DP-203 Practice Test 31 — 15 Questions

Question 1

Refer to the exhibit. An Azure Data Factory instance uses a self-hosted integration runtime. The exhibit shows the properties of the integration runtime. The data engineer notices that copy activities are failing with errors indicating that the integration runtime is not available. What is the most likely cause?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The integration runtime version is outdated and needs to be manually updated. The exhibit shows the integration runtime version as '5.24.8345.1' and the status as 'Online', but copy activities are failing. The most likely cause is that the self-hosted IR version is outdated and no longer compatible with the Azure Data Factory service endpoints, leading to connectivity failures. Auto-update being disabled (Option B) would prevent automatic updates, but the core issue is the outdated version itself, which requires manual intervention to update.

Answer

The integration runtime status is "Offline"

Answer

Auto-update is disabled, preventing the IR from updating

Answer

Self-contained interactive authoring is disabled, causing connectivity issues

Question 2

Refer to the exhibit. You are deploying an Azure Synapse Analytics workspace using an ARM template. The template defines a managed virtual network integration runtime. You need to ensure that the integration runtime can run mapping data flows with a time-to-live (TTL) of 10 minutes. What is the purpose of the 'timeToLive' property in this configuration?

Accepted Answer

It defines how long the cluster will be kept alive after a data flow completes, allowing subsequent data flows to reuse the cluster.. The 'timeToLive' property in an Azure Synapse Analytics managed virtual network integration runtime controls how long the cluster remains alive after a mapping data flow completes. By setting a TTL of 10 minutes, subsequent data flows can reuse the same warm cluster, avoiding the 5–10 minute cold start time for new clusters. This optimizes performance and reduces latency for consecutive data flow executions.

Answer

It sets the timeout for the integration runtime to connect to the data sources.

Answer

It specifies the maximum duration a data flow activity can run before timing out.

Answer

It determines the maximum number of concurrent data flows that can run on the cluster.

Question 3

Refer to the exhibit. You deploy the ARM template to create a storage account. After deployment, a developer reports that they cannot access the storage account from an Azure Databricks notebook running in the same virtual network as specified in the subnet rule. What is the most likely reason?

Accepted Answer

The Databricks cluster is not using a managed virtual network with a service endpoint to the storage account.. The storage account firewall is configured with a default deny and a subnet rule allowing traffic from a specific virtual network subnet. For an Azure Databricks notebook to access the storage account, the Databricks workspace must be deployed with a managed virtual network and the cluster must use that same subnet with a service endpoint to the storage account. If the Databricks cluster is not using a managed virtual network or the subnet is not correctly configured, the traffic will be blocked. Option A is incorrect because customer-managed keys from Key Vault do not block access; they are for encryption. Option B is incorrect because the firewall's default deny is expected, but the subnet rule should allow traffic from the Databricks VNet, not the cluster's public IP (Databricks clusters in a managed VNet use private IPs). Option D is incorrect because hierarchical namespace (ADLS Gen2) does not block access; it enables it.

Answer

The storage account uses customer-managed keys from Key Vault, which requires additional permissions.

Answer

The firewall default action is Deny, and there are no IP rules allowing the Databricks cluster's public IP.

Answer

The storage account has hierarchical namespace enabled, which blocks non-ADLS Gen2 tools.

Question 4

Refer to the exhibit. You have a mapping data flow in Azure Data Factory that aggregates sales data. The data flow runs successfully but the sink table contains only the total sum per run instead of per product. What is missing?

Accepted Answer

The aggregate transformation does not have a groupBy column. The aggregate transformation must include a groupBy column (e.g., ProductID) to produce per-product totals. Without a groupBy, the aggregate sums all rows into a single total per run. Option A is incorrect because filtering by date would not fix the lack of grouping. Option C is incorrect because a filter transformation is unrelated to aggregation grouping. Option D is incorrect because the sink append mode does not affect the aggregation logic.

Answer

The source dataset is not filtering by date

Answer

The data flow is missing a filter transformation

Answer

The sink dataset is not configured to append

Question 5

You are designing a near-real-time data processing solution that ingests millions of events per second from IoT devices. The data must be aggregated on a per-minute basis and stored in Azure Data Lake Storage Gen2 for long-term analytics. The solution must also support alerting when certain thresholds are exceeded. Which combination of Azure services should you use?

