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A data engineer is inspecting an ETL pipeline based on a Pyspark job that consistently encounters performance bottlenecks. Based on developer feedback, the data engineer assumes the job is low on compute resources. To pinpoint the issue, the data engineer observes the Spark Ul and finds out the job has a high CPU time vs Task time.
Which course of action should the data engineer take?
A data engineer needs to conduct Exploratory Data Analysis (EDA) on data residing in a database within the company's custom-defined cloud network. The data engineer is using SQL for this task.
Which type of SQL Warehouse will enable the data engineer to process large numbers of queries quickly and cost-effectively?
A SQL Serverless Warehouse is the most suitable option for running SQL-based exploratory data analysis at scale while minimizing operational and cost overhead. Serverless SQL warehouses are fully managed by Databricks, meaning Databricks automatically provisions and scales compute resources, applies Photon acceleration, and optimizes query execution without requiring the user to manage infrastructure. This makes them ideal for workloads involving large numbers of ad hoc or interactive SQL queries, which is common during EDA. Because the data resides in a custom-defined cloud network, serverless SQL warehouses can still securely access governed data when Unity Catalog and networking requirements are satisfied. All-purpose clusters (option A) are designed for collaborative development and mixed workloads, not cost-efficient high-concurrency SQL querying. Classic SQL warehouses (option D) are legacy and lack the performance and cost optimizations of newer serverless offerings. Pro SQL warehouses (option B) require user-managed sizing decisions, whereas serverless warehouses optimize both performance and cost automatically. Databricks documentation recommends SQL Serverless Warehouses for fast, scalable, and cost-effective SQL analytics.
Which of the following describes the relationship between Bronze tables and raw data?
Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures ''as-is'', along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer.Reference:What is a Medallion Architecture?,Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow,Apache Spark + Delta Lake concepts,Delta Lake Architecture & Azure Databricks Workspace.
Which Databricks SQL predicate correctly performs a null-safe equality comparison so that rows are matched when both sides are NULL or when both are equal non-NULL values?
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?
The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data.A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1.A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1.A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1.A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1.A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster.When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2.When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3.When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
:
1:Single Node clusters | Databricks on AWS
2:Autoscaling | Databricks on AWS
3:SQL Endpoints | Databricks on AWS
4:Databricks Lakehouse Platform | Databricks on AWS
: [Spark UI | Databricks on AWS]
