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What Is the Databricks-Certified-Data-Analyst-Associate Certification Exam?
The Databricks-Certified-Data-Analyst-Associate certification exam is a standardized assessment designed to measure a candidate's knowledge, competencies, and practical understanding within a defined professional field. It serves as the primary requirement for earning the Data Analyst, a credential that represents a recognized level of proficiency in its respective industry. Depending on the field, this may involve theoretical knowledge, applied problem-solving, regulatory understanding, or hands-on procedural competence.
The exam is typically developed and maintained by an accrediting body or professional organization that sets the standards for the Data Analyst. This ensures that anyone who earns the credential has met a consistent benchmark, regardless of where they studied or gained their experience. For many professionals, the Databricks-Certified-Data-Analyst-Associate Certification Exam represents a formal checkpoint in their career, one that confirms readiness to take on greater responsibility within their chosen field.
Why the Data Analyst Certification Matters?
Certifications like the Data Analyst exist because industries need a reliable way to verify competence beyond a resume or a job title. Earning this credential signals to employers, clients, and colleagues that a professional has invested time in building a structured foundation of knowledge and has been evaluated against an established standard.
Beyond individual recognition, the Data Analyst certification often supports broader professional development. It can influence hiring decisions, contribute to internal advancement, or serve as a prerequisite for more specialized roles within the field. In many industries, certifications also help standardize expectations across organizations, making it easier for professionals to move between employers or sectors while carrying a credential that is widely understood and respected.
Who Should Take the Databricks-Certified-Data-Analyst-Associate Exam?
The Databricks-Certified-Data-Analyst-Associate exam is generally relevant to individuals who are either entering a field or looking to formalize skills they have already developed through experience. This can include early-career professionals seeking a credential to support their first steps into the industry, as well as experienced practitioners who want official recognition of knowledge gained on the job.
Students preparing to enter the workforce may also pursue the Databricks-Certified-Data-Analyst-Associate exam as a way to strengthen their qualifications before graduating or applying for their first roles. In some fields, employers actively encourage or require staff to pursue this certification as part of ongoing professional development, particularly in industries where standards, safety, or compliance play a significant role in daily responsibilities.
Knowledge and Skills Evaluated in the Databricks Certified Data Analyst Associate Exam
The Databricks Certified Data Analyst Associate Exam is built to evaluate both foundational knowledge and the practical judgment needed to apply that knowledge in real situations. Candidates are generally expected to understand core principles and terminology relevant to their field, along with the reasoning behind established procedures, standards, or best practices.
Depending on the industry, this may include understanding regulatory requirements, following established protocols, applying analytical or technical methods, or exercising sound judgment in situations that require careful decision-making. Rather than testing isolated facts in a vacuum, the Databricks Certified Data Analyst Associate Exam tends to reward candidates who can connect concepts to realistic scenarios, reflecting the kind of thinking expected in day-to-day professional practice.
Preparing for the Databricks-Certified-Data-Analyst-Associate certification exam becomes more effective when using high-quality and up-to-date study materials. MyCertsHub provides resources designed to help candidates build knowledge, practice consistently, and become familiar with the actual exam format.
How to Prepare for the Databricks-Certified-Data-Analyst-Associate Certification Exam?
Effective preparation for the Databricks-Certified-Data-Analyst-Associate certification exam usually begins with a clear understanding of the exam's objectives and structure. Reviewing official guidelines or documentation published by the certifying body provides the most accurate picture of what will be covered and how heavily different areas are weighted.
From there, many candidates benefit from building a structured study plan that breaks preparation into manageable sections over a set period of time. A well-organized Databricks-Certified-Data-Analyst-Associate Study Guide can help sequence this material logically, especially for those approaching a topic for the first time. Consistent review, paired with realistic practice, tends to produce better retention than concentrated last-minute studying.
Practical experience, where applicable to the field, also plays an important role in preparation. Working through Databricks-Certified-Data-Analyst-Associate Practice Questions and a Databricks-Certified-Data-Analyst-Associate practice test can help candidates identify gaps in their understanding and become familiar with the format and pacing of the actual exam. In fields where hands-on skill is assessed, supplementing study with real-world practice or supervised experience often makes the difference between recognizing correct information and genuinely understanding it.
Benefits of Earning the Data Analyst Certification
Successfully earning the Data Analyst certification offers benefits that extend well beyond passing a single exam. It provides documented proof of competence that can be referenced on a resume, professional profile, or internal performance review, offering a clear, third-party validation of skill and knowledge.
