What are two of the benefits of using denormalized data structures in BigQuery?
A. Reduces the amount of data processed, reduces the amount of storage required
B. Increases query speed, makes queries simpler
C. Reduces the amount of storage required, increases query speed
D. Reduces the amount of data processed, increases query speed
Correct Answer: B
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data
has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?
A. Weights
B. Biases
C. Continuous features
D. Input values
Correct Answer: AB
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets. Reference: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground
Question 54:
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?
A. Include multiple time series values within the row key
B. Keep the row keep as an 8 bit integer
C. Keep your row key reasonably short
D. Keep your row key as long as the field permits
Correct Answer: C
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server. Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys
Question 55:
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
A. Preemptible workers cannot use persistent disk.
B. Preemptible workers cannot store data.
C. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
D. A Dataproc cluster cannot have only preemptible workers.
Correct Answer: BD
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only--Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes. No preemptible-only clusters--To ensure clusters
do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters. Persistent disk size--As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for
local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Which is not a valid reason for poor Cloud Bigtable performance?
A. The workload isn't appropriate for Cloud Bigtable.
B. The table's schema is not designed correctly.
C. The Cloud Bigtable cluster has too many nodes.
D. There are issues with the network connection.
Correct Answer: C
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded. Reference: https://cloud.google.com/bigtable/docs/performance
Question 57:
Which action can a Cloud Dataproc Viewer perform?
A. Submit a job.
B. Create a cluster.
C. Delete a cluster.
D. List the jobs.
Correct Answer: D
A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?
A. Unsupervised learning
B. Regressor
C. Classifier
D. Clustering estimator
Correct Answer: B
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores. Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades. Clustering is an unsupervised learning task for finding natural groupings of observations
(i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis. Reference: https://elitedatascience.com/machine-learning-algorithms
Question 59:
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.
A. primary key
B. unique key
C. row key
D. master key
Correct Answer: C
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Reference: https://cloud.google.com/bigtable/docs/overview
Question 60:
Does Dataflow process batch data pipelines or streaming data pipelines?
A. Only Batch Data Pipelines
B. Both Batch and Streaming Data Pipelines
C. Only Streaming Data Pipelines
D. None of the above
Correct Answer: B
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/
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