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Google Associate Cloud Engineer Exam Sample Questions (Q201-Q206):

NEW QUESTION # 201
Your company implemented BigQuery as an enterprise data warehouse. Users from multiple business units run queries on this data warehouse. However, you notice that query costs for BigQuery are very high, and you need to control costs. Which two methods should you use? (Choose two.)

A. Create separate copies of your BigQuery data warehouse for each business unit.
B. Split the users from business units to multiple projects.
C. Apply a user- or project-level custom query quota for BigQuery data warehouse.
D. Split your BigQuery data warehouse into multiple data warehouses for each business unit.
E. Change your BigQuery query model from on-demand to flat rate. Apply the appropriate number of slots to each Project.

Answer: C,E

Explanation:
https://cloud.google.com/bigquery/docs/custom-quotashttps://cloud.google.com/bigquery/pricing#flat_rate_prici

NEW QUESTION # 202
You are deploying an application to App Engine. You want the number of instances to scale based on request rate. You need at least 3 unoccupied instances at all times. Which scaling type should you use?

A. Manual Scaling with 3 instances.
B. Automatic Scaling with min_idle_instancesset to 3.
C. Basic Scaling with min_instancesset to 3.
D. Basic Scaling with max_instancesset to 3.

Answer: B

Explanation:
Explanation/Reference: https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed

NEW QUESTION # 203
You are operating a Google Kubernetes Engine (GKE) cluster for your company where different teams can run non-production workloads. Your Machine Learning (ML) team needs access to Nvidia Tesla P100 GPUs to train their models. You want to minimize effort and cost. What should you do?

A. Ask your ML team to add the "accelerator: gpu" annotation to their pod specification.
B. Create your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.
C. Recreate all the nodes of the GKE cluster to enable GPUs on all of them.
D. Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.

Answer: D

Explanation:
This is the most optimal solution. Rather than recreating all nodes, you create a new node pool with GPU enabled. You then modify the pod specification to target particular GPU types by adding node selector to your workloads Pod specification. YOu still have a single cluster so you pay Kubernetes cluster management fee for just one cluster thus minimizing the cost. Ref: https://cloud.google.com/kubernetes-engine/docs/how-to/gpus Ref: https://cloud.google.com/kubernetes-engine/pricing Example:
apiVersion: v1
kind: Pod
metadata:
name: my-gpu-pod
spec:
containers:
name: my-gpu-container
image: nvidia/cuda:10.0-runtime-ubuntu18.04
command: [/bin/bash]
resources:
limits:
nvidia.com/gpu: 2
nodeSelector:
cloud.google.com/gke-accelerator: nvidia-tesla-k80 # or nvidia-tesla-p100 or nvidia-tesla-p4 or nvidia-tesla-v100 or nvidia-tesla-t4

NEW QUESTION # 204
Your organization has a dedicated person who creates and manages all service accounts for Google Cloud projects. You need to assign this person the minimum role for projects. What should you do?

A. Add the user to roles/iam.serviceAccountAdmin role.
B. Add the user to roles/iam.securityAdmin role.
C. Add the user to roles/iam.serviceAccountUser role.
D. Add the user to roles/iam.roleAdmin role.

Answer: A

Explanation:
Reference:
Service Account User (roles/iam.serviceAccountUser): Includes permissions to list service accounts, get details about a service account, and impersonate a service account. Service Account Admin (roles/iam.serviceAccountAdmin): Includes permissions to list service accounts and get details about a service account. Also includes permissions to create, update, and delete service accounts, and to view or change the IAM policy on a service account.

NEW QUESTION # 205
You want to run a single caching HTTP reverse proxy on GCP for a latency-sensitive website. This specific reverse proxy consumes almost no CPU. You want to have a 30-GB in-memory cache, and need an additional
2 GB of memory for the rest of the processes. You want to minimize cost. How should you run this reverse proxy?

A. Create a Cloud Memorystore for Redis instance with 32-GB capacity.
B. Run it on Compute Engine, and choose a custom instance type with 6 vCPUs and 32 GB of memory.
C. Run it on Compute Engine, choose the instance type n1-standard-1, and add an SSD persistent disk of
32 GB.
D. Package it in a container image, and run it on Kubernetes Engine, using n1-standard-32 instances as nodes.

Answer: B

NEW QUESTION # 206
......

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