The credential can also strengthen professional credibility when working with clients, patients, stakeholders, or colleagues who may not be positioned to evaluate technical or specialized knowledge directly. Over time, this recognition often contributes to expanded career opportunities, whether through new responsibilities, higher-level roles, or eligibility for additional certifications that build on this foundational credential.
Prepare for the Databricks-Certified-Data-Analyst-Associate Exam with MyCertsHub
Preparing for the Databricks-Certified-Data-Analyst-Associate exam is a process that benefits from organized, consistent effort rather than rushed, last-minute review. MyCertsHub is designed to support that process by offering study resources, practice materials, and educational content that help candidates understand what the Databricks Certified Data Analyst Associate Exam covers and how to approach their preparation thoughtfully.
Whether someone is just beginning to explore the Data Analyst or is in the final stages of reviewing material before their exam date, MyCertsHub aims to serve as a dependable resource throughout that journey. Every candidate's path to certification looks a little different, and the goal remains the same: to provide clear, genuinely useful information that supports real understanding of the subject matter.
In which of the following situations should a data analyst use higher-order functions?
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 run through the Catalyst Optimizer
Answer: C
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A
higher-order function takes an array, implements how the array is processed, and what the result of
the computation will be. It delegates to a lambda function how to process each item in the array. This
allows you to define functions that manipulate arrays in SQL, without having to unpack and repack
them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance
benefit over user defined functions. Reference: Higher-order functions | Databricks on AWS, Working
with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order
functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks |
Databricks on AWS
Question # 2
A data analyst has created a Query in Databricks SQL, and now they want to create two datavisualizations from that Query and add both of those data visualizations to the same Databricks SQLDashboard.Which of the following steps will they need to take when creating and adding both data visualizationsto the Databricks SQL Dashboard?
A. They will need to alter the Query to return two separate sets of results. B. They will need to add two separate visualizations to the dashboard based on the same Query. C. They will need to create two separate dashboards. D. They will need to decide on a single data visualization to add to the dashboard. E. They will need to copy the Query and create one data visualization per query.
Answer: B
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking
the + button next to the Results tab and selecting Visualization. Each visualization can have a
different type, name, and configuration. To add a visualization to a dashboard, the data analyst can
click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an
existing or new dashboard. The data analyst can repeat this process for each visualization they want
to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and
create a dashboard in Databricks SQL
Question # 3
A data analyst creates a Databricks SQL Query where the result set has the following schema:region STRINGnumber_of_customer INTWhen the analyst clicks on the "Add visualization" button on the SQL Editor page, which of thefollowing types of visualizations will be selected by default?
A. Violin Chart B. Line Chart C. IBar Chart D. Histogram E. There is no default. The user must choose a visualization type.
Answer: C
According to the Databricks SQL documentation, when a data analyst clicks on the œAdd
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. Reference: Visualization in Databricks SQL, Visualization types
Question # 4
Which of the following statements about a refresh schedule is incorrect?
A. A query can be refreshed anywhere from 1 minute lo 2 weeks B. Refresh schedules can be configured in the Query Editor. C. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQLEndpoint). D. A refresh schedule is not the same as an alert. E. You must have workspace administrator privileges to configure a refresh schedule
Answer: E
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or
dashboard can configure a refresh schedule”workspace administrator privileges are not required.
Here is the breakdown of the correct information:
A . True “ Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
B . True “ You can configure refresh schedules in the Query Editor.
C . False statement “ A query being refreshed does use a SQL Warehouse. However, the option in
question says it does not use a warehouse, which would be incorrect in a different context. Since this
is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
D . True “ Refresh schedules are different from alerts; alerts are triggered based on specific
conditions being met in query results.
E . False (and thus the correct answer to this question) “ You do not need to be a workspace admin to
set a refresh schedule. You only need the correct permissions on the object.
Reference: Schedule a Query in Databricks SQL
Question # 5
A data analysis team is working with the table_bronze SQL table as a source for one of its mostcomplex projects. A stakeholder of the project notices that some of the downstream data isduplicative. The analysis team identifies table_bronze as the source of the duplication.Which of the following queries can be used to deduplicate the data from table_bronze and write it toa new table table_silver?A)CREATE TABLE table_silver ASSELECT DISTINCT *FROM table_bronze;B)CREATE TABLE table_silver ASINSERT *FROM table_bronze;C)CREATE TABLE table_silver ASMERGE DEDUPLICATE *FROM table_bronze;D)INSERT INTO TABLE table_silverSELECT * FROM table_bronze;E)INSERT OVERWRITE TABLE table_silverSELECT * FROM table_bronze;
A. Option A B. Option B C. Option C D. Option D E. Option E
Answer: A
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and
create a new table table_silver with the deduplicated data. This is the correct way to deduplicate
data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver,
without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE
DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without
removing any duplicates. Option E overwrites the existing data in table_silver with the data
from table_bronze, without removing any duplicates. Reference: Delete Duplicate using SPARK
SQL, Spark SQL - How to Remove Duplicate Rows
Question # 6
How can a data analyst determine if query results were pulled from the cache?
