TEST CLOUDERA CDP-3002 ASSESSMENT, CDP-3002 FREE LEARNING CRAM

Test Cloudera CDP-3002 Assessment, CDP-3002 Free Learning Cram

Test Cloudera CDP-3002 Assessment, CDP-3002 Free Learning Cram

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Tags: Test CDP-3002 Assessment, CDP-3002 Free Learning Cram, Testking CDP-3002 Learning Materials, Reliable CDP-3002 Exam Simulations, CDP-3002 Test Questions Fee

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CDP-3002 - Efficient Test CDP Data Engineer - Certification Exam Assessment

Are you worried about you poor life now and again? Are you desired to gain a decent job in the near future? Do you dream of a better life? Do you want to own better treatment in the field? If your answer is yes, please prepare for the CDP-3002 exam. It is known to us that preparing for the exam carefully and getting the related certification are very important for all people to achieve their dreams in the near future. It is a generally accepted fact that the CDP-3002 Exam has attracted more and more attention and become widely acceptable in the past years.

Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q319-Q324):

NEW QUESTION # 319
While working with Spark SQL, you encounter an error message stating "Unable to resolve table 'table_name"'. What could be the potential cause of this error?

  • A. The Spark application doesn't have sufficient permissions to access the table
  • B. The table doesn't exist in the Hive metastore
  • C. The table schema is not compatible with Spark SQL
  • D. All of the above

Answer: D

Explanation:
All listed options could potentially lead to the error message. The table might not exist in the Hive metastore A, access permissions might be insufficient B, or the schema might not be compatible with Spark SQL C. Examining the metastore, checking permissions, and validating the schema can help diagnose the specific cause.


NEW QUESTION # 320
Which technologies are typically involved in schema inference processes in Cloudera Data Platform (CDP)?

  • A. Apache Avro
  • B. Apache Spark
  • C. Traditional RDBMS
  • D. Apache NiFi
  • E. Manual scripting

Answer: A,B,D

Explanation:
Apache Spark, Apache NiFi, and Apache Avro are technologies that commonly support schema inference in the Cloudera Data Platform. Spark and NiFi facilitate data processing and flow management capable of schema inference, while Avro is a data serialization system that supports schema evolution, allowing schemas to be inferred, stored, and processed alongside the data.


NEW QUESTION # 321
Which Airflow feature allows you to template your tasks, enabling dynamic generation of task parameters such as table names for data quality checks?

  • A. Variables
  • B. Jinja Templating
  • C. Airflow Plugins
  • D. XComs

Answer: B

Explanation:
Jinja Templating is a powerful feature in Apache Airflow that allows you to dynamically generate task parameters, including SQL queries, file paths, and other task arguments. This is especially useful for data quality checks where you might need to pass different table names or SQL queries to a task based on the execution date or other workflow parameters.


NEW QUESTION # 322
How does Hive handle bucketing when the data inserted into a bucketed table does not evenly distribute across the buckets?

  • A. Hive dynamically adjusts the number of buckets to evenly distribute the data.
  • B. Hive rejects the data insertion and raises an error.
  • C. Hive distributes the data based on the hash value of the bucketing column, potentially leading to skewed buckets.
  • D. Hive automatically rebalances the data across buckets using a round-robin distribution.

Answer: C

Explanation:
Hive distributes the data into buckets based on the hash value of the bucketing column(s), and it does so regardless of how evenly the data distributes across those buckets. If the data inserted into a bucketed table does not evenly distribute across the buckets, Hive will still proceed with the insertion based on the hash values, which can lead to skewed buckets if the data distribution is not uniform. Hive does not automatically rebalance data, adjust the number of buckets, or reject the insertion due to uneven distribution.


NEW QUESTION # 323
Which of the following is a benefit of using broadcast variables in Spark for caching static lookup tables?

  • A. They reduce network I/O by making data available locally on each node.
  • B. They increase the amount of data shuffle during joins.
  • C. They are automatically cleaned up after each task.
  • D. They reduce the reliability of Spark applications.

Answer: A

Explanation:
Broadcast variables are used in Spark to cache static lookup tables on each node, rather than sending this data with every task. This approach reduces network I/O by making the data available locally, which is particularly beneficial for tasks that need to access the same data repeatedly, such as when performing map-side joins or lookup operations.


NEW QUESTION # 324
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