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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement batch processing systems 3. Implement streaming data systems - Operationalizing systems 1. Manage logging and alerting 2. Monitor data pipelines and workloads 3. Implement fault tolerance and recovery
Topic 2: Designing data processing systems	22%-27%	- Designing data pipelines 1. Design orchestration workflows 2. Build ETL and ELT pipelines 3. Optimize pipeline performance and cost - Designing for data ingestion 1. Select appropriate storage systems 2. Design batch and streaming ingestion solutions 3. Design scalable and reliable ingestion architecture
Topic 3: Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Tune query and pipeline performance 2. Control operational costs 3. Optimize storage and compute usage - Reliability and scalability 1. Scale data workloads efficiently 2. Implement disaster recovery strategies 3. Design highly available systems
Topic 4: Ensuring solution quality	20%-25%	- Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls - Data quality management 1. Validate and clean data 2. Implement data governance 3. Maintain metadata and lineage

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Google Data Engineer Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which of these is not a supported method of putting data into a partitioned table?

A. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
B. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
C. Create a partitioned table and stream new records to it every day.
D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Answer: B

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 63
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
C. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

Answer: D

NEW QUESTION # 64
Which of the following statements is NOT true regarding Bigtable access roles?

A. You can configure access control only at the project level.
B. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
C. To give a user access to only one table in a project, you must configure access through your application.
D. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

Answer: D

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 65
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Include multiple time series values within the row key
B. Keep your row key as long as the field permits
C. Keep your row key reasonably short
D. Keep the row keep as an 8 bit integer

Answer: C

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 66
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Consume the stream of data in Cloud Dataflow using Kafka I
B. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
D. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
E. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
F. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
G. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
H. If that number falls below 4000, send an alert.
I. Set a sliding time window of 1 hour every 5 minute
J. If that number falls below 4000, send an alert.
K. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
L. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
M. Consume the stream of data in Cloud Dataflow using Kafka I

Answer: F

NEW QUESTION # 67
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