Get Ready to Pass the CDP-3002 exam with Cloudera Latest Practice Exam [Q80-Q102]

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Get Ready to Pass the CDP-3002 exam with Cloudera Latest Practice Exam

Get Prepared for Your CDP-3002 Exam With Actual Cloudera Study Guide!

NO.80 In Apache Airflow, which strategy allows for the dynamic generation of tasks within a DAG based on external data sources, such as a list of database tables?

Using the Variable class to store and retrieve the list of tables

Utilizing the @dag decorator with dynamic input parameters

Implementing a PythonOperator that generates other tasks at runtime

Employing the TaskFlow API with dynamic task mapping

NO.81 You are processing a large dataset using Spark and need to ensure that the results are available for subsequent stages without recomputing. Which approach achieves this efficiently?

Store the data in a temporary table using Spark SQL

Use rdd.persist() with the appropriate storage level based on your needs

Implement custom logic to save the data to HDFS between stages

Leverage Spark’s automatic checkpointing mechanism

NO.82 In the context of Spark SQL, what does the Catalyst optimizer use to optimize queries?

A cost-based optimization model that considers the size of intermediate data

A rule-based optimization model that applies predefined rules to simplify queries

Machine learning algorithms to predict the fastest query execution plan

NO.83 When configuring a Spark job to run on Kubernetes within the Cloudera Data Engineering (CDE. service, which property is crucial for specifying the Docker image to be used by the executors?

spark.kubernetes.executor.container.image

spark.executor.image

spark.kubernetes.container.image

spark.cde.container.image

NO.84 What is the key benefit of using the Cloudera Data Engineering service compared to building and managing data pipelines manually?

Cost savings due to open-source technology.

Increased scalability, reliability, and manageability.

Improved data security through proprietary tools.

Faster development time for simple data pipelines.

NO.85 What is the impact of query vectorization in Cloudera’s Optimization Framework?

It slows down query execution by adding complexity

It enables the execution of SQL commands

It improves query performance by processing batches of rows together

It encrypts query results for security

NO.86 Describe how you would implement a data lineage solution within the Cloudera Data Engineering service to track the origin and flow of data throughout your data pipelines.

Manually document data lineage within the YAML configuration files.

Utilize custom scripting to track data movement between pipeline steps.

Leverage the integrated lineage tracking capabilities of the Cloudera Data Engineering service.

Integrate the service with a separate third-party lineage tracking tool.

NO.87 In the context of big data processing, what is a potential downside of relying heavily on schema inference?

Increased data storage efficiency

Potential performance overhead due to the dynamic analysis of data structure

Reduced flexibility in handling different data types

Enhanced data security and compliance

NO.88 How does partition pruning contribute to query performance in the Cloudera Data Platform?

By increasing the number of partitions

By removing unnecessary data replication

By selecting only relevant partitions for query execution

By encrypting data at rest

NO.89 A data analyst is performing a join operation in PySpark between a large DataFrame Clarge_df) and a much smaller DataFrame (‘small_df). The process is very slow. What can the analyst do to optimize this join operation?

Use “key”).

Apply before the join.

Utilize the ‘broadcast’ function in the join operation.

Increase the number of partitions in ‘small_df’.

NO.90 Which feature in Apache Airflow allows you to retry a data quality check task if it fails initially due to transient issues?

catchup

retries parameter in the task definition

SLAs

Branching

NO.91 You are deploying a Spark application on Kubernetes and need to specify the amount of memory allocated to each Executor. In your PySpark code, which configuration setting will you use?

‘spark.executor.memoryoverhead’

‘spark.executor.instances’

‘spark.executor.memory’

‘spark.driver.memory’

NO.92 In an Airflow DAG, you have tasks A, B, C, and D. Task A must complete before B and C can start, but B and C can run in parallel. Task D should only run once both B and C have completed. How do you set up these dependencies?

A]] B ]] C]] D

A]] B, C]] D

[A ]] B, A]] C] and [B, C] ]] D

A.set_downstream([B, C]) and set_upstream([B, C])

NO.93 In a PySpark application running on Kubernetes, you want to enable dynamic allocation of Executors. Which configuration setting is essential to turn on this feature?

‘spark.dynamicAllocation.enabled’

‘spark.kubernetes.dynamicAllocation.enabled’

‘spark.kubernetes.executor.dynamicAllocation’

‘spark.executor.instances’

NO.94 You’re building a complex Airflow DAG with numerous tasks and dependencies. How can you improve the DAG’s readability and maintainability?

Use clear and descriptive names for tasks, operators, and variables throughout the DAG code.

Break down the DAG into smaller sub-DAGs with well-defined functionalities.

Implement extensive logging within each task to capture detailed execution information.

All of the above

NO.95 You’re working with a large DAG that contains numerous tasks and complex dependencies. How can you improve the DAG’s readability and maintainability?

Use cryptic and abbreviated names for tasks and variables, assuming everyone understands the context.

Break down the DAG into smaller sub-DAGs with well-defined functionalities and clear naming conventions.

Implement extensive logging within each task, regardless of its purpose, to capture detailed execution information.

Utilize comments sparingly within the DAG code, as the logic should be self-explanatory.

NO.96 You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?

Convert the DataFrame to a temporary table and then use HiveQL operations

Use the DataFrame.write.saveAsTable(“table_name”) method

Implement a custom function to write individual rows to the Hive table

Use the hiveContext.sql(“lNSERT INTO table_name SELECT FROM df’) method

NO.97 In the context of Cloudera’s Optimization Framework, what is the purpose of dynamic partition pruning?

To increase the size of partitions dynamically based on data volume

To update partition metadata in real-time

To dynamically eliminate unnecessary partitions from a query plan based on runtime statistics

To partition data dynamically based on query execution plans

NO.98 For scripting and automation purposes, how can Cloudera’s CLI tools be integrated into administrative workflows?

By creating custom plugins for web browsers to manage Cloudera services.

Incorporating CLI commands into shell scripts or automation tools like Ansible, Chef, or Puppet.

Using exclusively manual CLI commands for each task without automation.

CLI tools cannot be integrated into administrative workflows; they are only for interactive use.

NO.99 You have an Airflow DAG that extracts data from a CSV file. How can you ensure that the DAG doesn’t attempt to process the same file multiple times in case of retries or task failures?

Use the File lock operator before the extraction task to prevent other tasks from accessing the file while it’s being processed.

Configure the DAG to run only once and manually trigger subsequent runs when needed.

Implement a custom mechanism to rename the processed file after each successful run.

Modify the extraction task to skip processing existing files based on a timestamp or marker within the file.

NO.100 For a dataset that is used multiple times in different transformations but is not large enough to warrant disk storage, which caching level in Apache Spark is most appropriate?

MEMORY ONLY

DISK ONLY

MEMORY AND DISK SER

MEMORY ONLY SER

NO.101 A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?

Use ‘df.collect()’.

Apply ‘df.coalesce(Y to reduce the number of partitions.

Employ to redistribute data based on a key.

Convert the DataFrame to an RDD and then back to a DataFrame.

NO.102 Your ETL pipeline processes sensitive dat
a. How can you ensure data security within Airflow?

Use environment variables to store sensitive credentials for accessing data sources.

Store passwords and other sensitive data directly within your Airflow code.

Implement encryption for sensitive data at rest and in transit.

Grant everyone access to the Airflow web UI to monitor and manage the pipeline.