View Full Databricks Certified Data Engineer Associate Exam Dumps and Practice Test Dumps.

 

Question 41

Which component of a Databricks Lakehouse is primarily responsible for storing tabular data with transactional capabilities?

1. Delta Lake
2. Git
3. Databricks Repos
4. Cluster policy

Correct Answer: 1

Explanation

Delta Lake provides the storage layer commonly used for reliable tabular data in the Databricks Lakehouse. It adds capabilities such as ACID transactions, schema enforcement, schema evolution, and historical data access on top of cloud object storage. Git and Databricks Repos support source-code management, while cluster policies govern compute configurations. Delta Lake is therefore central to building reliable data engineering pipelines because it provides a transactional foundation for storing and managing structured datasets.

Question 42

A data engineer receives JSON files from an external system. The structure of the files may change over time as new fields are added. Which Delta Lake capability can help the target table accommodate compatible schema changes?

1. Time travel
2. Schema evolution
3. Job scheduling
4. Data visualization

Correct Answer: 2

Explanation

Schema evolution allows a Delta table to accommodate certain compatible changes to the schema, such as the addition of new columns, when the appropriate write configuration is used. This can be useful when upstream systems gradually introduce additional fields. Time travel provides access to previous table versions, while job scheduling controls execution timing. Data visualization does not manage table schemas. Schema evolution should still be used carefully because uncontrolled changes can affect downstream applications and data quality expectations.

Question 43

A pipeline must retain every version of a Delta table for historical analysis. Which Delta Lake feature allows the engineer to query an earlier table version?

1. Auto Loader
2. Schema enforcement
3. Time travel
4. Cluster autoscaling

Correct Answer: 3

Explanation

Delta Lake time travel allows users to query previous versions of a Delta table using historical versions or timestamps, subject to the availability of the underlying data and transaction history. It is useful for auditing, troubleshooting, comparing data states, and recovering information from earlier versions. Auto Loader handles incremental file ingestion, schema enforcement validates table structure, and cluster autoscaling manages compute resources. Time travel therefore provides the historical table access required by the scenario.

Question 44

A data engineer wants to make sure a pipeline does not accidentally overwrite an existing Delta table with incompatible data. Which Delta Lake capability is most relevant?

1. Git integration
2. Time travel
3. Schema enforcement
4. Job scheduling

Correct Answer: 3

Explanation

Schema enforcement helps prevent writes that do not conform to the expected structure of a Delta table. When incoming data contains incompatible columns or data types, the write can fail rather than silently producing an inconsistent table. This protects downstream workloads from unexpected schema changes. Git integration is used for source control, time travel provides historical access, and job scheduling controls execution. Schema enforcement is therefore the most relevant capability when the goal is to protect a table from incompatible writes.

Question 45

Which Spark function is used to count the number of rows in a DataFrame?

1. count()
2. join()
3. filter()
4. union()

Correct Answer: 1

Explanation

The count() action returns the number of rows contained in a Spark DataFrame. It is useful for validation tasks, such as checking whether a pipeline produced the expected number of records. join() combines related datasets, filter() selects rows that meet a condition, and union() combines rows from compatible DataFrames. Because count() is an action, Spark must execute the relevant computation to produce the result. Data engineers should consider the processing cost when counting very large datasets.

Question 46

A DataFrame contains a column named status. The engineer wants to retain only records where the value is active. Which expression is most appropriate?

1. df.join(“status”)
2. df.filter(df.status == “active”)
3. df.union(“active”)
4. df.groupBy(“active”)

Correct Answer: 2

Explanation

The filter() operation is appropriate for retaining only rows that satisfy a condition. In this case, the expression checks whether the status column equals active. Records with other values are excluded from the resulting DataFrame. join() is used to combine datasets, union() combines rows from compatible DataFrames, and groupBy() prepares records for grouped calculations. Filtering data early in a pipeline can also reduce the amount of data that later transformations need to process.

Question 47

Which Spark operation can be used to select only the columns needed for a downstream transformation?

1. collect()
2. cache()
3. select()
4. repartition()

Correct Answer: 3

Explanation

The select() operation allows a data engineer to choose specific columns from a DataFrame. Selecting only required columns can simplify transformations and may reduce the amount of data carried through later stages of a pipeline. collect() moves data to the driver, cache() stores computed results for possible reuse, and repartition() changes how records are distributed across partitions. Therefore, select() is the direct operation for limiting a DataFrame to the columns needed by downstream processing.

