DP-203 Practice Test 9 — 10 Questions

Question 1

A company uses Azure Data Lake Storage Gen2 with Azure Databricks. They notice that the job to write data into Delta Lake tables takes too long. The data is coming from a streaming source with a high velocity of small writes. Which approach should be taken to optimize write performance?

Accepted Answer

Configure the streaming to write in micro-batches with a higher trigger interval.. Option A is correct because increasing the trigger interval for micro-batches reduces the frequency of writes, allowing more data to accumulate per batch. This minimizes the overhead of small file commits and metadata operations in Delta Lake, which is the primary bottleneck for high-velocity streaming writes. By batching more records together, the job writes fewer, larger files, improving overall throughput.

Answer

Increase the cluster size to 16 nodes.

Answer

Enable 'auto optimize' and 'optimized writes' on the Delta table.

Answer

Change the output format from Delta to Parquet.

Question 2

You are a data engineer for a retail company. The company uses Azure Data Lake Storage Gen2 to store raw transaction data partitioned by date. Each day, a folder is created with the format 'YYYY/MM/DD' containing thousands of small JSON files (each ~10 KB). An Azure Databricks job runs daily to read the previous day's folder, transform the data, and write to a Delta table for reporting. Over time, the job's execution time has increased from 15 minutes to over 2 hours. The job uses a cluster with 4 nodes (each 16 GB memory). Monitoring shows that the job spends most of its time in the 'listing files' stage. Which optimization should you implement to reduce the job duration?

Accepted Answer

Pre-process the raw data to coalesce small JSON files into larger parquet files (e.g., 256 MB each).. The job spends most of its time in the 'listing files' stage because reading thousands of small JSON files (each ~10 KB) from Azure Data Lake Storage Gen2 incurs high metadata operation overhead. Coalescing these small files into larger Parquet files (e.g., 256 MB each) reduces the number of files that Spark must list and process, dramatically cutting down the listing stage time and improving overall throughput.

Answer

Increase the number of nodes in the cluster to 16.

Answer

Change the output format from JSON to Delta and enable Delta caching.

Answer

Use Azure Data Factory instead of Databricks to copy the raw data.

Question 3

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?

Accepted Answer

You can query the external table using standard T-SQL SELECT statements.. Option C is correct because an external table in Azure Synapse Analytics dedicated SQL pool is a read-only abstraction over data stored externally (e.g., in Azure Blob Storage or Azure Data Lake Store). You can query it directly using standard T-SQL SELECT statements without importing data into the pool, leveraging PolyBase to push down predicate filtering and read only the required data.

Answer

Querying the external table automatically imports data into a round-robin distribution.

Answer

The table cannot be queried until the data is imported into the dedicated SQL pool.

Answer

You must first create a PolyBase external table before querying.

Question 4

You are designing a data processing solution using Azure Databricks with Delta Lake. The data is partitioned by date and ingested daily. You notice that the Delta table has many small files, causing slow read performance. Which strategy should you recommend to optimize the table for faster queries?

Accepted Answer

Run OPTIMIZE on the table to compact small files.. Option A is correct because running OPTIMIZE on a Delta Lake table compacts many small files into larger ones, reducing the number of files that need to be read during queries. This directly addresses the slow read performance caused by the small file problem, which is common in daily partitioned ingestion. OPTIMIZE uses bin-packing to merge files up to a target size (default 256 MB), improving scan efficiency without changing the data.

Answer

Run ZORDER BY on the date column.

Answer

Run VACUUM to delete old files.

Answer

Increase the number of partitions by adding a new partition column.

Question 5

Which THREE metrics from Azure Monitor should be used to diagnose performance bottlenecks in an Azure Data Factory pipeline?

Accepted Answer

Pipeline Succeeded Rerun Count. Pipeline Succeeded Rerun Count (A) is correct because a high number of reruns indicates that the pipeline is repeatedly failing and retrying, which directly points to a performance bottleneck such as resource contention or throttling. This metric helps identify pipelines that are not completing successfully on the first attempt, signaling underlying issues that degrade throughput.

Answer

Blob Capacity

Answer

SQL Pool DWU Used

Question 6

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?

Accepted Answer

Store the logs in Azure Blob Storage with cool access tier.. Azure Blob Storage with the cool access tier is the correct choice because it is optimized for storing large amounts of semi-structured data (like JSON logs) at low cost, with infrequent access (once a month) and long retention (7 years). The cool tier offers lower storage costs than hot or premium tiers, while still providing high durability and the ability to query logs using tools like Azure Data Lake Storage or serverless SQL. This meets the compliance requirement without the high compute or transaction costs of a database solution.

Answer

Store the logs in Azure SQL Database as a table.

Answer

Store the logs in Azure Files share.

Answer

Store the logs in Azure Cosmos DB with a JSON container.

Question 7

Which TWO options are valid ways to load data into Azure Synapse SQL Pool? (Choose two.)

Accepted Answer

Using the COPY statement. The COPY statement (Option C) is a valid and recommended method for loading data into Azure Synapse SQL Pool because it provides a high-throughput, fully managed ingestion path from Azure Data Lake Storage or Azure Blob Storage. It supports parallel loading, automatic schema inference, and built-in error handling, making it the preferred choice over older methods like PolyBase for many scenarios.

Answer

Using INSERT INTO VALUES

Answer

Using BCP utility

Answer

Using SQL Server Integration Services (SSIS)

Question 8

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?

Accepted Answer

Azure Cosmos DB (SQL API). Azure Cosmos DB (SQL API) is the correct choice because it natively supports optimistic concurrency control via ETags (HTTP entity tags) and provides automatic indexing of all fields without requiring manual index management. This makes it ideal for storing JSON documents that are frequently updated by multiple concurrent users, as it ensures conflict detection and resolution while maintaining high performance.

Answer

Azure Blob Storage

Answer

Azure SQL Database

Answer

Azure Table Storage

Question 9

You are designing a batch processing pipeline that reads CSV files from Azure Blob Storage, performs aggregations using Azure Databricks, and writes results to Azure Synapse Analytics. The pipeline must handle schema drift (new columns appearing in source files). Which approach should you recommend?

Accepted Answer

Use Spark with mergeSchema option when reading, and write using a Delta table to evolve schema automatically.. Option C is correct because Spark's `mergeSchema` option, when used with Delta Lake, automatically evolves the schema to accommodate new columns in CSV files. This allows the batch pipeline to handle schema drift without manual intervention, and writing to a Delta table ensures the schema evolution is persisted and compatible with downstream writes to Azure Synapse Analytics.

Answer

Use Azure Data Factory mapping data flows with schema drift enabled, mapping to a fixed sink schema.

Answer

Define a fixed schema in the source and ignore any new columns.

Answer

Use Azure Stream Analytics to pre-process and enforce schema.

Question 10

A data engineer needs to process a large dataset stored in Azure Blob Storage using Azure Databricks. The dataset consists of millions of small CSV files. The processing job is slow due to the overhead of reading many small files. Which technique should be used to improve performance?

Accepted Answer

Coalesce the small files into larger files using a Databricks notebook. Option C is correct because coalescing the millions of small CSV files into larger files reduces the metadata overhead and I/O operations when reading from Azure Blob Storage. Databricks can then process fewer, larger files more efficiently, as each task handles a substantial data chunk rather than incurring the cost of opening and closing many small files.

Answer

Increase the number of worker nodes in the cluster

Answer

Convert the CSV files to Parquet format

Answer

Use Delta Lake caching to store the data in memory