JSON data in Azure Data Lake Storage Gen2. The data is accessed by Azure Databricks for batch processing and by Azure Synapse Analytics for interactive queries. The data must be organized for efficient partition elimination and must support atomic operations. You need to choose the appropriate file format and partitioning strategy. What should you do?

You are designing a storage solution for a financial analytics platform that ingests CSV files into Azure Data Lake Storage Gen2. Analysts run complex T-SQL queries against the data using Azure Synapse Analytics serverless SQL pool. You need to minimize query cost and improve performance. What should you do?

You need to store structured data in Azure. The data will be accessed by multiple applications using T-SQL queries, and you require automatic indexing and serverless compute. Which Azure service should you use?

A healthcare company stores patient records in Azure Data Lake Storage Gen2. The data must be organized for efficient querying by a Synapse Analytics serverless SQL pool. The data is currently stored as many small CSV files in a flat directory. You need to improve query performance and reduce the cost of scanning unnecessary data. What should you do?

A financial services company needs to store transactional data in Azure Cosmos DB. The data is accessed by multiple applications using different partition keys. The company requires strong consistency for financial transactions and wants to minimize latency for reads and writes. Which consistency level should they choose?

You are designing a data storage solution for a financial analytics platform. The platform ingests CSV files into Azure Data Lake Storage Gen2 and processes them with Azure Synapse Analytics serverless SQL pools. Queries frequently filter on a transaction date column and a region column, but the files are currently organized in a flat folder structure. You need to minimize the amount of data scanned by serverless SQL queries while keeping the files queryable using standard T-SQL OPENROWSET. What should you do?

You are designing a storage solution for a global application that requires low-latency reads and writes of JSON documents. The data model includes nested properties, and you need to query these properties efficiently. You also need to ensure the data is available in multiple regions with automatic failover. Which Azure service should you use?

You are designing a storage layer for a fraud detection system. The system writes millions of small JSON records per hour to Azure Data Lake Storage Gen2 and must support both batch analytics and interactive queries from Azure Databricks. You need to choose a storage format and layout that minimizes query latency for selective filters on customer ID while keeping storage costs predictable. What should you do?

You are designing a data storage solution for a media company that ingests video files from various sources into Azure Data Lake Storage Gen2. The files are uploaded continuously and must be processed by Azure Databricks. You need to ensure that the data is organized efficiently for query performance and that access is secure. Which two actions should you include in your design? (Choose two.)

You are designing a data storage solution in Azure Synapse Analytics. You need to store large volumes of semi-structured data in a dedicated SQL pool. The data will be used for analytical queries that often filter on a date column and join on a customer ID column. You want to optimize query performance. Which two actions should you perform? (Choose two.)

You are designing a storage layer in Azure Data Lake Storage Gen2 for a data engineering pipeline. You need to store Parquet files that will be queried by Azure Synapse Analytics serverless SQL pools and Azure Databricks. You must optimize for query performance and minimize data scanned. Which two actions should you perform? (Choose two.)

A company is planning to migrate an on-premises data warehouse to Azure Synapse Analytics dedicated SQL pool. The data warehouse contains a large fact table with billions of rows and several dimension tables. The company wants to optimize query performance and minimize data movement during joins. They need to choose an appropriate distribution type for the fact table. The fact table is frequently joined with dimension tables on a column that has high cardinality and is evenly distributed. What distribution type should they use?

A healthcare company stores patient records in Azure Data Lake Storage Gen2. The data must be encrypted at rest using customer-managed keys (CMK) stored in Azure Key Vault. The company also requires that the encryption keys are automatically rotated every 90 days. You need to configure the storage account to meet these requirements. What should you do?

You are designing a data storage solution in Azure Synapse Analytics dedicated SQL pool. The solution must support efficient loading of large volumes of data from external sources and provide high query performance for reporting. You need to choose two table distribution types that are most appropriate for large fact tables and dimension tables respectively. (Choose two.)

A financial services firm stores trade records in an Azure Data Lake Storage Gen2 account. Regulatory requirements mandate that all data at rest be encrypted with a customer-managed key (CMK) stored in Azure Key Vault, and that the key be rotated every 90 days without re-uploading any data. The storage account currently uses Microsoft-managed keys. What should you do to meet these requirements with the least administrative effort?

