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| Section | Weight | Objectives |
|---|
| Topic 1: Building and operationalizing data processing systems | 28%-33% | - Operationalizing systems
- 1. Implement fault tolerance and recovery
- 2. Monitor data pipelines and workloads
- 3. Manage logging and alerting
- Building data processing systems
- 1. Implement batch processing systems
- 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- 3. Implement streaming data systems
|
| Topic 2: Ensuring solution quality | 20%-25% | - Data quality management
- 1. Validate and clean data
- 2. Maintain metadata and lineage
- 3. Implement data governance
- Security and compliance
- 1. Manage IAM and access control
- 2. Implement encryption and compliance controls
- 3. Protect sensitive data
|
| Topic 3: Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Scale data workloads efficiently
- 2. Implement disaster recovery strategies
- 3. Design highly available systems
- Managing resources and costs
- 1. Tune query and pipeline performance
- 2. Optimize storage and compute usage
- 3. Control operational costs
|
| Topic 4: Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Optimize pipeline performance and cost
- 2. Build ETL and ELT pipelines
- 3. Design orchestration workflows
- Designing for data ingestion
- 1. Select appropriate storage systems
- 2. Design scalable and reliable ingestion architecture
- 3. Design batch and streaming ingestion solutions
|
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Free PDF Quiz Google - Updated GCP-DE - Data Engineer Testking Exam Questions
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Google Data Engineer Sample Questions (Q59-Q64):
NEW QUESTION # 59
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
- A. Triggers
- B. Windows
- C. Sessions
- D. OutputCriteria
Answer: A
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 # 60
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
- A. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- B. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- C. Introduce resource hierarchy to leverage access control policy inheritance.
- D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
- E. Use Cloud Deployment Manager to automate access provision.
- F. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
Answer: B,C
NEW QUESTION # 61
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. Deploy a Cloud Dataproc cluste
- B. Deploy a Cloud Dataproc cluste
- C. Change references in scripts from hdfs:// to gs://
- D. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- E. Use a standard persistent disk and 50% preemptible worker
- F. Install the Cloud Storage connector, and store the data in Cloud Storag
- G. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
- H. Use an SSD persistent disk and 50% preemptible worker
- I. Change references in scripts from hdfs:// to gs://
- J. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
- K. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
Answer: A
NEW QUESTION # 62
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
- B. Use the TABLE_DATE_RANGE function
- C. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- D. Use the WHERE_PARTITIONTIME pseudo column
Answer: B
Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap
NEW QUESTION # 63
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
- A. Use Cloud Vision AutoML with the existing dataset.
- B. Use Cloud Vision AutoML, but reduce your dataset twice.
- C. Train your own image recognition model leveraging transfer learning techniques.
- D. Use Cloud Vision API by providing custom labels as recognition hints.
Answer: A
NEW QUESTION # 64
......
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