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| Section | Weight | Objectives |
|---|
| Operationalizing data and ML pipelines | 30% | - Monitoring and troubleshooting
- 1. Performance optimization and debugging
- 2. Logging and observability
- Pipeline automation and orchestration
- 1. Scheduling and monitoring pipelines
- 2. Cloud Composer workflows
|
| Maintaining and optimizing data and ML solutions | 20% | - Machine learning integration
- 1. BigQuery ML usage
- 2. Vertex AI integration for pipelines
- Security and governance
- 1. Data encryption and compliance
- 2. IAM and access control
|
| Designing data processing systems | 20% | - Data pipeline architecture design
- 1. Scalable data ingestion design
- 2. Batch vs streaming data processing selection
- Storage and data modeling
- 1. Data warehouse design using BigQuery
- 2. Data lake architecture on Google Cloud Storage
|
| Building and operationalizing data processing systems | 30% | - Data pipeline implementation
- 1. Dataflow pipeline development
- 2. Dataproc and Spark-based processing
- Data ingestion and transformation
- 1. ETL/ELT workflows
- 2. Pub/Sub streaming ingestion
|
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Google Data Engineer Sample Questions (Q38-Q43):
NEW QUESTION # 38
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. HTTPS
- B. SOCKS
- C. HTTP
- D. VPN
Answer: B
Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 39
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?
- A. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
- B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
- C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
- D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
Answer: B
NEW QUESTION # 40
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. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
- B. Deploy a Cloud Dataproc cluste
- C. Change references in scripts from hdfs:// to gs://
- D. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
- E. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- F. Deploy a Cloud Dataproc cluste
- G. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- H. Use a standard persistent disk and 50% preemptible worker
- I. Change references in scripts from hdfs:// to gs://
- J. Use an SSD persistent disk and 50% preemptible worker
- K. Install the Cloud Storage connector, and store the data in Cloud Storag
Answer: F
NEW QUESTION # 41
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. Use feature engineering to add features for eyes, noses, and mouths to the input data.
- B. Use K-means Clustering to detect faces in the pixels.
- C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
- D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
Answer: C
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 # 42
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 SQL
- B. BigQuery
- C. Cloud Datastore
- D. Cloud Bigtable
Answer: A
NEW QUESTION # 43
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