Databricks Data Analyst Associate Exam Dumps 2025
Databricks Data Analyst Associate Exam Dumps 2025
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Question 1:
You need to update a view named CustomerInsights that was created with the WITH SCHEMABINDING option. What step must you take first?
A.Drop and recreate the view without the WITH SCHEMABINDING option.
B.Directly update the view as WITH SCHEMABINDING does not restrict updates.
C.Use the ALTER VIEW statement to modify the view definition.
D.Remove all dependencies on the view before updating it.
Answer: C
Explanation:
✅ Use the ALTER VIEW statement to modify the view definition.
When a view is created with the WITH SCHEMABINDING option, it ensures that the underlying schema of the referenced tables cannot be changed, preserving the integrity of the view. To update a view that was created with WITH SCHEMABINDING, you must use the ALTER VIEW statement. This allows modifications to the view while maintaining schema binding.
Why Other Options Are Incorrect:
❌ Option A: Dropping and recreating the view without WITH SCHEMABINDING
Removing schema binding eliminates its benefits, potentially leading to integrity issues.
The correct approach is to modify the existing view rather than dropping and recreating it.
❌ Option B: Directly updating the view as WITH SCHEMABINDING does not restrict updates
This statement is incorrect because WITH SCHEMABINDING does restrict modifications to the underlying tables.
Any changes to the tables must be carefully managed to avoid breaking dependencies.
❌ Option D: Removing all dependencies on the view before updating it
It is not necessary to remove all dependencies before modifying the view.
Using the ALTER VIEW statement allows changes without impacting dependencies.
Question 2:
When tasked with creating an interactive geographic visualization in Plotly that displays trade flows between countries, how would you implement functionality to allow users to select a country and dynamically update the visualization to show only trade flows from the selected country?
A.Utilize Dashs callback system to update the Plotly figure based on user selection from a dcc.Dropdown component containing country names.
B.Implement a Plotly Graph Objects figure with custom JavaScript handlers to react to user selections and filter the displayed data accordingly.
C.Create a Plotly Express choropleth map and use IPython widgets to select countries, updating the map via Python callbacks.
D.Design a static Plotly map with all possible trade flows pre-calculated, using visible attributes to show/hide specific flows based on country selection.
Answer: A
Explanation:
✅ Utilize Dash’s Callback System with a dcc.Dropdown Component
Using Dash’s callback system is the most efficient and user-friendly way to dynamically update a Plotly figure based on user selection. By incorporating a dcc.Dropdown component containing country names, users can effortlessly select a country, and the visualization updates in real-time to display only the trade flows from that selected country.
Key Benefits of Using Dash’s Callback System:
Interactivity: Users can instantly see changes without reloading the page.
Real-Time Updates: A callback function listens for dropdown selection changes and updates the Plotly figure accordingly.
User-Friendly Experience: Dropdown menus make it easy for users to select a country instead of manually typing it.
Efficiency: Reduces the need for unnecessary data processing and ensures a smooth user experience.
By implementing Dash’s callback system with a dcc.Dropdown, the visualization remains dynamic, responsive, and easy to use, making it the best approach for filtering trade flows based on user selection.
Question 3:
For a deep learning model developed with TensorFlow to classify images, which method would most effectively improve model performance when you have a limited labeled dataset?
A.Augmenting the dataset by adding noise to the images
B.Implementing transfer learning using a pre-trained model as a feature extractor
C.Increasing the number of layers in the neural network to capture more complex features
D.Switching to a simpler machine learning model like logistic regression to avoid overfitting
Answer: B
Explanation:
✅Implementing Transfer Learning Using a Pre-Trained Model as a Feature Extractor
When working with a limited labeled dataset, training a deep learning model from scratch can be challenging due to insufficient data. Transfer learning provides an effective solution by leveraging a pre-trained model trained on a large dataset.
Key Advantages of Transfer Learning:
Leverages Pre-Trained Knowledge: The model has already learned rich feature representations from a large dataset.
Improves Generalization: Helps the model adapt to new data, even with limited labeled samples.
Enhances Performance: High-level features from the pre-trained model improve accuracy in image classification tasks.
Comparison with Other Options:
❌ Option A: Data Augmentation (Adding Noise)
Can help increase data diversity and prevent overfitting.
However, it may not be as effective as transfer learning when data is extremely limited.
❌ Option C: Increasing the Number of Layers
May lead to overfitting, especially when working with small datasets.
More layers require more data to generalize effectively.
❌ Option D: Using a Simpler Model (e.g., Logistic Regression)
Simpler models are not suitable for complex tasks like image classification, where deep features are crucial.
