Azure Data Engineer (D305)

Correct Answer C. big data

Explanation

Apache Hadoop is designed to process and store big data across distributed computing environments. It uses the MapReduce programming model to process large datasets in parallel, providing scalability and fault tolerance.

Why other options are wrong

A. push-pull tools

Push-pull tools are not related to the core functionality of Hadoop, which is designed for handling large-scale data processing, not tool-based communication systems.

B. revenue

Revenue is not the focus of Hadoop. While Hadoop can process various types of data, it is not specifically aimed at processing financial metrics like revenue.

D. algorithms

While Hadoop can process data for machine learning and algorithmic analysis, its primary purpose is to handle big data storage and distributed processing, not just to process algorithms.

Correct Answer B. Parquet

Explanation

Parquet is a columnar storage format designed for efficiency, both in terms of minimizing the size of files and improving the performance of analytics workloads. Parquet files store data in a way that allows automatic creation of column statistics, which enhances query optimization in systems like Azure Synapse Analytics. This format minimizes file size through efficient compression, which is beneficial for large-scale data processing. Additionally, since it is columnar, it allows for better query performance when only specific columns need to be read.

Why other options are wrong

A. JSON

JSON is a flexible, human-readable format that is widely used for data interchange but does not optimize file size or performance for analytical queries. It also lacks the ability to create column statistics automatically, making it inefficient for large-scale analytics.

C. Avro

Avro is another file format optimized for schema evolution and is widely used in event-driven applications. While it can compress data efficiently, it does not offer the same performance optimizations for analytical queries as Parquet, especially regarding column-based statistics.

D. CSV

CSV files are a simple, text-based format and are not optimized for performance or file size in analytical scenarios. They don't automatically create column statistics, and querying large CSV files can be inefficient compared to columnar formats like Parquet.

Correct Answer E. Dynamic data masking

Explanation

Dynamic Data Masking (DDM) is a feature in Azure Synapse Analytics that helps protect sensitive data by limiting exposure to the data, such as showing only partial values. With DDM, you can define masking rules for specific columns so that only authorized users can view the full value. In this case, you can mask the Phone column to display only the last four digits, while the rest of the number is hidden for users in the specific role.

Why other options are wrong

A. Table partitions

Table partitions are used for dividing large tables into smaller, manageable parts based on a specific column's value (such as date). While they improve query performance and management of large tables, they do not control what data is visible to users.

B. A default value

A default value is used to assign a value to a column when a new row is inserted without specifying a value for that column. It does not control user access or data masking.

C. Row-level security (RLS)

RLS is used to restrict access to rows in a table based on the user's role or some conditions, but it is not intended to mask or obfuscate column values. It focuses on row-level visibility, not column-level data masking.

D. Column encryption

Column encryption encrypts data in a column so that it is secure and only accessible by users with the appropriate decryption keys. However, this would make the entire phone number hidden to all users, which is not the desired solution here, as we need users to see the last four digits.

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.

Correct Answer C. It allows for high levels of compression and is ideal for large fact tables.

Explanation

Clustered columnstore indexing in Azure Synapse Analytics is designed for analytical workloads, particularly for large fact tables. It stores data in a columnar format, which provides high levels of compression and efficiency when querying large datasets. This indexing is particularly suitable for data warehousing scenarios where read-heavy operations, like analytical queries, are common. The compression provided by columnstore indexing helps in reducing the storage requirements and speeding up query performance.

Why other options are wrong

A. It is most suitable for OLTP workloads.

Clustered columnstore indexing is optimized for OLAP (Online Analytical Processing) workloads, not OLTP (Online Transaction Processing). OLTP workloads typically benefit from row-based indexing, not column-based indexing.

B. It offers row-based data storage.

Clustered columnstore indexing uses column-based storage, not row-based storage. This is what enables its high compression and query performance for analytical workloads.

D. It is primarily used for staging data before loading it into refined tables.

While columnstore indexing can be used to store large datasets efficiently, it is not primarily used for staging data. It is meant for storing and querying large volumes of data in analytics scenarios, not just for interim data storage.

Correct Answer A. Archive to a storage account.

Explanation

Archiving to a storage account provides the most cost-effective solution for retaining data indefinitely for auditing purposes. Azure Storage, especially with the Archive tier, is designed to store data at a low cost while allowing for long-term retention. The Archive tier is perfect for infrequently accessed data, making it the best choice when minimizing costs for indefinite retention.

Why other options are wrong

B. Send to a Log Analytics workspace.

Log Analytics workspaces are more suited for real-time monitoring and querying, not for indefinite storage at a low cost. Storing large amounts of historical data in Log Analytics can become expensive over time, especially for data that does not need to be frequently queried.

C. Send to a partner solution.

Partner solutions may offer additional features, but they typically come with their own costs and complexities. This option may not be as cost-efficient or simple as using Azure Storage, especially for long-term data retention.

D. Stream to an Azure event hub.

Azure Event Hubs is designed for real-time streaming of data, not long-term storage. While it is useful for ingesting large amounts of event data, it does not provide the low-cost, long-term retention options required for auditing purposes.

Correct Answer A. Auditing Tab

Explanation

The Auditing Tab within the Security settings of Azure SQL Database allows administrators to configure auditing policies that track and log database events. This feature helps in maintaining compliance, understanding database activity, and detecting anomalies or unauthorized access attempts. It provides flexibility in choosing the destination for logs, such as storage accounts, Event Hubs, or Log Analytics.

Why other options are wrong

B. Monitoring Tab

The Monitoring Tab is used to view metrics and diagnostic settings but does not contain the controls for enabling or configuring auditing of database events. It focuses on performance and health rather than security auditing.

C. Security Policies

Security Policies are used for broader security configurations like threat detection, data classification, or encryption settings. They are not specifically designed for auditing event logs or tracking access.

D. Access Control

Access Control (IAM) manages role-based access to the Azure SQL Database but does not configure event logging or auditing. It determines who can access and manage the database, not what actions should be tracked.

Correct Answer B. View the run details in the run history.

Explanation

In Azure Synapse Analytics, the run history contains detailed information about the execution of each pipeline, including the duration of each individual activity. This is the best way to track how long each activity took to complete without the need to rerun the pipeline.

Why other options are wrong

A. Rerun the pipeline and observe the output, timing each activity – This is not an efficient or effective way to measure the duration of activities. The run history provides a much easier and more accurate way to see activity durations.

C. View the Refreshed value for your lakehouse's default semantic model – This option is irrelevant because the Refreshed value pertains to the refresh status of a semantic model in a lakehouse, not activity durations within a pipeline.

Correct Answer A. Archive

Explanation

The Archive tier in Azure Blob storage is designed for data that is infrequently accessed and needs to be retained for long periods. Since the retrieval time is unimportant and the goal is to minimize storage costs, the Archive tier is the ideal choice. It offers the lowest storage cost but with higher access latency and retrieval charges.

Why other options are wrong

B. Hot – The Hot tier is intended for data that is frequently accessed. While it offers low retrieval costs, it has a higher storage cost compared to the Archive tier, making it unsuitable for long-term storage where access is infrequent.

C. Cool – The Cool tier is meant for infrequently accessed data but is more expensive than the Archive tier. It provides a balance between cost and access frequency, but since retrieval time is unimportant, the Archive tier is a more cost-effective solution.