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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic - Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms
Topic 2: Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately
Topic 3: Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Topic 4: Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency - Designing data pipelines 1. Planning for data migration and integration 2. Designing for data transformation and enrichment 3. Defining architecture for batch and streaming processing
Topic 5: Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs

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Google Data Engineer Sample Questions (Q19-Q24):

NEW QUESTION # 19
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Load the ORC files into BigQuer
B. Mount the Hive tables from HDFS.
C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
D. Mount the Hive tables locally.
E. Mount the Hive tables locally.
F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
G. Replicate external Hive tables to the native ones.
H. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
I. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
J. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
K. Replicate external Hive tables to the native ones.
L. Then run the Hadoop utility to copy them do HDF

Answer: E,J

NEW QUESTION # 20
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A. Increase the size of the Google Persistent Disk on your server.
B. Increase your network bandwidth from your datacenter to GCP.
C. Increase the CPU size on your server.
D. Increase your network bandwidth from Compute Engine to Cloud Storage.

Answer: B

NEW QUESTION # 21
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
B. Partition the tables by a column containing a TIMESTAMP or DATE Type.
C. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
D. Re-create the tables using DD
E. Write an Apache Beam pipeline that creates a BigQuery table per data
F. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

Answer: F

NEW QUESTION # 22
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A. Do not use a production instance.
B. Before you test, run a heavy pre-test for several minutes.
C. Run your test for at least 10 minutes.
D. Use at least 300 GB of data.

Answer: A

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 23
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Bigtable
B. BigQuery
C. Cloud Datastore
D. Cloud Dataflow

Answer: B

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

NEW QUESTION # 24
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