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2026 CDP-3002: Fantastic Study Materials CDP Data Engineer - Certification Exam
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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q179-Q184):NEW QUESTION # 179
When would it be advantageous to use both partitioning and bucketing on a Hive table?
- A. When data security is a primary concern
- B. When managing small datasets to reduce complexity
- C. When dealing with large datasets that require efficient querying and data sampling
- D. When data needs to be stored in a single file for archival purposes
Answer: C
Explanation:
Using both partitioning and bucketing on a Hive table is advantageous when dealing with large datasets that require efficient querying and data management. Partitioning allows for segregating data into logical segments based on column values, reducing the data scanned during queries. Bucketing further divides each partition into more manageable chunks (buckets) based on a hash function of a column, which can improve performance for certain types of queries and enable more efficient data sampling and join operations.
NEW QUESTION # 180
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?
- A. 'spark.executor.memory'
- B. 'spark.executor.instances'
- C. 'spark.executor.memoryoverhead'
- D. 'spark.driver.memory'
Answer: A
Explanation:
The configuration 'spark.executor.memory' is used to specify the amount of memory to be allocated to each Spark Executor in a Spark application. This setting is crucial for ensuring efficient memory management for the Executors running in Kubernetes pods.
NEW QUESTION # 181
You're working with a DataFrame containing customer data, including a "purchase_date" column. How can you calculate the average purchase amount per month for the past year?
- A. Use Spark SQL's MONTH function and AVG function with appropriate windowing
- B. Convert the "purchase_date" column to a string and filter based on the year
- C. Utilize Spark's machine learning library (MLIiB. for time series analysis
- D. Loop through the DataFrame and group purchases by month, calculating the average manually
Answer: A
Explanation:
Spark SQL's windowing functions offer a powerful way to perform aggregations over specific time windows. Option A demonstrates this approach:from pyspark.sql.functions import month, avg
# Assuming "purchase_date" is a timestamp column
df = df.withColumn("month", month(df["purchase_date"]))
# Filter for the past year (assuming current date is stored in a variable "current_date") df_filtered = df.filter(df["purchase_date"].between(current_date - F.interval(l, "year"), current_datE. )
# Calculate average purchase amount per month
avg_purchase_df = df_filtered.groupBy("month").agg(avg("amount").alias("avg_purchase_amount")) avg_purchase_df.show()
NEW QUESTION # 182
You're working with an ETL pipeline that extracts data from multiple sources. How can you ensure that the pipeline only processes the latest data and avoids re-processing already processed data?
- A. Configure the data sources to only provide new data by default.
- B. Rely on Airflow's built-in mechanisms to handle data freshness automatically.
- C. Implement a custom mechanism to track the last processed record for each source and filter data accordingly.
- D. Use timestamps or versioning information provided by the data sources to identify new data.
Answer: C,D
Explanation:
While option C might not always be feasible or reliable depending on the specific data source, both option A and B provide valid approaches. Utilizing timestamps, versioning, or custom tracking mechanisms allows the pipeline to identify and process only the latest data, avoiding unnecessary re-processing.
NEW QUESTION # 183
Which strategy is most effective for managing schema evolution in a big data application that relies on schema inference?
- A. Completely disabling schema inference and relying on static schemas
- B. Using a single version of the schema for all data, regardless of changes
- C. Manually updating the inferred schema for each data ingestion
- D. Integrating schema registry services that track and manage schema versions
Answer: D
Explanation:
Integrating schema registry services to track and manage schema versions is the most effective strategy for managing schema evolution in applications that rely on schema inference. Schema registries provide a centralized repository for schemas, enabling applications to handle changes in data structure over time gracefully. This approach ensures compatibility across different data versions and simplifies schema management, particularly in systems that must adapt to evolving data formats without disrupting ongoing data processing.
NEW QUESTION # 184
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