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Download Databricks Certified Professional Data Engineer Exam Exam Dumps
NEW QUESTION 53
A data engineer has written the following query:
1. SELECT *
2. FROM json.`/path/to/json/file.json`;
The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT)
pipeline. The query should create the first table in the DLT pipeline.
Which of the following describes the change the colleague needs to make to the query?
- A. They need to add a live. prefix prior to json. in the FROM line
- B. They need to add a COMMENT line at the beginning of the query
- C. They need to add the cloud_files(…) wrapper to the JSON file path
- D. They need to add a CREATE LIVE TABLE table_name AS line at the beginning of the query
- E. They need to add a CREATE DELTA LIVE TABLE table_name AS line at the beginning of the query
Answer: D
NEW QUESTION 54
A data engineer has developed a code block to perform a streaming read on a data source. The code block is
below:
1. (spark
2. .read
3. .schema(schema)
4. .format(“cloudFiles”)
5. .option(“cloudFiles.format”, “json”)
6. .load(dataSource)
7. )
The code block is returning an error.
Which of the following changes should be made to the code block to configure the block to successfully
perform a streaming read?
- A. A new .stream line should be added after the .read line
- B. A new .stream line should be added after the .load(dataSource) line
- C. A new .stream line should be added after the spark line
- D. The .read line should be replaced with .readStream
- E. The .format(“cloudFiles”) line should be replaced with .format(“stream”)
Answer: D
NEW QUESTION 55
Projecting a multi-dimensional dataset onto which vector has the greatest variance?
- A. first principal component
- B. not enough information given to answer
- C. second eigenvector
- D. second principal component
- E. first eigenvector
Answer: A
Explanation:
Explanation
The method based on principal component analysis (PCA) evaluates the features according to the projection of
the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher’s linear
discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant
vector.
The first principal component corresponds to the greatest variance in the data, by definition. If we project the
data onto the first principal component line, the data is more spread out (higher variance) than if projected onto
any other line, including other principal components.
NEW QUESTION 56
……
