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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Understanding of Databricks Data Intelligence Platform | 11% | - Lakehouse platform fundamentals - Core architecture and components - Workspace navigation and interface |
| Topic 2: Importing Data | 5% | - UI-based data ingestion - API and Auto Loader - Databricks Marketplace - S3 and cloud storage integration - Delta Sharing |
| Topic 3: Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - Aggregations and grouping - Joining and combining datasets - Warehouse configuration and performance - ANSI SQL syntax and functions - Creating and managing views |
| Topic 4: Managing Data | 8% | - Unity Catalog usage - Data cleaning and preparation - Discovering and registering datasets - Dataset versioning and management |
| Topic 5: Creating Dashboards and Visualizations in Databricks | 16% | - Dashboard creation and layout - Scheduling and sharing dashboards - Filtering and interactivity - Visualization types and best practices |
| Topic 6: Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Genie space setup and configuration - Access control and sharing - Maintenance and improvement - Natural language query setup |
| Topic 7: Securing Data | 8% | - Data governance policies - Access control and permissions - Secure storage and compliance |
| Topic 8: Data Modeling with Databricks SQL | 5% | - Performance-oriented modeling - Delta table structure - Schema design principles |
| Topic 9: Analyzing Queries | 15% | - Execution plans and analysis - Query history and auditing - Liquid clustering and indexing - Performance optimization |
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NEW QUESTION # 37
An analyst writes a query that contains a query parameter. They then add an area chart visualization to the query. While adding the area chart visualization to a dashboard, the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart.
Which of the following statements is true?
Answer: A
Explanation:
A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. Reference: Databricks SQL dashboards, Query parameters
NEW QUESTION # 38
What describes the variance of a set of values?
Answer: C
Explanation:
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1
NEW QUESTION # 39
A data analyst is working with gold-layer tables to complete an ad-hoc project. A stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use.
Which of the following terms is used to describe this data augmentation?
Answer: A
Explanation:
Data enhancement is the process of adding or enriching data with additional information to improve its quality, accuracy, and usefulness. Data enhancement can be used to augment existing data sources with new data sources, such as external datasets, synthetic data, or machine learning models. Data enhancement can help data analysts to gain deeper insights, discover new patterns, and solve complex problems. Data enhancement is one of the applications of generative AI, which can leverage machine learning to generate synthetic data for better models or safer data sharing1.
In the context of the question, the data analyst is working with gold-layer tables, which are curated business-level tables that are typically organized in consumption-ready project-specific databases234. The gold-layer tables are the final layer of data transformations and data quality rules in the medallion lakehouse architecture, which is a data design pattern used to logically organize data in a lakehouse2. The stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use. This means that the analyst can use the additional dataset to enhance the existing gold-layer tables with more information, such as new features, attributes, or metrics. This data augmentation can help the analyst to complete the ad-hoc project more effectively and efficiently.
Reference:
What is the medallion lakehouse architecture? - Databricks
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse Platform | Databricks Blog What is the medallion lakehouse architecture? - Azure Databricks What is a Medallion Architecture? - Databricks Synthetic Data for Better Machine Learning | Databricks Blog
NEW QUESTION # 40
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold- level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?
Answer: C
Explanation:
A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
* B. The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
* C. The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
* D. The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
* E. The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. References: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload
NEW QUESTION # 41
A data analyst at an e-commerce company needs to process daily sales data. The data consists of approximately 50,000 records stored in a single CSV file, totaling about 20 MB. The analyst needs to perform aggregations and generate a summary report.
Which approach could the data analyst use in this situation?
Answer: A
Explanation:
Option B is correct. A 20 MB CSV file with about 50,000 records is small enough for a local pandas workflow. A real-time streaming solution, a distributed Spark cluster, or Hadoop MapReduce would add unnecessary complexity for a small single-file batch analysis. Databricks documentation explains that pandas is available in Databricks Runtime and can be used for data analysis, while pandas API on Spark is useful when pandas-style workloads need to scale beyond smaller datasets. Databricks also notes that pandas does not scale out to big data, which is exactly why Spark or pandas API on Spark is used for larger workloads, not for a small 20 MB file.
NEW QUESTION # 42
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