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The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.
Build & Operationalize Data Processing Systems
- Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
- Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
- Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
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Google Certified Professional Data Engineer Exam Sample Questions (Q268-Q273):
NEW QUESTION # 268
What are two methods that can be used to denormalize tables in BigQuery?
- A. 1) Use a partitioned table; 2) Join tables into one table
- B. 1) Split table into multiple tables; 2) Use a partitioned table
- C. 1) Use nested repeated fields; 2) Use a partitioned table
- D. 1) Join tables into one table; 2) Use nested repeated fields
Answer: D
Explanation:
Explanation
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
NEW QUESTION # 269
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?
- A. BETWEEN
- B. WHERE
- C. LIMIT
- D. SELECT
Answer: D
Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by
BigQuery.
NEW QUESTION # 270
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
- A. [0, 1]
- B. [0, 5, 0, 0, 0, 0]
- C. [0, 0, 0, 1, 0, 0, 1]
- D. [1, 0, 0, 0, 0, 0, 0]
Answer: A,D
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 # 271
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? Choose 2 answers.
- A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- B. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- C. Introduce resource hierarchy to leverage access control policy inheritance.
- D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- E. Use Cloud Deployment Manager to automate access provision.
Answer: B,E
NEW QUESTION # 272
The Dataflow SDKs have been recently transitioned into which Apache service?
- A. Apache Beam
- B. Apache Kafka
- C. Apache Hadoop
- D. Apache Spark
Answer: A
Explanation:
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive
NEW QUESTION # 273
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