def METHOD_NAME():
    env_helper = Mock()
    spark_session_factory = SparkSessionFactory(env_helper)
    spark_configs = dict(spark_session_factory._get_spark_configs(is_training_job=False))
    jsc_hadoop_configs = dict(spark_session_factory._get_jsc_hadoop_configs())
    # General optimizations
    assert spark_configs.get("spark.hadoop.fs.s3a.aws.credentials.provider") == ",".join(
        [
            "com.amazonaws.auth.ContainerCredentialsProvider",
            "com.amazonaws.auth.profile.ProfileCredentialsProvider",
            "com.amazonaws.auth.DefaultAWSCredentialsProviderChain",
        ]
    )
    assert spark_configs.get("spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version") == "2"
    assert (
        spark_configs.get("spark.hadoop.mapreduce.fileoutputcommitter.cleanup-failures.ignored")
        == "true"
    )
    assert spark_configs.get("spark.hadoop.parquet.enable.summary-metadata") == "false"
    assert spark_configs.get("spark.sql.parquet.mergeSchema") == "false"
    assert spark_configs.get("spark.sql.parquet.filterPushdown") == "true"
    assert spark_configs.get("spark.sql.hive.metastorePartitionPruning") == "true"
    assert spark_configs.get("spark.hadoop.fs.s3a.threads.max") == "500"
    assert spark_configs.get("spark.hadoop.fs.s3a.connection.maximum") == "500"
    assert spark_configs.get("spark.hadoop.fs.s3a.experimental.input.fadvise") == "normal"
    assert spark_configs.get("spark.hadoop.fs.s3a.block.size") == "128M"
    assert spark_configs.get("spark.hadoop.fs.s3a.fast.upload.buffer") == "disk"
    assert spark_configs.get("spark.hadoop.fs.trash.interval") == "0"
    assert spark_configs.get("spark.port.maxRetries") == "50"
    assert jsc_hadoop_configs.get("mapreduce.fileoutputcommitter.marksuccessfuljobs") == "false"
    # Verify configurations when not running on a training job
    assert ",".join(feature_store_pyspark.classpath_jars()) in spark_configs.get("spark.jars")
    assert ",".join(
        [
            "org.apache.hadoop:hadoop-aws:3.3.1",
            "org.apache.hadoop:hadoop-common:3.3.1",
        ]
    ) in spark_configs.get("spark.jars.packages")