Azure Data Engineer (D305)

Correct Answer B. Heap index

Explanation

A Heap index is a table without a clustered index. In Azure Synapse Analytics, heap tables are often used for staging data because they provide fast loading performance by avoiding the overhead of maintaining indexes during data insertion. Since the data will later be transformed or moved into refined tables, quick ingestion is prioritized over query performance at this stage.

Why other options are wrong

A. Clustered columnstore index

This is best suited for analytical queries on large datasets due to high compression and performance for read-heavy workloads. However, it adds overhead during data loading, making it less ideal for staging scenarios.

C. Clustered index

Clustered indexes are optimized for query performance with sorted data but can slow down data loading due to the need to maintain the index during insert operations. This makes it less suitable for staging environments where speed is more important than retrieval efficiency.

D. No index

While this may seem similar to a heap, in Synapse, specifying a table as a heap explicitly allows the system to optimize for loading performance. Simply choosing "no index" may lead to default behaviors that don't perform as efficiently.

Correct Answer C. Column-level encryption

Explanation

Column-level encryption is the appropriate solution for protecting sensitive data, such as credit card numbers, while still allowing users to use the data as a feature for fraud detection models. By encrypting the column that contains the credit card numbers, users can still process the data for analysis without directly accessing the sensitive information. This satisfies the requirement of protecting the actual credit card numbers while enabling users to use them for models.

Why other options are wrong

A. Transparent Data Encryption (TDE) – TDE encrypts the entire database at the storage level and protects data at rest, but it does not provide the fine-grained access control that column-level encryption does. It also does not prevent users from accessing sensitive data directly.

B. Row-level security (RLS) – RLS restricts access to rows based on user context, but it does not provide encryption or prevent direct access to sensitive data. It would not protect the credit card numbers in a way that ensures users cannot access them directly.

D. Azure Active Directory (Azure AD) pass-through authentication – This option provides user authentication but does not directly relate to the encryption of sensitive data such as credit card numbers. It would not prevent users from accessing sensitive data in the database.

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.

Correct Answer C. It employs a method of storing data rows sequentially based on a key.

Explanation

Clustered indexing in Azure Synapse Analytics organizes the data rows in a table sequentially based on a specified key. This key is usually the column or set of columns that uniquely identify each row in the table. The clustered index determines the physical order of the data, which improves query performance, especially for range queries.

Why other options are wrong

A. Data is stored in column-based format with no indices

This option refers to the columnar storage format used in Azure Synapse Analytics for data warehousing, but it does not describe clustered indexing. Clustered indexing relies on rows being stored in a specific order, not column-based storage without indices.

B. Tables are optimized for fast reads and writes at the cost of storage space

While clustered indexing can improve read performance, it does not explicitly imply that it sacrifices storage space. The focus is more on organizing the data efficiently for access based on the indexing key.

D. Each row is uniquely identified by a random heap ID

This describes a heap (non-clustered) structure where rows are stored in no specific order, but clustered indexing organizes the rows based on the key. Therefore, this does not apply to clustered indexing.

Correct Answer C. lifecycle management

Explanation

Azure Storage lifecycle management enables you to define rules that automatically transition data to a different storage tier or delete it after a specified retention period. This feature is ideal for implementing a data retention solution, ensuring that records like Twitter feed data are stored and managed in a cost-effective manner while meeting retention and analytics requirements.

Why other options are wrong

A. time-based retention

While time-based retention might seem like a suitable option, it is not a direct functionality provided by Azure Storage. Lifecycle management can handle time-based retention, which allows you to automatically manage data retention based on time.

B. change feed

The change feed in Azure Storage is used to track changes to data, such as modifications or deletions. It is not primarily designed for data retention management and would not directly address customer sentiment analytics requirements or retention policies.

D. soft delete

Soft delete ensures that deleted data is retained for a configurable period, allowing you to recover deleted objects. While useful for recovery, it does not address the need for managing data retention policies or meeting specific retention requirements for analytics.

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.

Correct Answer A. SQL API

Explanation

The SQL API is the default and most commonly used API in Azure Cosmos DB. It is specifically designed to interact with document databases, allowing users to query JSON-based documents using SQL-like syntax. This API provides powerful querying capabilities and is optimized for working with document-based data structures.

Why other options are wrong

B. Table API

The Table API is intended for key-value storage scenarios and is designed for compatibility with Azure Table Storage, not document-based data.

C. Graph API

The Graph API is used for working with graph databases in Cosmos DB, based on the Gremlin query language. It is not designed for document databases.

D. MongoDB API

While the MongoDB API allows Cosmos DB to support applications written for MongoDB, it is a compatibility layer rather than the native document API for Cosmos DB. The SQL API is the native and primary interface for document databases in Cosmos DB.

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.

Correct Answer B. Sliding window

Explanation

A sliding window is ideal for this scenario because it allows you to continuously analyze events over a fixed period (10 minutes in this case) as new events come in. This window moves along the data stream and helps track the usage of a credit card in real-time, enabling fraud detection when more than 3 transactions occur within a 10-minute window.

Why other options are wrong

A. Session window

A session window is based on gaps between events, and it closes the window when a gap larger than a specified threshold occurs. While it can be used for detecting patterns over time, it does not consistently track a fixed time period (like 10 minutes), making it less suited for detecting a specific number of events within a precise time frame.

C. Tumbling window

A tumbling window divides the data stream into non-overlapping, fixed-size time windows. While this could be used for time-based analysis, it does not allow for overlapping windows. As a result, it might not be effective for continuously tracking events like the sliding window does.

D. Hopping window

A hopping window works similarly to a sliding window but does not provide the continuous overlap needed for real-time fraud detection. It looks at events in overlapping periods, but it would not be as effective for this specific case where a continuous window of 10 minutes is required.