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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 22%-27% | - Designing for data ingestion
|
| Building and operationalizing data processing systems | 28%-33% | - Operationalizing systems
|
| Managing and optimizing solutions | 20%-25% | - Reliability and scalability
|
| Ensuring solution quality | 20%-25% | - Data quality management
|
Google Data Engineer Sample Questions:
1. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
A) Deploy the model using a KubernetesEngine cluste
B) Call the Cloud Natural Language API from your applicatio
C) Build and train a text classification model using TensorFlo
D) Call the model from your application and process the results as labels.
E) Build and train a text classification model using TensorFlo
F) Process the generated Entity Analysis as labels.
G) Process the generated Sentiment Analysis as labels.
H) Deploy the model using Cloud Machine Learning Engin
I) Call the Cloud Natural Language API from your applicatio
J) Call the model from your application and process the results as labels.
2. You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?
A) Create an application that provides an AP
B) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C) Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D) Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
E) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
F) Create an application that writes to a Cloud SQL database to store the dat
3. When a Cloud Bigtable node fails, is lost.
A) the last transaction
B) the time dimension
C) all data
D) no data
4. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
A) If a preemptible worker is reclaimed, then a replacement worker must be added manually.
B) A Dataproc cluster cannot have only preemptible workers.
C) Preemptible workers cannot use persistent disk.
D) Preemptible workers cannot store data.
5. 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
Solutions:
| Question # 1 Answer: F | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: B,D | Question # 5 Answer: D |



