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Databricks Associate-Developer-Apache-Spark-3.5 Valid Vce: Databricks Certified Associate Developer for Apache Spark 3.5 - Python - Exam4PDF Assist you to Pass One Time

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q48-Q53):

NEW QUESTION # 48
A developer is trying to join two tables,sales.purchases_fctandsales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in thepurchases_fcttable that do not exist in thecustomer_dimtable are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
C. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
D. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.

NEW QUESTION # 49
Given this code:

.withWatermark("event_time","10 minutes")
.groupBy(window("event_time","15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
C. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
D. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.

Answer: D

Explanation:
According to Spark's watermarking rules:
"Records that are older than the watermark (event time < current watermark) are considered too late and are dropped." So, if a record'sevent_timeis earlier than (max event_time seen so far - 10 minutes), it is discarded.
Reference:Structured Streaming - Handling Late Data

NEW QUESTION # 50
A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A. Spark DataFrames, Spark SQL, and MLlib
B. Spark Streaming, GraphX, and Pandas API on Spark
C. Spark SQL, Pandas API on Spark, and Structured Streaming
D. Spark DataFrames, Structured Streaming, and GraphX

Answer: A

Explanation:
Comprehensive Explanation:
To cover structured data processing, SQL querying, and machine learning in Apache Spark, the correct combination of components is:
Spark DataFrames: for structured data processing
Spark SQL: to execute SQL queries over structured data
MLlib: Spark's scalable machine learning library
This trio is designed for exactly this type of use case.
Why other options are incorrect:
A: GraphX is for graph processing - not needed here.
B: Pandas API on Spark is useful, but MLlib is essential for ML, which this option omits.
C: Spark Streaming is legacy; GraphX is irrelevant here.
Reference:Apache Spark Modules Overview

NEW QUESTION # 51
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A. The operation will fail if the Pandas DataFrame exceeds 1000 rows
B. The operation will load all data into the driver's memory, potentially causing memory overflow
C. The conversion will automatically distribute the data across worker nodes
D. Data will be lost during conversion

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D

NEW QUESTION # 52
How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A. Increase the number of local threads based on the number of CPU cores.
B. Configure the application to run in cluster mode instead of local mode.
C. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
D. Set the spark.executor.memory property to a large value.

Answer: A

Explanation:
When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.
Using local[*] ensures Spark uses all available CPU cores for parallelism.
Example:
spark-submit --masterlocal[*]
Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.
Reference:Spark Master URLs

NEW QUESTION # 53
......

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