Databricks Certified Data Analyst Associate Exam Dumps, Certified Data Analyst Associate Practice Test Questions

Databricks Certified Data Analyst Associate Exam Dumps & Practice Test Questions

Question No 1:

Which of the following benefits of using Databricks SQL is enabled by Data Explorer?

A. It allows users to run UPDATE queries to modify any tables within a database.
B. It enables users to view metadata and data, as well as manage permissions and access control.
C. It facilitates the creation of dashboards that enable in-depth data exploration.
D. It supports the generation of visualizations that can be easily shared with stakeholders.
E. It allows users to connect Databricks to third-party BI tools for data analysis.

Correct Answer:
B. It enables users to view metadata and data, as well as manage permissions and access control.

Explanation:

Databricks SQL is a platform for running SQL queries and performing analytics. Data Explorer within Databricks SQL is specifically designed for data exploration and management. It allows users to view metadata (e.g., schema details), access the data, and manage permissions. This feature is valuable for data governance tasks, as users can control access at the table level. While other options like dashboards and BI tool integrations are important, they are not directly tied to Data Explorer's capabilities.

Question No 2:

A data analyst has created and is currently the owner of a managed table called "my_table." They now want to transfer the ownership of this table to another specific user using Data Explorer.

Which of the following methods can the analyst use to change the ownership of the table to the new user?

A. Edit the "Owner" field in the table page by removing their own account
B. Edit the "Owner" field in the table page by selecting "All Users"
C. Edit the "Owner" field in the table page by selecting the new owner's account
D. Edit the "Owner" field in the table page by selecting the "Admins" group
E. Edit the "Owner" field in the table page by removing all access

Correct Answer:
C. Edit the "Owner" field in the table page by selecting the new owner's account

Explanation:

To transfer the ownership of the table in Data Explorer, the data analyst should edit the "Owner" field and select the new owner's account. This allows the new user to assume full ownership, which includes control over permissions and management of the table. Removing access or selecting "All Users" or a group like "Admins" would not appropriately assign ownership. Ensuring a specific user is designated as the owner is critical for maintaining proper access control.

Question No 3:

A data analyst works with a managed table, table_name, in a database named database_name. The analyst needs to remove the table_name table and its associated data files from the database, ensuring that the other tables in the same database remain unaffected.

Which command will help the analyst accomplish this task without causing any errors?

A. DROP DATABASE database_name;
B. DROP TABLE database_name.table_name;
C. DELETE TABLE database_name.table_name;
D. DELETE TABLE table_name FROM database_name;
E. DROP TABLE table_name FROM database_name;

Correct Answer: B. DROP TABLE database_name.table_name;

Explanation:

To remove a table from a database along with all its data files, the correct command is DROP TABLE. This command will permanently delete the specified table and any associated data files, while leaving all other tables in the database unaffected.

Let’s break down each option:

Option A: DROP DATABASE database_name;
This command deletes the entire database_name, along with all its tables, data, and associated files. Since the task requires only deleting a single table, this is not the correct choice.
Option B: DROP TABLE database_name.table_name;
This is the correct command. DROP TABLE removes the specified table and its data, including the storage files associated with it. This ensures that the table and its data are completely deleted without affecting other tables in the same database.
Option C: DELETE TABLE database_name.table_name;
This is not a valid SQL command. DELETE is used for removing data from a table, not the table itself. Therefore, this option is not applicable.
Option D: DELETE TABLE table_name FROM database_name;
This syntax is incorrect in both SQL and database management systems. DELETE FROM is the proper syntax for removing data, but it cannot be used to delete the table itself.
Option E: DROP TABLE table_name FROM database_name;
This command is incorrect due to the improper use of the FROM clause. The DROP TABLE command does not require the FROM keyword.

Conclusion: Option B is the correct solution for removing the table and its data while leaving the other tables unaffected.

Question No 4:

In which situation should a data analyst prefer to use higher-order functions? Please explain the reasoning behind each option.

A. When custom logic needs to be applied to simple, unnested data.
B. When custom logic needs to be translated into Python-native code.
C. When custom logic needs to be applied efficiently to large-scale array data structures.
D. When built-in functions are not performing tasks efficiently and are too slow.
E. When built-in functions must be executed through the Catalyst Optimizer.