Accepted Answer

Azure IoT Hub, Azure Stream Analytics, and Azure Functions.. Azure IoT Hub is designed for secure, bidirectional communication with IoT devices and can ingest millions of events per second. Azure Stream Analytics provides low-latency, SQL-based stream processing to aggregate data on a per-minute basis and can output to Azure Data Lake Storage Gen2 for long-term analytics. Additionally, Stream Analytics can trigger Azure Functions for alerting when thresholds are exceeded, enabling near-real-time responses.

Answer

Azure Event Hubs, Azure Data Factory, and Azure SQL Database.

Answer

Azure IoT Hub, Azure Databricks with Structured Streaming, and Azure Data Lake Storage Gen2.

Answer

Azure Event Hubs, Azure Data Explorer, and Power BI.

Question 6

Your organization uses Azure Data Lake Storage Gen2 with hierarchical namespace enabled. You need to implement a security strategy that allows users to read only specific folders within a container. Which authorization method should you use?

Accepted Answer

Access control lists (ACLs) on the folder. ACLs (Access Control Lists) in Azure Data Lake Storage Gen2 can be applied to individual folders, enabling granular read permissions. Option A is incorrect because a storage account shared key grants full access to the entire account. Option B is incorrect because Azure RBAC roles like Storage Blob Data Contributor apply at the container level, affecting all folders within. Option C is incorrect because shared access signatures (SAS) can be scoped to a container or a file, but not to a specific folder within a container.

Answer

Storage account shared key

Answer

Azure RBAC roles (e.g., Storage Blob Data Contributor) at the container level

Answer

Shared access signatures (SAS) with folder-level permissions

Question 7

You need to design a near-real-time data processing solution that ingests IoT telemetry data from millions of devices. The data must be aggregated per minute and stored in Azure Cosmos DB for low-latency queries. Which Azure service combination should you use?

Accepted Answer

Azure Event Hubs -> Azure Stream Analytics -> Azure Cosmos DB. Azure Stream Analytics provides native, low-latency windowed aggregation (e.g., TumblingWindow for per-minute aggregates) directly on data ingested from Event Hubs, and it has a built-in output sink to Azure Cosmos DB. This combination meets the near-real-time requirement without needing an intermediate compute or storage layer, minimizing end-to-end latency.

Answer

Azure Event Hubs -> Azure HDInsight (Kafka) -> Azure Cosmos DB

Answer

Azure IoT Hub -> Azure Databricks (Structured Streaming) -> Azure Cosmos DB

Answer

Azure Event Hubs -> Azure Data Factory -> Azure Cosmos DB

Question 8

You are partitioning a large fact table in Azure Synapse Dedicated SQL Pool by date. The table is used for queries that filter on CustomerID and Date. You want to minimize data movement. Which distribution strategy should you use?

Accepted Answer

Hash distribution on CustomerID. Hash distribution on CustomerID is correct because queries filtering on CustomerID and Date will benefit from collocated joins and aggregations when CustomerID is the distribution key. Since the table is large and partitioned by Date, hash distribution on CustomerID minimizes data movement by ensuring that rows with the same CustomerID reside on the same distribution node, allowing filters on Date to be applied locally within each partition.

Answer

Round-robin distribution

Answer

Replicate distribution

Answer

Hash distribution on Date

Question 9

You are designing a data processing solution for a large e-commerce company. The data includes sensitive customer information that must be encrypted at rest and in transit. The solution uses Azure Data Lake Storage Gen2 and Azure Databricks. Compliance requires that all access to the data is logged and monitored. Which combination of security features should you implement?

Accepted Answer

Enable Azure Storage encryption at rest, enforce HTTPS, and configure diagnostic settings to log all read/write operations.. It covers encryption at rest (Azure Storage encryption), encryption in transit (HTTPS), and logging via diagnostic settings for read/write operations. Option A is incorrect because Azure RBAC does not log access details; it only controls permissions. Option B is incorrect because Azure Firewall restricts network traffic but does not provide logging of data access or encryption. Option D is incorrect because Transparent Data Encryption (TDE) is a feature for Azure SQL databases, not for Azure Data Lake Storage Gen2.

Answer

Enable Azure RBAC for storage and use Azure AD authentication.

Answer

Use Azure Firewall to restrict access and enable Azure AD Authentication for Databricks.

