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Download Databricks Certified Professional Data Engineer Exam Exam Dumps
NEW QUESTION 52
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. Logistic regression
- B. TF-IDF
- C. Linear regression
- D. Decision trees
Answer: C
NEW QUESTION 53
Which of the following data workloads will utilize a Silver table as its source?
- A. A job that cleans data by removing malformatted records
- B. A job that enriches data by parsing its timestamps into a human-readable format
- C. A job that aggregates cleaned data to create standard summary statistics
- D. A job that ingests raw data from a streaming source into the Lakehouse
- E. A job that queries aggregated data that already feeds into a dashboard
Answer: C
NEW QUESTION 54
Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?
- A. The ability to write pipelines in Python and/or SQL
- B. The ability to access previous versions of data tables
- C. The ability to perform batch and streaming queries
- D. The ability to automatically scale compute resources
- E. The ability to declare and maintain data table dependencies
Answer: E
NEW QUESTION 55
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?
- A. 1. (spark.table("sales")
2. .withColumn("avgPrice", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("append")
6. .table("cleanedSales")
7.) - B. 1. (spark.table("sales")
2. .groupBy("store")
3. .agg(sum("sales"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8.) - C. 1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. ) - D. 1. (spark.table("sales")
2. .agg(sum("sales"),
3. sum("units"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8. ) - E. 1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. )
Answer: A
NEW QUESTION 56
......
