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Download Databricks Certified Professional Data Engineer Exam Exam Dumps
NEW QUESTION 48
A data engineering team needs to query a Delta table to extract rows that all meet the same condi-tion.
However, the team has noticed that the query is running slowly. The team has already tuned the size of the
data files. Upon investigating, the team has concluded that the rows meeting the condition are sparsely located
throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query?
- A. Write as a Parquet file
- B. Data skipping
- C. Tuning the file size
- D. Bin-packing
- E. Z-Ordering
Answer: E
NEW QUESTION 49
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?
- A. Linear regression
- B. TF-IDF
- C. Decision trees
- D. Logistic regression
Answer: A
NEW QUESTION 50
A data analyst has provided a data engineering team with the following Spark SQL query:
1.SELECT district,
2.avg(sales)
3.FROM store_sales_20220101
4.GROUP BY district;
The data analyst would like the data engineering team to run this query every day. The date at the end of the
table name (20220101) should automatically be replaced with the current date each time the query is run.
Which of the following approaches could be used by the data engineering team to efficiently auto-mate this
process?
- A. They could manually replace the date within the table name with the current day's date
- B. They could replace the string-formatted date in the table with a timestamp-formatted date
- C. They could request that the data analyst rewrites the query to be run less frequently
- D. They could pass the table into PySpark and develop a robustly tested module on the existing query
- E. They could wrap the query using PySpark and use Python's string variable system to automatically
update the table name
Answer: E
NEW QUESTION 51
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table("sales")
2. .withColumn("avg_price", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("complete")
6. ._____
7. .table("new_sales")
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?
- A. .trigger(continuous="once")
- B. .trigger(once=True)
- C. .trigger(processingTime="once")
- D. .processingTime(1)
- E. .processingTime("once")
Answer: B
NEW QUESTION 52
Which of the following describes a benefit of a data lakehouse that is unavailable in a traditional data
warehouse?
- A. A data lakehouse captures snapshots of data for version control purposes
- B. A data lakehouse utilizes proprietary storage formats for data
- C. A data lakehouse couples storage and compute for complete control
- D. A data lakehouse enables both batch and streaming analytics
- E. A data lakehouse provides a relational system of data management
Answer: D
NEW QUESTION 53
......