Answer

Enable transparent data encryption (TDE) on the storage account and use service endpoints.

Question 10

You are designing an ETL process in Azure Data Factory. You need to transform data using Mapping Data Flows. Which THREE of the following transformations are available in Mapping Data Flows?

Accepted Answer

Pivot. The Pivot transformation in Mapping Data Flows allows you to rotate data from rows into columns, enabling aggregation and restructuring of datasets. It is a native transformation in Azure Data Factory's Mapping Data Flows, supporting group-by columns, pivot keys, and aggregated column values.

Answer

Union All

Answer

Merge Join

Question 11

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?

Accepted Answer

Replicate the ProductDim and DateDim tables to all compute nodes. Replicating small dimension tables (ProductDim at 100 MB and DateDim at 5 MB) to all compute nodes eliminates the need to shuffle these tables across nodes during joins. In Azure Synapse Analytics, replicated tables are copied to each distribution, so when the fact table (hash-distributed on CustomerID) joins with ProductDim and DateDim on ProductID and DateID, no data movement occurs for the dimension tables. This directly reduces the high data movement and long execution times for aggregation queries by product category and date.

Answer

Create materialized views on the fact table that aggregate by product category and date

Answer

Partition the fact table by date and keep the same distribution

Answer

Change the fact table distribution to round-robin and create non-clustered indexes on ProductID and DateID

Question 12

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?

Accepted Answer

Azure Blob Storage (cool or archive tier). Azure Blob Storage with cool or archive tier is the most cost-effective solution for storing log files that are rarely accessed after 30 days. The cool tier offers low storage costs with higher access costs, while the archive tier provides the lowest storage cost but requires rehydration for access, making both ideal for long-term retention and occasional analysis.

Answer

Azure Files

Answer

Azure Data Lake Storage Gen2

Answer

Azure Cosmos DB

Question 13

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?

Accepted Answer

The join columns are not aligned; data must be shuffled across distributions. D is correct because in Azure Synapse Analytics dedicated SQL pools, hash distribution distributes rows across distributions based on a hash of the distribution key (CustomerID). When joining on OrderDate, which is not the distribution key, the join columns are not aligned across distributions. This forces data movement (shuffling) where rows from one or both tables must be redistributed to match the join key, causing significant performance degradation.

Answer

The table is not partitioned by OrderDate

Answer

Statistics on the join columns are outdated

Answer

The table should use round-robin distribution instead

Question 14

You are designing a solution to store semi-structured JSON logs from a web application in Azure Cosmos DB. The logs are written once and rarely read. The application writes up to 10,000 documents per second, and each document is about 2 KB. You need to minimize RU/s cost. Which API and indexing policy should you choose?

Accepted Answer

Azure Cosmos DB for MongoDB with a custom indexing policy that only includes a wildcard index on _id and disables automatic indexing.. Azure Cosmos DB for MongoDB with a custom indexing policy that disables automatic indexing and only includes a wildcard index on _id minimizes RU/s cost for write-heavy, rarely read workloads. Each write operation consumes RUs proportional to the number of indexed paths; by eliminating automatic indexing of all fields, you drastically reduce the per-document write RU charge. The MongoDB API supports this fine-grained indexing control, making it ideal for high-throughput ingestion of semi-structured JSON logs.

Answer

Azure Cosmos DB for Cassandra with default indexing policy.

Answer

Azure Cosmos DB for Table with default indexing policy.

Answer

Azure Cosmos DB for NoSQL with default indexing policy (automatic indexing of all fields).

Question 15

Refer to the exhibit. You are reviewing an Azure Stream Analytics job query. The job has a stream input and a reference data input. The job is failing with the error 'Reference data input must be of type Reference, not Stream'. What is the cause of the error?

Accepted Answer

The input type is Stream; it should be Reference.. The error 'Reference data input must be of type Reference, not Stream' explicitly indicates that the input configured as Stream should be set to Reference type. In Azure Stream Analytics, reference data inputs are used for static lookup data, and they must be declared as Reference input type. Setting the input type to Stream when it should be Reference causes this error. Therefore, option C is correct. Option D is incorrect because the error message does not relate to output configuration; it is strictly about input type mismatch.

Answer

The input alias is incorrect.

Answer

The JOIN syntax is incorrect for reference data.

Answer

The output type is set to ReferenceData; it should be a different type.