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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Data Engineer Exam
Exam Number:	Professional-Data-Engineer
Available Languages:	English, Japanese
Passing Score:	700 / 1000
Exam Price:	USD 200 (plus tax where applicable)
Certificate Validity Period:	2 years
Related Certifications:	Google Cloud Associate Cloud Engineer Google Cloud Professional Data Analyst Google Cloud Professional Cloud Architect
Exam Format:	Multiple select, Multiple choice
Real Exam Qty:	40 - 50
Exam Duration:	120 minutes
Recommended Training:	Official Exam Guide Google Cloud Professional Data Engineer Learning Path
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online-proctored or onsite-proctored
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

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Google Professional-Data-Engineer Certification Exam is designed to assess the skills and knowledge of candidates in various areas related to data engineering. Professional-Data-Engineer exam covers topics such as data processing architecture, data modeling, data ingestion, data transformation, and data storage. Candidates are also expected to have a strong understanding of Google Cloud technologies, including BigQuery, Cloud Storage, and Dataflow.

Google Certified Professional Data Engineer Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to copy all the data to a new clustered table. What should you do?

A. Re-create the table using data partitioning on the package delivery date.
B. Tier older data onto Cloud Storage files and create a BigQuery table using Cloud Storage as an external data source.
C. Implement clustering in BigQuery on the ingest date column.
D. Implement clustering in BigQuery on the package-tracking ID column.

Answer: D

NEW QUESTION # 23
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

A. [0, 1]
B. [1, 0, 0, 0, 0, 0, 0]
C. [0, 5, 0, 0, 0, 0]
D. [0, 0, 0, 1, 0, 0, 1]

Answer: A,B

Explanation:
Explanation
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent
'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

NEW QUESTION # 24
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

A. A concatenation of the product name and the current epoch time
B. The original order identification number from the sales system, which is a monotonically increasing integer
C. The current epoch time
D. A random universally unique identifier number (version 4 UUID)

Answer: D

NEW QUESTION # 25
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

A. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.
B. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
C. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataproc job.
D. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataprep job.

Answer: D

NEW QUESTION # 26
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
B. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
C. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
D. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

Answer: A

NEW QUESTION # 27
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