A. Go to the Query History tab and click on the text of the query. The slideout shows if the resultscame from the cache. B. Go to the Alerts tab and check the Cache Status alert. C. Go to the Queries tab and click on Cache Status. The status will be green if the results from thelast run came from the cache. D. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file willshow the contents of the cache. E. Go to the Data tab and click Last Query. The details of the query will show if the results came fromthe cache.
Answer: A
Databricks SQL uses a query cache to store the results of queries that have been executed
previously. This improves the performance and efficiency of repeated queries. To determine if a
query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI
and click on the text of the query. A slideout will appear on the right side of the screen, showing the
query details, including the cache status. If the result came from the cache, the cache status will
show œCached . If the result did not come from the cache, the cache status will show œNot cached .
You can also see the cache hit ratio, which is the percentage of queries that were served from the
cache. Reference: The answer can be verified from Databricks SQL documentation which provides
information on how to use the query cache and how to check the cache status. Reference link:
Databricks SQL - Query Cache
Question # 7
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?
A. It has increased customization capabilities B. It is easy to migrate existing SQL queries to Databricks SQL C. It allows for the use of Photon's computation optimizations D. It is more performant than other SQL dialects E. It is more compatible with Spark's interpreters
Answer: B
Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL
specifications defined by the American National Standards Institute (ANSI). This makes it easier to
migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a
similar dialect, such as PostgreSQL, Oracle, or Teradata. By using ANSI SQL, Databricks SQL avoids
surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such
as Spark SQL or Hive SQL12. Moreover, Databricks SQL also adds compatibility features to support
common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and
user-defined functions2. Reference: ANSI compliance in Databricks Runtime, Evolution of the SQL
language at Databricks: ANSI standard by default and easier migrations from data warehouses
Question # 8
A data analyst runs the following command:INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;What is the result of running this command?
A. The suppliers table now contains both the data it had before the command was run and the datafrom the new suppliers table, and any duplicate data is deleted. B. The command fails because it is written incorrectly. C. The suppliers table now contains both the data it had before the command was run and the datafrom the new suppliers table, including any duplicate data. D. The suppliers table now contains the data from the new suppliers table, and the new supplierstable now contains the data from the suppliers table. E. The suppliers table now contains only the data from the new suppliers table.
Answer: B
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid
syntax for inserting data into a table in Databricks SQL. According to the documentation12, the
correct syntax for inserting data into a table is either:
INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query
The command in the question is missing the OVERWRITE or INTO keyword, and the query part that
specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted.
The PARTITION clause and the column list are also optional and depend on the table schema and the
data source. Therefore, the command in the question will fail with a syntax error.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn
Question # 9
A data analyst has a managed table table_name in database database_name. They would now like toremove the table from the database and all of the data files associated with the table. The rest of thetables in the database must continue to exist.Which of the following commands can the analyst use to complete the task without producing anerror?
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;
Answer: B
The DROP TABLE command removes a table from the metastore and deletes the associated data files.
The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional
IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix
specifies the database where the table resides. If not specified, the current database is used.
Therefore, the correct command to remove the table table_name from the database database_name
and all of the data files associated with it is DROP TABLE database_name.table_name;. The other
commands are either invalid syntax or would produce undesired results. Reference: Databricks -
DROP TABLE
Question # 10
A data analyst created and is the owner of the managed table my_ table. They now want to changeownership of the table to a single other user using Data Explorer.Which of the following approaches can the analyst use to complete the task?
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
Answer: C
The Owner field in the table page shows the current owner of the table and allows the owner to
change it to another user or group. To change the ownership of the table, the owner can click on the
Owner field and select the new owner from the drop-down list. This will transfer the ownership of
the table to the selected user or group and remove the previous owner from the list of table access
control entries1. The other options are incorrect because:
A . Removing the owner-s account from the Owner field will not change the ownership of the table,
but will make the table ownerless2.
B . Selecting All Users from the Owner field will not change the ownership of the table, but will grant
all users access to the table3.