Correct Answer:

C. When custom logic needs to be applied efficiently to large-scale array data structures.

Explanation:

Higher-order functions are functions that either take other functions as arguments or return them as results. They are commonly used in functional programming to simplify and optimize tasks, especially when working with data. Here’s why C is the best option:

Option A: When custom logic needs to be applied to simple, unnested data.
Higher-order functions can be useful, but they’re generally unnecessary for simple, unnested data. For straightforward tasks like iterating over simple data, built-in functions (like map(), filter(), or list comprehensions) are typically sufficient, making higher-order functions less essential.
Option B: When custom logic needs to be translated into Python-native code.
Higher-order functions are not designed to convert custom logic into Python-native code. Their primary strength lies in simplifying the application of functions over data structures, not in code translation.
Option C: When custom logic needs to be applied efficiently to large-scale array data structures.
This is the best use case for higher-order functions. When dealing with large data sets like arrays or lists, higher-order functions (such as map(), filter(), and reduce()) enable efficient application of logic across all elements. These functions are optimized for handling large datasets and can leverage parallelism or other performance optimizations provided by libraries like NumPy or Pandas.
Option D: When built-in functions are not performing tasks efficiently and are too slow.
If built-in functions are too slow, it usually indicates a performance issue with the algorithm or data structure. Higher-order functions are not a silver bullet for improving performance and may not necessarily solve performance bottlenecks. Optimizing algorithms or using more efficient built-in functions would be a better approach.
Option E: When built-in functions must be executed through the Catalyst Optimizer.
The Catalyst Optimizer is part of Spark’s optimization process, which is focused on SQL-like query optimizations. Higher-order functions are not specifically tailored for Catalyst, and using Spark’s built-in optimizations in DataFrames would be more appropriate for handling large-scale data processing tasks.

Conclusion: Higher-order functions excel at efficiently processing large-scale data structures, particularly when custom logic needs to be applied across array-like datasets, making C the ideal scenario.

Question No 5:

A data analyst has defined a user-defined function (UDF) using the following SQL code:

The function price takes two parameters, spend and units, both of type DOUBLE, and calculates the price by dividing spend by units.

Given a table called customer_summary with columns customer_spend and customer_units, the task is to apply the price function to these columns and create a new column, customer_price, that stores the result.

Which of the following SQL code blocks will correctly apply the price function to the customer_spend and customer_units columns of the customer_summary table and create the customer_price column?

A. SELECT PRICE customer_spend, customer_units AS customer_price
B. SELECT price FROM customer_summary
C. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
D. SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
E. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary

Correct Answer:
E. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary

Explanation:

In this scenario, the goal is to apply the price function to the customer_spend and customer_units columns in the customer_summary table and create a new column, customer_price, which stores the computed result. The correct SQL syntax involves calling the user-defined function (UDF) price() with the appropriate arguments (i.e., customer_spend and customer_units), and then aliasing the resulting value as customer_price. This is exactly what option E does. It correctly applies the function and aliases the result with the appropriate column name.

Let's analyze the other options:

Option A: This option incorrectly uses PRICE as if it were a column name or built-in function, and it doesn't properly call the price function. Additionally, the query lacks parentheses to invoke the function.
Option B: Here, the query is trying to select price but doesn't pass any arguments or apply the function to the columns customer_spend and customer_units. This will lead to an error since the function requires arguments to perform a calculation.
Option C: The syntax function(price(...)) is incorrect. In SQL, there's no need to wrap the function call in an additional function() wrapper. The price function is already defined and should be called directly.
Option D: The use of double(price(...)) is unnecessary because the price() function already returns a DOUBLE type. Explicitly casting it to DOUBLE is redundant.

Therefore, option E is the best and most correct solution. It calls the price function with the necessary arguments and aliases the result to the new column customer_price.

Question No 6:

In what scenarios should a data analyst consider utilizing higher-order functions in data analysis?

A. When custom logic needs to be applied to simple, unnested data
B. When custom logic needs to be converted to Python-native code
C. When custom logic needs to be applied at scale to array data objects
D. When built-in functions are taking too long to perform tasks
E. When built-in functions need to be optimized through the Catalyst Optimizer

Correct Answer: C. When custom logic needs to be applied at scale to array data objects

Explanation:

Higher-order functions are an essential concept in functional programming that allows functions to accept other functions as arguments or return them as results. These functions are particularly valuable when working with large datasets, arrays, or collections. In data analysis, they provide a convenient and efficient way to apply custom logic across datasets without the need for explicit loops.

The best scenario for utilizing higher-order functions is Option C, where you need to apply custom logic at scale to array-like data structures. Higher-order functions such as map(), filter(), and reduce() are specifically designed to process each element of an array or collection efficiently. These functions allow analysts to write concise, functional-style code that can scale to large datasets. For example, map() allows analysts to apply a function to each element of a collection, filter() helps in selectively filtering out data based on custom conditions, and reduce() is great for aggregating or combining elements in a dataset.