Question 48

A data engineer has two DataFrames with identical schemas and wants to append the records from the second DataFrame to the first. Which operation should be used?

1. groupBy()
2. filter()
3. join()
4. union()

Correct Answer: 4

Explanation

The union() operation combines rows from two compatible DataFrames. When the DataFrames have corresponding schemas and the requirement is to append one dataset’s records to another, union is appropriate. A join() combines columns from related records based on a matching condition, while groupBy() is used for grouping and aggregation. filter() removes records that do not satisfy a condition. Therefore, union is the appropriate choice for vertically combining compatible datasets.

Question 49

A data engineer needs to calculate the average order value for each store. Which Spark pattern should be used?

1. groupBy() followed by avg()
2. filter() followed by drop()
3. union() followed by count()
4. orderBy() followed by collect()

Correct Answer: 1

Explanation

The groupBy() operation can organize records according to store, while the avg() aggregation calculates the average order value within each group. This pattern is commonly used for grouped analytical calculations in Spark. Filtering and dropping columns do not perform the required aggregation, while union combines datasets rather than calculating averages. Sorting and collecting also do not provide the requested grouped metric. The data engineer should therefore group the records by store and apply the average aggregation to the relevant numeric column.

Question 50

A data engineer wants to rename a DataFrame column from cust_id to customer_id before writing the result to a Delta table. Which operation is appropriate?

1. repartition()
2. withColumnRenamed()
3. cache()
4. dropDuplicates()

Correct Answer: 2

Explanation

The withColumnRenamed() operation can rename an existing DataFrame column without changing the underlying values. In this scenario, it can change cust_id to customer_id before the DataFrame is written to the target Delta table. repartition() changes partition distribution, cache() stores computed data for reuse, and dropDuplicates() removes duplicate records. Renaming columns can be useful for aligning incoming datasets with the naming conventions or expected schema of downstream tables.

Question 51

A data engineer wants to create a new column called total_price by multiplying the quantity column by the unit_price column. Which Spark operation is commonly used?

1. drop()
2. orderBy()
3. withColumn()
4. union()

Correct Answer: 3

Explanation

The withColumn() operation can create a new DataFrame column based on an expression involving existing columns. In this case, the engineer can calculate quantity * unit_price and assign the result to a new column named total_price. drop() removes columns, orderBy() sorts records, and union() combines compatible DataFrames. Creating derived columns with withColumn() is a common transformation pattern in Spark-based data engineering pipelines.

Question 52

Which Databricks service is primarily used to run SQL queries and perform interactive SQL analytics?

1. Databricks SQL
2. Auto Loader
3. Unity Catalog
4. Delta Sharing

Correct Answer: 1

Explanation

Databricks SQL is designed for SQL-based analytics and querying data within the Databricks environment. It provides capabilities for running SQL queries, exploring datasets, and supporting analytical workloads. Auto Loader focuses on incremental file ingestion, Unity Catalog provides governance and access management, and Delta Sharing enables secure data sharing. A data engineer or analyst can use Databricks SQL to query Delta tables and other supported data sources without needing to implement the workload entirely through Spark DataFrame code.

Question 53

A data engineer needs to discover the available catalogs, schemas, and tables that they are authorized to access. Which Databricks capability is most relevant?

1. Cluster logs
2. Data Explorer
3. Auto Loader
4. Git branches

Correct Answer: 2

Explanation

Data Explorer provides an interface for discovering and working with data assets that a user has permission to access. It can help users browse catalogs, schemas, tables, views, and related metadata. Cluster logs are primarily used for operational troubleshooting, Auto Loader handles file ingestion, and Git branches manage source-code versions. Data Explorer is therefore useful when a data engineer needs to inspect available governed data assets before building or troubleshooting a pipeline.

Question 54

A data engineer wants to ensure that a production table is accessible only to users and workloads that have been explicitly granted permission. Which Databricks capability is central to this requirement?

