Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Practice Exam Questions and Answers

A.  

transactionsDf.resample(0.15, False, 3142)

B.  

transactionsDf.sample(0.15, False, 3142)

C.  

transactionsDf.sample(0.15)

D.  

transactionsDf.sample(0.85, 8429)

E.  

transactionsDf.sample(True, 0.15, 8261)

A.  

The attributes array is specified incorrectly, Spark cannot identify the file format, and the syntax of the call to Spark's DataFrameReader is incorrect.

B.  

Columns in the schema definition use the wrong object type and the syntax of the call to Spark's DataFrameReader is incorrect.

C.  

The data type of the schema is incompatible with the schema() operator and the modification date threshold is specified incorrectly.

D.  

Columns in the schema definition use the wrong object type, the modification date threshold is specified incorrectly, and Spark cannot identify the file format.

E.  

Columns in the schema are unable to handle empty values and the modification date threshold is specified incorrectly.

A.  

spark.read.json(filePath)

B.  

spark.read.path(filePath, source="json")

C.  

spark.read().path(filePath)

D.  

spark.read().json(filePath)

E.  

spark.read.path(filePath)

A.  

transactionsDf.drop(["predError", "value"])

B.  

transactionsDf.drop("predError", "value")

C.  

transactionsDf.drop(col("predError"), col("value"))

D.  

transactionsDf.drop(predError, value)

E.  

transactionsDf.drop("predError & value")

A.  

transactionsDf.sort("storeId", asc("productId"))

B.  

transactionsDf.sort(col(storeId)).desc(col(productId))

C.  

transactionsDf.order_by(col(storeId), desc(col(productId)))

D.  

transactionsDf.sort("storeId", desc("productId"))

E.  

transactionsDf.sort("storeId").sort(desc("productId"))

A.  

1. filter

2. "storeId"==25

3. collect

4. 5

B.  

1. filter

2. col("storeId")==25

3. toLocalIterator

4. 5

C.  

1. select

2. storeId==25

3. head

4. 5

D.  

1. filter

2. col("storeId")==25

3. take

4. 5

E.  

1. filter

2. col("storeId")==25

3. collect

4. 5

A.  

The DataFrameReader is not accessed correctly.

B.  

The transaction is evaluated lazily, so no file will be read.

C.  

Spark is unable to understand the file type.

D.  

The code block is unable to capture all columns.

E.  

The resulting DataFrame will not have the appropriate schema.

A.  

1. withColumn

2. "itemNameBetweenSeparators"

3. split

4. "itemName"

5. 4

(Correct)

B.  

1. withColumnRenamed

2. "itemNameBetweenSeparators"

3. split

4. "itemName"

5. 4

C.  

1. withColumnRenamed

2. "itemName"

3. split

4. "itemNameBetweenSeparators"

5. 4

D.  

1. withColumn

2. "itemNameBetweenSeparators"

3. split

4. "itemName"

5. 5

E.  

1. withColumn

2. itemNameBetweenSeparators

3. str_split

4. "itemName"

5. 5

A.  

Spark uses a subset of the reserved system memory.

B.  

Storage memory is used for caching partitions derived from DataFrames.

C.  

As a general rule for garbage collection, Spark performs better on many small objects than few big objects.

D.  

Disabling serialization potentially greatly reduces the memory footprint of a Spark application.

E.  

Spark's memory usage can be divided into three categories: Execution, transaction, and storage.

A.  

The maximum number of tasks that an executor can process at the same time is controlled by the spark.task.cpus property.

B.  

The maximum number of tasks that an executor can process at the same time is controlled by the spark.executor.cores property.

C.  

The default value for spark.sql.autoBroadcastJoinThreshold is 10M

B.  

D.  

The default number of partitions to use when shuffling data for joins or aggregations is 300.

E.  

The default number of partitions returned from certain transformations can be controlled by the spark.default.parallelism property.