Let’s break down why the other options are less suitable:

Option A: Higher-order functions are not typically necessary for simple, unnested data. When the data is not large or complex, straightforward loops or built-in functions may suffice. In such cases, the overhead of using higher-order functions is unnecessary.
Option B: Converting custom logic to Python-native code is not a scenario where higher-order functions are particularly relevant. Higher-order functions work within the functional programming paradigm, and their primary advantage is simplifying complex data manipulations, not converting logic to a different language.
Option D: If built-in functions are taking too long to perform tasks, higher-order functions may not inherently resolve the performance bottleneck. Optimization may require algorithmic improvements or other techniques like parallel processing or using optimized libraries (e.g., NumPy) rather than simply using higher-order functions.
Option E: The Catalyst Optimizer is a feature in Spark for optimizing query execution plans, and it has nothing to do with higher-order functions in Python. Higher-order functions are used for functional-style programming and not for query plan optimization.

In conclusion, higher-order functions are best used when there’s a need to apply custom logic across large datasets or array-like structures. They offer a cleaner, more efficient approach to handling large-scale data processing tasks.

Question No 7:

A data analyst executes the following SQL command:

INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;

What will be the result of running this command?

A. The suppliers table now contains both its original data and the data from the new_suppliers table, with duplicate entries removed.
B. The command fails because it is syntactically incorrect.
C. The suppliers table now contains both its original data and the data from the new_suppliers table, including any duplicate entries.
D. The suppliers table now contains the data from the new_suppliers table, and the new_suppliers table is updated to include the data from the suppliers table.
E. The suppliers table now only contains the data from the new_suppliers table.

Correct Answer: B. The command fails because it is syntactically incorrect.

Explanation:

The provided SQL command is syntactically incorrect. In SQL, the INSERT INTO statement is used to insert data from one table into another, but the correct syntax is not followed here. The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers; uses the TABLE keyword incorrectly, as the INSERT INTO statement does not require this keyword.

The correct syntax to insert data from the new_suppliers table into the suppliers table is:

Key Points:

The INSERT INTO statement adds rows to an existing table.
The SELECT statement is used to retrieve data from the source table (new_suppliers).
The TABLE keyword is not used in an INSERT INTO statement.

If the correct syntax is used, all rows from new_suppliers would be added to suppliers, including duplicates, unless there are constraints (e.g., unique keys) in place on the suppliers table. However, since the original command contains a syntax error, it would result in a failure, and the data would not be inserted.

Thus, the correct answer is B: the command fails due to incorrect syntax.

Question No 8:

You are tasked with analyzing a large dataset of sales transactions using Databricks. You need to find the total sales amount for each product category over the past 6 months. The dataset is stored in a Delta table, and you want to ensure that the operation is efficient and scalable.

Which of the following approaches would be most suitable for this task?

A) Use PySpark to load the data into memory and perform the aggregation with groupBy().
B) Use SQL in Databricks to query the Delta table directly and perform the aggregation using the GROUP BY clause.
C) Use Python and pandas to load the data from the Delta table and perform the aggregation.
D) Use MLflow to train a machine learning model to predict the total sales.

Correct Answer:
B) Use SQL in Databricks to query the Delta table directly and perform the aggregation using the GROUP BY clause.

Explanation:

This question asks you to perform an aggregation on a large dataset to compute total sales for each product category. The goal is to choose the most efficient and scalable approach.

Why is Option B the correct answer?

Using SQL in Databricks to query the Delta table directly is the most efficient approach for this task. Delta Lake provides optimized performance on large datasets, particularly when using SQL to query Delta tables. Databricks is designed for scalable analytics, and running SQL queries directly on the Delta table can leverage the built-in optimizations for partitioning, caching, and indexing. The GROUP BY clause in SQL allows you to group data by product category and efficiently compute the total sales amount. SQL queries also support distributed processing, which is ideal for large datasets.

Here’s why the other options are less suitable:

Option A: PySpark with groupBy()

While PySpark can handle large datasets and perform aggregation operations, it requires more code and can be less efficient compared to using SQL, especially for simple aggregation tasks. Additionally, you would need to manually manage data partitions, memory usage, and shuffling, which SQL handles optimally when working with Delta tables.

Option C: Python and pandas

Using pandas is generally not ideal for large-scale data in Databricks. pandas works well with small to medium-sized datasets, but it does not scale well for large datasets typically handled in Databricks. Loading large data into pandas can lead to memory issues and slower performance. In contrast, Spark and Delta Lake are built for distributed processing of large-scale datasets.