1. Notebook widgets
2. Unity Catalog permissions
3. Spark caching
4. Job schedules

Correct Answer: 2

Explanation

Unity Catalog permissions provide centralized access control for governed data assets in Databricks. Permissions can be granted to appropriate users, groups, or service principals according to the organization’s security requirements. Notebook widgets handle runtime parameters, Spark caching concerns data reuse and performance, and job schedules control execution timing. Applying permissions through Unity Catalog helps data teams implement controlled access rather than relying on unrestricted table access.

Question 55

A pipeline reads a large dataset and then performs several transformations. The same intermediate DataFrame is used repeatedly by different branches of the pipeline. Which approach may improve performance by avoiding repeated computation?

1. cache() the reused DataFrame
2. collect() the DataFrame
3. convert it to JSON manually
4. print every row

Correct Answer: 1

Explanation

Caching a frequently reused DataFrame can reduce repeated computation because Spark may retain the DataFrame’s computed data for subsequent operations. This can be useful when multiple downstream branches repeatedly depend on the same expensive transformation. However, caching consumes cluster resources, so it should be applied selectively based on workload characteristics. Collecting a large DataFrame to the driver can create memory problems, while converting or printing data does not provide an efficient strategy for reuse.

Question 56

Which statement describes a Spark transformation correctly?

1. It always sends all records to the driver
2. It immediately terminates the Spark application
3. It defines a new DataFrame or computation that can be evaluated later
4. It can only be used with SQL tables

Correct Answer: 3

Explanation

Spark transformations define operations that produce a new dataset or computation based on existing data. Spark generally evaluates transformations lazily, meaning that the computation is planned and executed when an action requires a result. Operations such as filter(), select(), and join() are examples of transformations. Transformations do not inherently send all records to the driver and are not limited to SQL tables. Understanding transformations and actions is important when designing efficient Spark pipelines.

Question 57

Which operation is an example of a Spark action that causes computation to produce a result?

1. filter()
2. select()
3. groupBy()
4. count()

Correct Answer: 4

Explanation

count() is a Spark action because it causes Spark to execute the necessary computation and return a result to the calling application. In contrast, filter(), select(), and groupBy() are transformations that describe how data should be processed. Spark uses lazy evaluation for transformations, allowing it to optimize the execution plan before an action triggers computation. Recognizing actions is important because they determine when a pipeline’s transformations are actually executed.

Question 58

A data engineer wants to inspect the execution plan generated by Spark for a DataFrame operation to understand how Spark intends to process it. Which method is useful for this purpose?

1. explain()
2. count()
3. collect()
4. drop()

Correct Answer: 1

Explanation

The explain() method displays information about the execution plan for a Spark DataFrame operation. It can help data engineers understand how Spark plans to execute transformations and identify opportunities for optimization. count() executes a computation and returns the number of rows, collect() transfers results to the driver, and drop() removes columns. Reviewing execution plans can be particularly useful when diagnosing inefficient queries, joins, filters, or other transformations in large-scale data pipelines.

Question 59

A data engineer notices that a join operation is taking much longer than expected because one key appears extremely frequently while many other keys are rare. What issue should be investigated?

1. Data skew
2. Git merge conflict
3. Schema naming convention
4. Notebook formatting

Correct Answer: 1

Explanation

Data skew should be investigated when certain keys contain disproportionately large amounts of data. During a join, a heavily represented key can cause one or more partitions to process much more data than others, creating slow tasks and reducing overall performance. Git merge conflicts concern source-code collaboration, schema naming conventions concern data structure, and notebook formatting does not cause this type of execution imbalance. Identifying skewed keys can help engineers determine whether specialized optimization strategies are needed.

Question 60

A data engineer has successfully tested a new pipeline transformation in development. Before deploying it to production, the engineer wants a repeatable process that allows the tested code version to be identified and deployed consistently. Which approach is most appropriate?

1. Manually copy notebook cells into production
2. Use source control with a controlled deployment workflow
3. Delete the development environment
4. Change production code directly during business hours

Correct Answer: 2

Explanation

Source control combined with a controlled deployment workflow allows teams to identify specific code versions and promote tested changes consistently. Developers can review and validate modifications before deploying an approved version to production. Manual copying makes it harder to track exactly what changed and increases the possibility of errors. Deleting the development environment provides no deployment benefit, while directly modifying production reduces control and traceability. A version-controlled deployment process supports repeatability and reliable pipeline management.