ACTUAL ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 TESTS - GUARANTEED ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 PASSING

Actual Associate-Developer-Apache-Spark-3.5 Tests - Guaranteed Associate-Developer-Apache-Spark-3.5 Passing

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

NEW QUESTION # 48
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

  • A. .format("parquet")
    .option("path", "path/to/destination/dir")
  • B. .format("parquet")
    .option("location", "path/to/destination/dir")
  • C. CopyEdit
    .option("format", "parquet")
    .option("destination", "path/to/destination/dir")
  • D. .option("format", "parquet")
    .option("location", "path/to/destination/dir")

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D


NEW QUESTION # 49
A data engineer is working ona Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streaming_df?

  • A. streaming_df.orderBy("timestamp").limit(4)
  • B. streaming_df.select(countDistinct("Name"))
  • C. streaming_df.filter(col("count") < 30).show()
  • D. streaming_df.groupby("Id").count()

Answer: D

Explanation:
Comprehensive and Detailed
Explanation:
In Structured Streaming, only a limited subset of operations is supported due to the nature of unbounded data.
Operations like sorting (orderBy) and global aggregation (countDistinct) require a full view of the dataset, which is not possible with streaming data unless specific watermarks or windows are defined.
Review of Each Option:
A). select(countDistinct("Name"))
Not allowed - Global aggregation like countDistinct() requires the full dataset and is not supported directly in streaming without watermark and windowing logic.
Reference: Databricks Structured Streaming Guide - Unsupported Operations.
B). groupby("Id").count()Supported - Streaming aggregations over a key (like groupBy("Id")) are supported.
Spark maintains intermediate state for each key.Reference: Databricks Docs # Aggregations in Structured Streaming (https://docs.databricks.com/structured-streaming/aggregation.html)
C). orderBy("timestamp").limit(4)Not allowed - Sorting and limiting require a full view of the stream (which is infinite), so this is unsupported in streaming DataFrames.Reference: Spark Structured Streaming - Unsupported Operations (ordering without watermark/window not allowed).
D). filter(col("count") < 30).show()Not allowed - show() is a blocking operation used for debugging batch DataFrames; it's not allowed on streaming DataFrames.Reference: Structured Streaming Programming Guide
- Output operations like show() are not supported.
Reference Extract from Official Guide:
"Operations like orderBy, limit, show, and countDistinct are not supported in Structured Streaming because they require the full dataset to compute a result. Use groupBy(...).agg(...) instead for incremental aggregations."- Databricks Structured Streaming Programming Guide


NEW QUESTION # 50
A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

  • A. df = spark.read.parquet("/path/events/data/*")
  • B. df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
  • C. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
  • D. df = spark.read.parquet("/path/events/data/")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To read all files recursively within a nested directory structure, Spark requires therecursiveFileLookupoption to be explicitly enabled. According to Databricks official documentation, when dealing with deeply nested Parquet files in a directory tree (as shown in this example), you should set:
df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/") This ensures that Spark searches through all subdirectories under/path/events/data/and reads any Parquet files it finds, regardless of the folder depth.
Option A is incorrect because while it includes an option,inferSchemais irrelevant here and does not enable recursive file reading.
Option C is incorrect because wildcards may not reliably match deep nested structures beyond one directory level.
Option D is incorrect because it will only read files directly within/path/events/data/and not subdirectories like
/2023/01/01.
Databricks documentation reference:
"To read files recursively from nested folders, set therecursiveFileLookupoption to true. This is useful when data is organized in hierarchical folder structures" - Databricks documentation on Parquet files ingestion and options.


NEW QUESTION # 51
A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

  • A. Set the number of partitions to a fixed value, such as 200
  • B. Set the number of partitions equal to the total number of CPUs in the cluster
  • C. Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as
    128 MB
  • D. Set the number of partitions equal to the number of nodes in the cluster

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark's best practice is to estimate partition count based on data volume and a reasonable partition size - typically 128 MB to 256 MB per partition.
With 1 TB of data: 1 TB / 128 MB # ~8000 partitions
This ensures that tasks are distributed across available CPUs for parallelism and that each task processes an optimal volume of data.
Option A (equal to cores) may result in partitions that are too large.
Option B (fixed 200) is arbitrary and may underutilize the cluster.
Option C (nodes) gives too few partitions (10), limiting parallelism.
Reference: Databricks Spark Tuning Guide # Partitioning Strategy


NEW QUESTION # 52
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

  • A. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
  • B. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
  • C. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
  • D. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Shuffling occurs in operations likegroupBy,reduceByKey, orjoin-which cause data to be moved across partitions. Therepartition()operation can also cause a shuffle, but in this context, it follows an aggregation.
InOption D, thegroupByfollowed byaggresults in a shuffle due to grouping across nodes.
Therepartition(10)is a partitioning transformation but does not involve a new shuffle since the data is already grouped.
This sequence - shuffle (groupBy) followed by non-shuffling (repartition) - is correct.
Option A does the opposite: thefilterdoes not cause a shuffle, butgroupBydoes - this makes it the wrong order.


NEW QUESTION # 53
......

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