Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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- Total Questions: 179
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Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing DataFrame API Applications | 44% | - Handling missing data and duplicates - Filtering, sorting, aggregation - Column and row manipulation - User-defined functions (UDFs) - Reading/writing data and partitioning |
| Troubleshooting and Tuning | 8% | - Query plans and execution analysis - Performance optimization |
| Apache Spark Architecture and Components | 28% | - Driver and executor architecture - Fault tolerance and shuffle operations - Lazy evaluation, transformations vs actions - Spark execution model and hierarchy |
| Using Spark SQL | 12% | - Supported file formats and data sources - Querying data with Spark SQL - Working with views and tables |
| Structured Streaming | 8% | - Output modes and triggers - Streaming concepts and operations |
Associate-Developer-Apache-Spark Exam Facts, Early Starts and Safety Nets
- Troubleshooting and Tuning (8%)
- Structured Streaming (8%)
- Apache Spark Architecture and Components (28%)
Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:
Which of the following code blocks reads the parquet file stored at filePath into DataFrame itemsDf, using a valid schema for the sample of itemsDf shown below?
Sample of itemsDf:
1.+------+-----------------------------+-------------------+
2.|itemId|attributes |supplier |
3.+------+-----------------------------+-------------------+
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
7.+------+-----------------------------+-------------------+
- A. 1.itemsDfSchema = StructType([
2. StructField("itemId", IntegerType()),
3. StructField("attributes", ArrayType([StringType()])),
4. StructField("supplier", StringType())])
5.
6.itemsDf = spark.read(schema=itemsDfSchema).parquet(filePath) - B. 1.itemsDfSchema = StructType([
2. StructField("itemId", IntegerType),
3. StructField("attributes", ArrayType(StringType)),
4. StructField("supplier", StringType)])
5.
6.itemsDf = spark.read.schema(itemsDfSchema).parquet(filePath) - C. 1.itemsDfSchema = StructType([
2. StructField("itemId", IntegerType()),
3. StructField("attributes", StringType()),
4. StructField("supplier", StringType())])
5.
6.itemsDf = spark.read.schema(itemsDfSchema).parquet(filePath) - D. 1.itemsDfSchema = StructType([
2. StructField("itemId", IntegerType()),
3. StructField("attributes", ArrayType(StringType())),
4. StructField("supplier", StringType())])
5.
6.itemsDf = spark.read.schema(itemsDfSchema).parquet(filePath) - E. 1.itemsDf = spark.read.schema('itemId integer, attributes <string>, supplier string').parquet(filePath)
Correct Answer: D 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
Which of the following code blocks returns approximately 1000 rows, some of them potentially being duplicates, from the 2000-row DataFrame transactionsDf that only has unique rows?
- A. transactionsDf.take(1000).distinct()
- B. transactionsDf.sample(True, 0.5)
- C. transactionsDf.take(1000)
- D. transactionsDf.sample(False, 0.5)
- E. transactionsDf.sample(True, 0.5, force=True)
Correct Answer: B 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
Which of the following code blocks returns a single-column DataFrame of all entries in Python list throughputRates which contains only float-type values ?
- A. spark.createDataFrame(throughputRates, FloatType())
- B. spark.createDataFrame((throughputRates), FloatType)
- C. spark.createDataFrame(throughputRates, FloatType)
- D. spark.createDataFrame(throughputRates)
- E. spark.DataFrame(throughputRates, FloatType)
Correct Answer: A 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
Which of the following statements about Spark's DataFrames is incorrect?
- A. Spark's DataFrames are immutable.
- B. The data in DataFrames may be split into multiple chunks.
- C. Data in DataFrames is organized into named columns.
- D. RDDs are at the core of DataFrames.
- E. Spark's DataFrames are equal to Python's DataFrames.
Correct Answer: E 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
The code block displayed below contains an error. The code block should write DataFrame transactionsDf as a parquet file to location filePath after partitioning it on column storeId. Find the error.
Code block:
transactionsDf.write.partitionOn("storeId").parquet(filePath)
- A. The partitioning column as well as the file path should be passed to the write() method of DataFrame transactionsDf directly and not as appended commands as in the code block.
- B. No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead.
- C. The partitionOn method should be called before the write method.
- D. The operator should use the mode() option to configure the DataFrameWriter so that it replaces any existing files at location filePath.
- E. Column storeId should be wrapped in a col() operator.
Correct Answer: B 🗳️
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