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Who should take the Google Professional Data Engineer exam
Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification
- Data analysts
- Data scientists
- Business analysts
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The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:
Design Data Processing Systems
- Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
- Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
- Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
- Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
Google Professional-Data-Engineer Exam Syllabus Topics:
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Google Certified Professional Data Engineer Exam Sample Questions (Q118-Q123):
NEW QUESTION # 118
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the dat
a. They should only see certain tables based on their team membership. How should you set user permissions?
- A. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
- B. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
- C. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
- D. Assign the users/groups data viewer access at the table level for each table
Answer: D
NEW QUESTION # 119
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?
- A. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
- B. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
- C. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
- D. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
Answer: C
Explanation:
Import and Export to Bigquery from Cloud Storage is FREE. Also, when u store the csv files, Cloud Storage is cheaper than Bigquery. For processing Dataproc is cheaper than Dataflow.
NEW QUESTION # 120
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?
- A. Field promotion
- B. Salting
- C. Randomization
- D. Hashing
Answer: A
Explanation:
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotting
NEW QUESTION # 121
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
- A. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
- B. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- C. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- D. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
Answer: B
Explanation:
https://cloud.google.com/compute/docs/disks/customer-managed-encryption
NEW QUESTION # 122
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?
- A. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
- B. Create a numeric column from a feature cross of latitude and longtitude.
- C. Provide latitude and longtitude as input vectors to your neural net.
- D. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
Answer: B
Explanation:
Explanation
Reference https://cloud.google.com/bigquery/docs/gis-data
NEW QUESTION # 123
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