Question 4:
When utilizing schema evolution in Delta Lake, what is a key consideration to prevent downstream errors in data processing pipelines?
A.Always disable schema evolution to maintain strict compatibility
B.Ensure that new columns added through schema evolution are immediately populated with default values to avoid null errors
C.Communicate schema changes to all downstream users and adjust their queries and analytics applications accordingly
D.Schema evolution should only be used for removing columns, not adding new ones
Answer: C
Explanation:
✅Communicate Schema Changes to Downstream Users and Adjust Queries Accordingly
Schema evolution in Delta Lake allows for modifications such as adding or modifying columns. However, these changes can impact downstream data pipelines, queries, and analytics applications.
Proactive communication ensures that:
Users and applications are aware of schema modifications.
Queries and analytics workflows are adjusted accordingly to prevent errors.
Business requirements are met while maintaining data integrity.
Comparison with Other Options:
❌ Option A: Disabling Schema Evolution
Too restrictive and limits flexibility in adapting to changing data requirements.
Prevents seamless updates needed for evolving business needs.
❌ Option B: Populating New Columns with Default Values
Helps avoid null values but is not a comprehensive solution.
Does not address how queries and applications need to adapt.
❌ Option D: Limiting Schema Evolution to Column Removal
Schema evolution should include adding new columns to accommodate new data sources and features.
Simply removing columns does not fully utilize the benefits of schema evolution.
Question 5:
When considering performance tuning in Databricks, which approach is most effective for optimizing data read operations from Delta Lake?
A.Partitioning data based on frequently queried columns
B.Increasing the number of worker nodes in the cluster
C.Utilizing columnar storage formats for all tables
D.Enforcing strict schema validation on data ingestion
Answer: A
Explanation:
✅Partitioning Data Based on Frequently Queried Columns
Partitioning organizes data into multiple partitions based on a specific column or columns.
This enables partition pruning, allowing Databricks to:
Read only the necessary partitions instead of scanning the entire dataset.
Reduce data scan times, significantly improving query performance.
Optimize processing efficiency, especially for large datasets.
Comparison with Other Approaches:
❌ Increasing the Number of Worker Nodes
Enhances parallel processing, but does not directly reduce the amount of data scanned.
Higher infrastructure costs without necessarily optimizing query performance.
❌ Utilizing Columnar Storage Formats
Improves performance by reducing scan size, but lacks the targeted benefits of partition pruning.
Works best in combination with partitioning, rather than as a standalone optimization.
❌ Enforcing Strict Schema Validation on Data Ingestion
Ensures data consistency and quality, but does not optimize read operations.
For a full set of 570 questions. Go to
https://skillcertpro.com/product/databricks-data-analyst-associate-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.
Question 6:
What is the recommended method for setting up monitoring and alerting on job performance metrics in Databricks?
A.Manually checking the job‘s execution details after each run.
B.Configuring Azure Monitor with Databricks to send alerts based on specific metrics.
C.Using external tools exclusively, without leveraging Databricks‘ built-in features.
D.Writing custom Spark code to monitor job metrics and send alerts via email.
Answer: B
Explanation:
✅Configuring Azure Monitor with Databricks to Send Alerts Based on Specific Metrics
Monitoring and alerting are essential for maintaining efficiency, reliability, and performance in data processing workflows. Azure Monitor provides a seamless integration with Databricks, enabling proactive monitoring and alerting based on specific job performance metrics.
Key Benefits of Using Azure Monitor with Databricks:
✔ Real-Time Monitoring: Track critical job metrics such as execution time, resource utilization, and error rates.
✔ Custom Alerts: Set up notifications for threshold breaches and detect anomalies before they impact workflows.
✔ Centralized Dashboard: View real-time metrics and trends across all Databricks jobs in one unified interface.
✔ Proactive Issue Resolution: Quickly identify and address performance issues, minimizing downtime and inefficiencies.
Comparison with Other Approaches:
❌ Manual Monitoring: Inefficient and lacks real-time alerts, leading to delayed issue detection.
❌ Third-Party Monitoring Solutions: May require additional integration efforts and lack native support for Azure services.
❌ Ad-Hoc Performance Checks: Do not provide continuous tracking or proactive alerts.
Question 7:
How can you utilize SQL to identify duplicate rows in a sales table without removing them?
A.Using a GROUP BY clause and COUNT() function to find records appearing more than once
B.Employing window functions to assign a row number to each record and filtering by those with counts greater than one
C.Implementing a DISTINCT clause on all columns of the sales table
D.Writing a subquery that selects all rows where the sales ID is not unique
Answer: A
Explanation:
✅Using the GROUP BY Clause and COUNT() Function
How It Works:
· GROUP BY Clause – Groups rows based on specific columns, allowing aggregation and analysis of duplicate records.
