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| Section | Weight | Objectives |
|---|
| Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Implement disaster recovery strategies
- 2. Design highly available systems
- 3. Scale data workloads efficiently
- Managing resources and costs
- 1. Control operational costs
- 2. Optimize storage and compute usage
- 3. Tune query and pipeline performance
|
| Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Optimize pipeline performance and cost
- 2. Design orchestration workflows
- 3. Build ETL and ELT pipelines
- Designing for data ingestion
- 1. Design scalable and reliable ingestion architecture
- 2. Select appropriate storage systems
- 3. Design batch and streaming ingestion solutions
|
| Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
- 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- 2. Implement streaming data systems
- 3. Implement batch processing systems
- Operationalizing systems
- 1. Monitor data pipelines and workloads
- 2. Implement fault tolerance and recovery
- 3. Manage logging and alerting
|
| Ensuring solution quality | 20%-25% | - Data quality management
- 1. Implement data governance
- 2. Validate and clean data
- 3. Maintain metadata and lineage
- Security and compliance
- 1. Protect sensitive data
- 2. Implement encryption and compliance controls
- 3. Manage IAM and access control
|
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Google Data Engineer Sample Questions (Q49-Q54):
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?
- A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
- B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
- C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
- D. Use K-means Clustering to detect faces in the pixels.
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
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .
- A. master node
- B. conditional node
- C. application node
- D. worker node
Answer: A
Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 51
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?
- A. Sink API
- B. Source API
- C. Data extraction
- D. ParDo
Answer: D
Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do
NEW QUESTION # 52
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
- A. Cloud Bigtable
- B. Cloud Datastore
- C. BigQuery
- D. Cloud SQL
Answer: D
NEW QUESTION # 53
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. Process the generated Entity Analysis as labels.
- B. Deploy the model using Cloud Machine Learning Engin
- C. Deploy the model using a KubernetesEngine cluste
- D. Call the model from your application and process the results as labels.
- E. Build and train a text classification model using TensorFlo
- F. Call the model from your application and process the results as labels.
- G. Call the Cloud Natural Language API from your applicatio
- H. Build and train a text classification model using TensorFlo
- I. Process the generated Sentiment Analysis as labels.
- J. Call the Cloud Natural Language API from your applicatio
Answer: A
NEW QUESTION # 54
......
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