D . Selecting the Admins group from the Owner field will not change the ownership of the table, but
will grant the Admins group access to the table3.
E . Removing all access from the Owner field will not change the ownership of the table, but will
revoke all access to the table4. Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table
Question # 11
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries anddata visualizations. It is a requirement that the compute resources in the platform can be madeserverless, and it is expected that data visualizations can be placed within a dashboard.Which of the following Databricks Lakehouse Platform services/capabilities meets all of theserequirements?
A. Delta Lake B. Databricks Notebooks C. Tableau D. Databricks Machine Learning E. Databricks SQL
Answer: E
Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and
BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data
warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL
Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a
dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta
Lake, which provides reliability, performance, and governance for your data lake4. Reference:
Databricks SQL
Query data using SQL Analytics
Visualizations in Databricks notebooks
Delta Lake
Question # 12
A data analyst wants to create a dashboard with three main sections: Development, Testing, andProduction. They want all three sections on the same dashboard, but they want to clearly designatethe sections using text on the dashboard.Which of the following tools can the data analyst use to designate the Development, Testing, andProduction sections using text?
A. Separate endpoints for each section B. Separate queries for each section C. Markdown-based text boxes D. Direct text written into the dashboard in editing mode E. Separate color palettes for each section
Answer: C
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add
text to a dashboard using the %md magic command in a notebook cell and then select the dashboard
icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the
dashboard using the float layout option. Reference: Dashboards in notebooks, How to add text to a
dashboard in Databricks
Question # 13
Which of the following approaches can be used to ingest data directly from cloud-based object storage?
A. Create an external table while specifying the DBFS storage path to FROM B. Create an external table while specifying the DBFS storage path to PATH C. It is not possible to directly ingest data from cloud-based object storage D. Create an external table while specifying the object storage path to FROM E. Create an external table while specifying the object storage path to LOCATION
Answer: E
External tables are tables that are defined in the Databricks metastore using the information stored
in a cloud object storage location. External tables do not manage the data, but provide a schema and
a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE
statement and specify the object storage path to the LOCATION clause. For example, to create an
external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION 's3://bucket/path/file.parquet'
AI-generated code. Review and use carefully. More info on FAQ.
Reference: External tables
Question # 14
Which of the following benefits of using Databricks SQL is provided by Data Explorer?
A. It can be used to run UPDATE queries to update any tables in a database. B. It can be used to view metadata and data, as well as view/change permissions. C. It can be used to produce dashboards that allow data exploration. D. It can be used to make visualizations that can be shared with stakeholders. E. It can be used to connect to third party Bl cools.
Answer: B
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables,
models, and permissions in Databricks SQL. You can use Data Explorer to view schema details,
preview sample data, and see table and model details and properties. Administrators can view and
change owners, and admins and data object owners can grant and revoke
permissions1. Reference: Discover and manage data using Data Explorer
Question # 15
A data engineering team has created a Structured Streaming pipeline that processes data in microbatches 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 orless 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 tocomplete this task?
A. The required compute resources could be costly B. The gold-level tables are not appropriately clean for business reporting C. The streaming data is not an appropriate data source for a dashboard D. The streaming cluster is not fault tolerant E. The dashboard cannot be refreshed that quickly
Answer: A
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. Reference: 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
Question # 16
Which of the following is an advantage of using a Delta Lake-based data lakehouse over commondata lake solutions?
A. ACID transactions B. Flexible schemas C. Data deletion D. Scalable storage E. Open-source formats
Answer: A
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and
flexibility of a data lake with the reliability and performance of a data warehouse. One of the key
advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it
supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable
concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and
data quality checks. These features are not available in traditional data lakes, which rely on file-based
storage systems that do not support transactions. Reference:
Synapse “ Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide
Question # 17
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.Which of the following is stored alongside data files when using Delta Lake?
A. None of these B. Table metadata, data summary visualizations, and owner account information C. Table metadata D. Data summary visualizations E. Owner account information
Answer: C
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema
enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a
transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
Table schema
Partitioning information
Data file paths
Transactional operations like inserts, updates, and deletes
Commit history and version control
This metadata is critical for supporting Delta Lake-s advanced capabilities such as time travel and
efficient query execution. Delta Lake does not store data summary visualizations or owner account
information directly alongside the data files.
Reference: Delta Lake Table Features - Databricks Documentation
Question # 18
A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQLendpoint is taking too long to start up with each run.Which of the following changes can the data analyst make to reduce the start-up time for theendpoint while managing costs?
