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You're tasked with creating a DAG in Airflow that orchestrates a complex data processing workflow. What are some key considerations for designing an effective DAG?
A. Configure the DAG to run continuously without any specific scheduling or triggering mechanism.
B. Break down the workflow into smaller, modular tasks with clear dependencies.
C. Use a single DAG for the entire workflow, regardless of its complexity.
D. Implement extensive logging within each task for detailed information, even if it slows down execution.
Answer: B
NEW QUESTION # 237
In the context of Cloudera's Optimization Framework, what role does data statistics collection play?
A. It reduces the need for data compression
B. It provides metadata for security enforcement
C. It helps the optimizer make informed decisions about data layout and query execution plans
D. It is used to generate more data
Answer: C
Explanation:
Collecting data statistics (like row counts, data distribution, and column cardinality) is crucial for the optimizer. It uses these statistics to make informed decisions about the most efficient way to execute queries, including data layout and choosing the best execution plan.
NEW QUESTION # 238
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. Utilize Spark's machine learning library (MLIiB. for time series analysis
C. Loop through the DataFrame and group purchases by month, calculating the average manually
D. Convert the "purchase_date" column to a string and filter based on the year
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 # 239
What is the primary advantage of using Apache Spark for distributed processing compared to traditional single-node processing?
A. Improved data visualization capabilities
B. Enhanced data security
C. Faster processing of large datasets
D. Increased storage capacity
Answer: C
Explanation:
Spark leverages a cluster of machines to parallelize tasks, allowing it to process massive datasets significantly faster than a single machine.
NEW QUESTION # 240
In Hive, what impact does setting the hive.exec.dynamic.partition.mode to nonstrict have on dynamic partitioning operations?
A. It disables dynamic partitioning, requiring all partitions to be predefined.
B. It allows the creation of new partitions dynamically based on the input data values.
C. It restricts the insertion of data to existing partitions only.
D. It enforces that at least one static partition be specified in the query.
Answer: B
Explanation:
Setting hive.exec.dynamic.partition.mode to nonstrict in Hive configuration allows for the creation of new partitions dynamically based on the input data values during data insertion operations. This mode removes the restriction that requires at least one static partition to be specified in the query, thereby providing greater flexibility in managing and organizing data into partitions based on the actual values encountered at runtime.
NEW QUESTION # 241
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