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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q65-Q70):

NEW QUESTION # 65
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
B. Use a standard persistent disk and 50% preemptible worker
C. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
D. Change references in scripts from hdfs:// to gs://
E. Change references in scripts from hdfs:// to gs://
F. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
G. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
H. Use an SSD persistent disk and 50% preemptible worker
I. Install the Cloud Storage connector, and store the data in Cloud Storag
J. Deploy a Cloud Dataproc cluste
K. Deploy a Cloud Dataproc cluste

Answer: K

NEW QUESTION # 66
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Sessions
B. Windows
C. Triggers
D. OutputCriteria

Answer: C

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

NEW QUESTION # 67
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 Cloud Machine Learning Engin
B. Process the generated Sentiment Analysis as labels.
C. Call the Cloud Natural Language API from your applicatio
D. Call the Cloud Natural Language API from your applicatio
E. Call the model from your application and process the results as labels.
F. Build and train a text classification model using TensorFlo
G. Call the model from your application and process the results as labels.
H. Process the generated Entity Analysis as labels.
I. Build and train a text classification model using TensorFlo
J. Deploy the model using a KubernetesEngine cluste

Answer: H

NEW QUESTION # 68
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
C. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

Answer: A

NEW QUESTION # 69
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Create separate views to cover each month, and query from these views
B. Convert the sharded tables into a single partitioned table
C. Convert all daily log tables into date-partitioned tables
D. Enable query caching so you can cache data from previous months

Answer: C

NEW QUESTION # 70
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