You are designing a storage layer for an Azure Synapse Analytics dedicated SQL pool that ingests 4 TB of CSV files daily into a fact table. The files are landed in Azure Data Lake Storage Gen2 by an external ETL process. You need to load the data with the highest possible throughput while minimizing the load window. What should you do?

You are designing a data storage solution for a company that uses Azure Data Lake Storage Gen2. The company needs to store data in a hierarchical namespace and requires fine-grained access control at the folder and file level. The solution must also support POSIX-style permissions. Which feature should you enable?

You are designing a data storage solution in Azure Synapse Analytics. You need to store large fact tables that are frequently joined with dimension tables on a common column. The solution must minimize data movement during query execution and support high-concurrency queries. Which two actions should you take? (Choose two.)

You are designing a storage solution for a financial services company that uses Azure SQL Database. The database contains a table with sensitive customer data, including credit card numbers. Regulatory requirements mandate that the credit card numbers must be encrypted at rest and in use, and only authorized applications should be able to decrypt them. You need to implement a solution that allows encryption keys to be managed in Azure Key Vault. What should you use?

A data engineering team is designing a storage solution for a retail company that receives point-of-sale (POS) transaction data from thousands of stores. The data arrives as JSON files in Azure Data Lake Storage Gen2. The team needs to query the data using Azure Synapse Analytics serverless SQL pool and optimize for performance and cost. The data is partitioned by year, month, and day. They want to minimize the amount of data scanned per query. What should they do?

You are designing a data storage solution in Azure Synapse Analytics. You need to load data incrementally from an Azure Data Lake Storage Gen2 source into a dedicated SQL pool. The source files are appended daily with new data, and you must ensure that only new records are loaded without duplicating existing records. The target table has a column named LoadDate that records when each row was inserted. Which approach should you use?

You are designing a storage solution for a healthcare analytics platform. The data lands in Azure Data Lake Storage Gen2, and analysts must query it through Azure Synapse Analytics serverless SQL pools. Security policy requires that analysts see only the columns relevant to their role, and that access be governed by Microsoft Entra ID identities rather than shared keys. Which two actions should you include in the design? (Choose two.)

A logistics company needs to store shipment tracking events in Azure Cosmos DB. Events are written continuously throughout the day, and the most common query pattern retrieves all events for a specific shipment ID ordered by timestamp. The workload is write-heavy and must scale horizontally across partitions. Which partition key should you choose?

A media company uses Azure Data Lake Storage Gen2 to store video files and metadata. They need to ensure that when a user is deleted from Azure Active Directory, their access to the data lake is immediately revoked. They also want to minimize administrative overhead. What should they do?

A logistics company uses Azure Blob Storage to store shipping manifests as block blobs. The manifests are accessed frequently for the first 30 days, then rarely accessed for the next 60 days, and after 90 days they must be retained for seven years for compliance but are almost never accessed. You need to minimize storage costs while ensuring the data remains available for compliance audits. What should you do?

You are designing a data storage solution for a media company that stores video files in Azure Blob Storage. The company wants to optimize storage costs by automatically moving older files to cooler tiers. The files are accessed frequently for the first 30 days, then infrequently for the next 60 days, and rarely after that. You need to configure a lifecycle management policy. Which two actions should you include in the policy? (Choose two.)

A retail analytics team stores Parquet files in Azure Data Lake Storage Gen2 partitioned by year, month, and day. Queries in Azure Synapse serverless SQL pools filter on a transaction date column, but performance is poor because the engine scans all files in the folder hierarchy. You need to reduce the amount of data scanned without changing the file layout. What should you implement?

A media company ingests high-definition video files into Azure Data Lake Storage Gen2. The files are uploaded once and then read by multiple analytics jobs for 48 hours, after which they are deleted. The company wants to optimize read performance and reduce latency for the analytics jobs. Which storage configuration should you recommend?

You are designing a data storage solution for a global retail company that uses Azure Synapse Analytics dedicated SQL pool. The fact table is partitioned by date and contains 10 years of sales data. You need to implement a rolling window that keeps only the most recent 3 years of data while loading new daily data with minimal impact on concurrent queries. What should you do?