· COUNT() Function – Counts the number of rows within each group to determine how often a record appears.
· Identifying Duplicates – By filtering groups where COUNT(*) > 1, we can identify records that appear more than once without deleting them.
SQL Example:
SELECT column_name, COUNT(*)
FROM sales
GROUP BY column_name
HAVING COUNT(*) > 1;
Why This Approach?
✔ Efficient Data Analysis – Quickly detects duplicate entries without modifying the table.
✔ Scalable – Works on large datasets with minimal performance impact.
✔ Non-Destructive – Identifies duplicates without deleting them, preserving data integrity.
Question 8:
How can window functions be used in SQL to calculate a moving average sales figure for the last 3 months for each product, assuming monthly sales data?
A.By employing the AVG() function with a GROUP BY clause on product and month
B.Utilizing the ROW_NUMBER() function to sequence sales data before averaging
C.Using the OVER() clause with PARTITION BY product ORDER BY month RANGE BETWEEN 2 PRECEDING AND CURRENT ROW
D.Implementing a subquery for each month, then averaging the results in the outer query
Answer: C
Explanation:
✅OVER() Clause with PARTITION BY and RANGE
How It Works:
· Window Functions – Perform calculations across a set of related rows without collapsing them into a single result.
· OVER() Clause – Defines the window of rows for the calculation.
· PARTITION BY product – Ensures the moving average is calculated separately for each product.
· ORDER BY month – Ensures calculations follow chronological order.
· RANGE BETWEEN 2 PRECEDING AND CURRENT ROW – Defines the window frame, including the current and two previous months.
SQL Example:
SELECT
product,
month,
sales,
AVG(sales) OVER (
PARTITION BY product
ORDER BY month
RANGE BETWEEN 2 PRECEDING AND CURRENT ROW
) AS moving_avg_sales
FROM sales_data;
Why This Approach?
✔ Efficient – Eliminates the need for complex joins or subqueries.
✔ Flexible – Works dynamically without requiring hardcoded date ranges.
✔ Optimized – Utilizes SQL's built-in windowing functions for performance.
Question 9:
What strategy maximizes the performance of a Delta Lake table used frequently for both read and write operations?
A.Periodic optimization of the table through Z-Ordering based on query patterns
B.Disabling schema enforcement and relying on schema inference
C.Maintaining multiple copies of the table, each optimized for specific operations
D.Leveraging caching for the entire table to improve read performance
Answer: A
Explanation:
✅Periodic Optimization Using Z-Ordering
Key Benefits of Z-Ordering:
· Efficient Data Layout – Organizes data based on frequently queried columns, improving read performance.
· Reduced Data Scanning – Aligns data storage with query patterns, minimizing the amount of data read.
· Balanced Performance – Enhances both read and write operations without excessive resource usage.
Why Z-Ordering?
Optimizes Read Queries: Reduces I/O by clustering related data together.
Enhances Write Performance: Prevents excessive small file creation and fragmentation.
Periodic Optimization: Maintains efficient access patterns as data evolves over time.
Why Other Options Fall Short?
❌ Disabling Schema Enforcement (Option B) – Can lead to inconsistent data and quality issues.
❌ Maintaining Multiple Copies (Option C) – Increases storage costs and management complexity.
❌ Full Table Caching (Option D) – Helps with read speed but doesn’t optimize storage layout like Z-Ordering.
Question 10:
What is the best practice for passing parameters between notebooks in Databricks workflows?
A.Use widgets to accept parameters in the called notebook.
B.Store parameters in a Delta table and read them in the called notebook.
C.Directly pass parameters as arguments when calling the notebook.
D.Use global variables to share parameters across notebooks.
Answer: A
Explanation:
✅Use Widgets to Accept Parameters in the Called Notebook
Why Use Widgets?
· Interactive & User-Friendly – Provides an easy way to specify and modify parameters without changing the notebook code.
· Flexible & Reusable – Allows seamless parameter updates, making workflows more adaptable.
· Collaboration-Friendly – Enables a simple interface for team members to input parameters when running notebooks.
Comparison with Other Methods
❌ Storing Parameters in a Delta Table (Option B) – Adds unnecessary complexity for simple parameter passing.
❌ Passing Parameters as Arguments (Option C) – Works but lacks the flexibility and ease of widgets.
❌ Using Global Variables (Option D) – Can lead to scope issues and poor maintainability.
For a full set of 570 questions. Go to
https://skillcertpro.com/product/databricks-data-analyst-associate-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.