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| Section | Weight | Objectives |
|---|
| Topic 1: Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Scale data workloads efficiently
- 2. Design highly available systems
- 3. Implement disaster recovery strategies
- Managing resources and costs
- 1. Tune query and pipeline performance
- 2. Control operational costs
- 3. Optimize storage and compute usage
|
| Topic 2: 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 streaming data systems
- 2. Implement batch processing systems
- 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
|
| Topic 3: 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. Protect sensitive data
- 2. Implement encryption and compliance controls
- 3. Manage IAM and access control
|
| Topic 4: Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Design orchestration workflows
- 2. Optimize pipeline performance and cost
- 3. Build ETL and ELT pipelines
- Designing for data ingestion
- 1. Design scalable and reliable ingestion architecture
- 2. Design batch and streaming ingestion solutions
- 3. Select appropriate storage systems
|
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Google Data Engineer Sample Questions (Q52-Q57):
NEW QUESTION # 52
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. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
- B. Replicate external Hive tables to the native ones.
- C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
- D. Then run the Hadoop utility to copy them do HDF
- E. Load the ORC files into BigQuer
- F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
- G. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
- H. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
- I. Mount the Hive tables from HDFS.
- J. Mount the Hive tables locally.
- K. Replicate external Hive tables to the native ones.
- L. Mount the Hive tables locally.
Answer: H,L
NEW QUESTION # 53
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?
- A. Keep your row key reasonably short
- B. Keep the row keep as an 8 bit integer
- C. Keep your row key as long as the field permits
- D. Include multiple time series values within the row key
Answer: A
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 # 54
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)
- A. Increase the number of max workers
- B. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
- C. Use a larger instance type for your Cloud Dataflow workers
- D. Change the zone of your Cloud Dataflow pipeline to run in us-central1
- E. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
- F. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
- G. Create a temporary table in Cloud Spanner that will act as a buffer for new data
Answer: C,E
NEW QUESTION # 55
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. Train your own image recognition model leveraging transfer learning techniques.
- B. Use Cloud Vision API by providing custom labels as recognition hints.
- C. Use Cloud Vision AutoML with the existing dataset.
- D. Use Cloud Vision AutoML, but reduce your dataset twice.
Answer: C
NEW QUESTION # 56
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.
- A. Use BigQuery UPDATE to further reduce the size of the dataset.
- B. Preserve the structure of the data as much as possible.
- C. Denormalize the data as must as possible.
- D. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
- E. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
- F. Use BigQuery'ssupport for external data sources to query.
Answer: D,E
NEW QUESTION # 57
......
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