Option D: MLflow for machine learning

MLflow is a machine learning tracking and deployment tool. It is not designed for direct data aggregation tasks. Training a machine learning model to predict total sales is unnecessary for this problem, as a simple aggregation query using SQL or Spark would be far more efficient.

Question No 9:

You have a DataFrame in Databricks that contains multiple columns, including a timestamp column. You need to calculate the average sales per month for the past year. The dataset is stored in a Delta table, and you need to handle any missing data in the timestamp column.

Which of the following steps should you take to ensure the accuracy of the calculation?

A) Drop all rows with missing values in the timestamp column before performing the calculation.
B) Fill in the missing timestamp values with the most recent available value and then perform the calculation.
C) Use Spark SQL to handle missing values using COALESCE() to replace missing timestamps with the current date.
D) Use Spark SQL to group by month and perform the aggregation, ignoring any rows with missing timestamps.

Correct Answer:
C) Use Spark SQL to handle missing values using COALESCE() to replace missing timestamps with the current date.

Explanation:

In this question, you are asked to calculate the average sales per month while dealing with missing values in the timestamp column.

Why is Option C the correct answer?

Using Spark SQL with the COALESCE() function is the best approach for handling missing values in the timestamp column. The COALESCE() function in SQL can replace null or missing values with a specified value, such as the current date. This ensures that the missing timestamps are handled correctly, and the dataset can be grouped by month for accurate aggregation.

For example, the following SQL query would handle missing timestamps by filling them with the current date:

Here’s why the other options are incorrect:

Option A: Drop rows with missing values

Dropping rows with missing timestamps could lead to the loss of valuable data, especially if the missing values are sparse. This is not ideal, especially when you can use functions like COALESCE() to handle missing data without losing information.

Option B: Fill missing values with the most recent value

While filling missing values with the most recent available value (also known as forward filling) may be suitable in some cases, this approach introduces potential bias by assuming that the missing timestamp corresponds to the most recent data. This may not be the best method for time-based aggregations where the exact date is crucial.

Option D: Ignore rows with missing timestamps

Ignoring rows with missing timestamps during the aggregation could lead to inaccurate results. If the missing timestamps are randomly distributed, ignoring them may bias the results by excluding relevant data points. It's better to handle missing values explicitly.

Question No 10:

You have been working with a large dataset in Databricks, and you need to perform exploratory data analysis (EDA) to identify trends and patterns. You want to visualize the data to help you with this analysis.

Which of the following tools in Databricks would be most appropriate for creating interactive visualizations directly within your notebooks?

A) Databricks Dashboards
B) Databricks Notebooks Visualizations
C) Apache Zeppelin
D) MLflow

Correct Answer: B) Databricks Notebooks Visualizations

Explanation:

This question focuses on choosing the best tool for visualizing data directly within Databricks notebooks.

Why is Option B the correct answer?

Databricks Notebooks Visualizations provide an easy and interactive way to visualize the results of your data analysis directly within the notebook interface. You can create line charts, bar graphs, scatter plots, and other visualizations to explore trends, distributions, and correlations in your dataset. This makes it an ideal tool for exploratory data analysis (EDA), allowing you to quickly analyze the data and gain insights.

For example, after running a query or a DataFrame operation, you can use the visualization tab in the Databricks notebook to create an interactive chart that helps you better understand the underlying data.

Here’s why the other options are less suitable:

Option A: Databricks Dashboards

While Databricks Dashboards allow you to share and display visualizations from notebooks, they are more suited for creating static or real-time dashboards for business or operational use. Dashboards are ideal for displaying visualizations to stakeholders but are not specifically designed for the interactive exploration of data, which is typically done within notebooks.

Option C: Apache Zeppelin

Apache Zeppelin is a web-based notebook system that is used for data analysis and visualization, but it is a separate tool from Databricks. While Zeppelin can be used for data visualization, it is not the native tool for Databricks. Databricks provides its own built-in visualization tools, making Databricks Notebooks Visualizations more appropriate.

Option D: MLflow

MLflow is a tool primarily designed for tracking and managing machine learning experiments, models, and workflows. It is not focused on general data visualization or exploratory data analysis. It is not the best choice for tasks that require the creation of interactive visualizations.

These practice questions focus on key concepts and tools used in Databricks, including Delta tables, SQL, PySpark, and visualizations. Understanding these concepts will help you succeed in the Databricks Certified Data Analyst Associate exam.