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| Section | Weight | Objectives |
|---|---|---|
| Understanding of Databricks Data Intelligence Platform | 11% | - Lakehouse platform fundamentals - Core architecture and components - Workspace navigation and interface |
| Managing Data | 8% | - Discovering and registering datasets - Data cleaning and preparation - Unity Catalog usage - Dataset versioning and management |
| Analyzing Queries | 15% | - Query history and auditing - Execution plans and analysis - Performance optimization - Liquid clustering and indexing |
| Importing Data | 5% | - S3 and cloud storage integration - Databricks Marketplace - UI-based data ingestion - Delta Sharing - API and Auto Loader |
| Creating Dashboards and Visualizations in Databricks | 16% | - Dashboard creation and layout - Filtering and interactivity - Scheduling and sharing dashboards - Visualization types and best practices |
| Securing Data | 8% | - Access control and permissions - Secure storage and compliance - Data governance policies |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - ANSI SQL syntax and functions - Warehouse configuration and performance - Aggregations and grouping - Joining and combining datasets - Creating and managing views |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Maintenance and improvement - Access control and sharing - Genie space setup and configuration - Natural language query setup |
| Data Modeling with Databricks SQL | 5% | - Performance-oriented modeling - Delta table structure - Schema design principles |
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NEW QUESTION # 62
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age > = 75 AND country = ' canada ' ;
Which of the following tables represents the output of the above command?
Answer: A
Explanation:
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. References:
The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL
NEW QUESTION # 63
In a healthcare provider organization using Delta Lake to store electronic health records, a data analyst needs to analyze a snapshot of the patient_records table from two weeks ago before some recent data corrections were applied.
What approach should the Data Engineer take to allow the analyst to query that specific prior version?
Answer: A
Explanation:
Option B is correct. Delta Lake supports time travel, which allows users to query previous table versions by timestamp or version number. The version can be obtained from table history and queried using VERSION AS OF. Restoring the table would change the current table state for everyone, which is not necessary when the analyst only needs to analyze a historical snapshot. VACUUM would remove old data files and can prevent time travel. Official Databricks extract: time travel supports "querying previous table versions," and the syntax includes SELECT * FROM people10m VERSION AS OF 123. Databricks also states that the version can be obtained from DESCRIBE HISTORY. Note: the two-week snapshot is queryable only if the required log and data files are still retained.
NEW QUESTION # 64
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the " Add visualization " button on the SQL Editor page, which of the following types of visualizations will be selected by default?
Answer: B
Explanation:
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. References: Visualization in Databricks SQL, Visualization types
NEW QUESTION # 65
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 10 minutes.
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 10 minutes or less of new data becoming available within the gold- level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?
Answer: C
Explanation:
In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard ' s refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.
NEW QUESTION # 66
Which of the following should data analysts consider when working with personally identifiable information (PII) data?
Answer: E
Explanation:
Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data. Reference:
How to Use Databricks to Encrypt and Protect PII Data
Automating Sensitive Data (PII/PHI) Detection
Databricks Certified Data Analyst Associate
NEW QUESTION # 67
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