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Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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Google Certified Professional Data Engineer Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?

A. Create groups for your users and give those groups access to the dataset
B. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
C. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
D. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

Answer: D

NEW QUESTION # 107
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A. A stock symbol followed by a timestamp
B. A timestamp followed by a stock symbol
C. A sequential numeric ID
D. A non-sequential numeric ID

Answer: B,C

Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes.
[https://cloud.google.com/bigtable/docs/schema-design]
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

NEW QUESTION # 108
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)

A. The subscriber code cannot keep up with the messages.
B. Error handling in the subscriber code is not handling run-time errors properly.
C. Total outstanding messages exceed the 10-MB maximum.
D. The subscriber code does not acknowledge the messages that it pulls.
E. Publisher throughput quota is too small.

Answer: A,B

NEW QUESTION # 109
Which of these is NOT a way to customize the software on Dataproc cluster instances?

A. Log into the master node and make changes from there
B. Set initialization actions
C. Modify configuration files using cluster properties
D. Configure the cluster using Cloud Deployment Manager

Answer: D

NEW QUESTION # 110
You are creating the CI'CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository.
What should you do?

A. 1. Use Cloud Build to build a container and the Kubemetes Pod Operator to deploy the code of the DAG to the Google KubernetesEngine (GKE) cluster of the development instance for testing.2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.
B. 1 Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing.
2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.
C. 1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.
D. 1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

Answer: B

NEW QUESTION # 111
......

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Downloadable Professional-Data-Engineer PDF - Accurate Professional-Data-Engineer Test

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Operationalizing Machine Learning Models

Accurate Professional-Data-Engineer Test - Professional-Data-Engineer 100% Exam Coverage

Google Certified Professional Data Engineer Exam Sample Questions (Q106-Q111):

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