A. Reduce the SQL endpoint cluster size B. Increase the SQL endpoint cluster size C. Turn off the Auto stop feature D. Increase the minimum scaling value E. Use a Serverless SQL endpoint
Answer: E
A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run
queries. Instead, it uses a shared pool of resources that can scale up and down automatically based
on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL
endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used.
A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer
the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data
analyst should consider the trade-offs between speed, cost, and quality when choosing between a
Serverless SQL endpoint and a SQL endpoint that uses a cluster. Reference: Databricks SQL
Which of the following should data analysts consider when working with personally identifiableinformation (PII) data?
A. Organization-specific best practices for Pll data B. Legal requirements for the area in which the data was collected C. None of these considerations D. Legal requirements for the area in which the analysis is being performed E. All of these considerations
Answer: E
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
Question # 20
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint withthe Databricks Lakehouse Platform. However, some users will use other services like DatabricksMachine Learning or Databricks Data Science and Engineering.Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?
A. Business analyst B. SQL analyst C. Data engineer D. Business intelligence analyst E. Data analyst
Answer: C
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and
architectures. They use Databricks Data Science and Engineering service to perform tasks such as
data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a
secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main
focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data
engineering with Databricks
Question # 21
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?
A. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with B. Use Delta Live Tables to establish a cluster for Fivetran to interact with C. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with D. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with E. Use Workflows to establish a cluster for Fivetran to interact with
Answer: C
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran
and other ingestion partners using an automated workflow. You can select a SQL warehouse or a
cluster as the destination for your data replication, and the connection details are sent to Fivetran.
You can then choose from over 200 data sources that Fivetran supports and start ingesting data into
Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran
Question # 22
Which of the following describes how Databricks SQL should be used in relation to other businessintelligence (BI) tools like Tableau, Power BI, and looker?
A. As an exact substitute with the same level of functionality B. As a substitute with less functionality C. As a complete replacement with additional functionality D. As a complementary tool for professional-grade presentations E. As a complementary tool for quick in-platform Bl work
Answer: E
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them
by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the
built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with
popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to
access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and nocode
experiences, as well as optimized connectors and serverless compute, to enhance the
productivity and performance of BI workloads on the lakehouse. Reference: Databricks
SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations
overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse
Question # 23
A data analyst has recently joined a new team that uses Databricks SQL, but the analyst has never used Databricks before.The analyst wants to know where in Databricks SQL they can write and execute SQL queries.On which of the following pages can the analyst write and execute SQL queries?
A. Data page B. Dashboards page C. Queries page D. Alerts page E. SQL Editor page
Answer: E
The SQL Editor page is where the analyst can write and execute SQL queries in Databricks SQL. The
SQL Editor page has a query pane where the analyst can type or paste SQL statements, and a results
pane where the analyst can view the query results in a table or a chart. The analyst can also browse
data objects, edit multiple queries, execute a single query or multiple queries, terminate a query,
save a query, download a query result, and more from the SQL Editor page. Reference: Create a query in SQL editor
Question # 24
A data analyst is attempting to drop a table my_table. The analyst wants to delete all table metadataand data.They run the following command:DROP TABLE IF EXISTS my_table;While the object no longer appears when they run SHOW TABLES, the data files still exist.Which of the following describes why the data files still exist and the metadata files were deleted?
A. The table's data was larger than 10 GB B. The table did not have a location C. The table was external D. The table's data was smaller than 10 GB E. The table was managed
Answer: C
An external table is a table that is defined in the metastore, but its data is stored outside of the
Databricks environment, such as in S3, ADLS, or GCS. When an external table is dropped, only the
metadata is deleted from the metastore, but the data files are not affected. This is different from a
managed table, which is a table whose data is stored in the Databricks environment, and whose data
files are deleted when the table is dropped. To delete the data files of an external table, the analyst
needs to specify the PURGE option in the DROP TABLE command, or manually delete the files from
the storage system. Reference: DROP TABLE, Drop Delta table features, Best practices for dropping a
managed Delta Lake table
Question # 25
Which of the following layers of the medallion architecture is most commonly used by data analysts?
A. None of these layers are used by data analysts B. Gold C. All of these layers are used equally by data analysts D. Silver E. Bronze
Answer: B
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and
powers analytics, machine learning, and production applications. Data analysts typically use the gold
layer to access data that has been transformed into knowledge, rather than just information. The
gold layer represents the final stage of data quality and optimization in the
lakehouse. Reference: What is the medallion lakehouse architecture?
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