GCP-DE Valid Test Papers, Examinations GCP-DE Actual Questions

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Optimize storage and compute usage
  • 2. Tune query and pipeline performance
  • 3. Control operational costs
- Reliability and scalability
  • 1. Scale data workloads efficiently
  • 2. Design highly available systems
  • 3. Implement disaster recovery strategies
Ensuring solution quality20%-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. Maintain metadata and lineage
  • 3. Implement data governance
Building and operationalizing data processing systems28%-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. Implement fault tolerance and recovery
  • 3. Monitor data pipelines and workloads
Designing data processing systems22%-27%- Designing for data ingestion
  • 1. Design scalable and reliable ingestion architecture
  • 2. Select appropriate storage systems
  • 3. Design batch and streaming ingestion solutions
- Designing data pipelines
  • 1. Design orchestration workflows
  • 2. Optimize pipeline performance and cost
  • 3. Build ETL and ELT pipelines

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GCP-DE Valid Test Papers | Accurate Data Engineer 100% Free Examinations Actual Questions

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Google Data Engineer Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which of these is not a supported method of putting data into a partitioned table?

Answer: D

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 # 48
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?

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 # 49
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

Answer: B

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview


NEW QUESTION # 50
Which of these sources can you not load data into BigQuery from?

Answer: A

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data


NEW QUESTION # 51
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?

Answer: D


NEW QUESTION # 52
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