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NEW QUESTION: 1
About the redistribution is correct
A. insert mode (online) does not support insert, delete, and update of local tables
B. Redistribution phase allows performance statistics queries to be performed
C. insert mode (online) supports users to perform DROP, TRUNCATE-PARTITION operations on local tables
D. Redistributed tables in read-only mode (offline) support updating, inserting, and deleting data
Answer: C

NEW QUESTION: 2
イベントグリッドを使用して他のサービスに接続するアプリを作成しています。アプリのイベントデータは、コンプライアンスをチェックするサーバーレス関数に送信されます。この機能はあなたの会社によって維持されています。
リソースのスコープで新しいイベントサブスクリプションを作成します。イベントは、3つの特定の期間の後に無効にする必要があります。セキュリティを確保するには、イベントグリッドを構成する必要があります。
何を実装する必要がありますか？回答するには、[回答エリアで適切なオプションを選択してください。
注：正しい選択はそれぞれ1ポイントの価値があります

Answer:
Explanation:

Explanation

Box 1: SAS tokens
Custom topics use either Shared Access Signature (SAS) or key authentication. Microsoft recommends SAS, but key authentication provides simple programming, and is compatible with many existing webhook publishers.
In this case we need the expiration time provided by SAS tokens.
Box 2: ValidationCode handshake
Event Grid supports two ways of validating the subscription: ValidationCode handshake (programmatic) and ValidationURL handshake (manual).
If you control the source code for your endpoint, this method is recommended.

NEW QUESTION: 3
You need to track and report the Store Close Date for all of the stores in your company in your block storage option database. Store close date will vary by store over time.
Which three solutions would allow you to do this?
A. Varying attribute with 2 Independent dimensions, Periods and Fiscal Year
B. Attribute dimension based on Store
C. Measure called Store Close Date with a data type of date
D. Varying attribute dimension with an Independent dimension of Time where Time contains both the Years and Months in the same dimension
E. User Defined attribute Store Close Date assigned to each store
Answer: A,B,E

NEW QUESTION: 4
Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation.
A. DataNode
B. Secondary NameNode
C. NameNode
D. TaskTracker
E. JobTracker
Answer: E
Explanation:
JobTracker is the daemon service for submitting and tracking MapReduce jobs in
Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on
its own JVM process. In a typical production cluster its run on a separate machine. Each slave
node is configured with job tracker node location. The JobTracker is single point of failure for the
Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop
performs following actions(from Hadoop Wiki:)
Client applications submit jobs to the Job tracker.
The JobTracker talks to the NameNode to determine the location of the data
The JobTracker locates TaskTracker nodes with available slots at or near the data
The JobTracker submits the work to the chosen TaskTracker nodes.
The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they
are deemed to have failed and the work is scheduled on a different TaskTracker.
A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do
then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid,
and it may may even blacklist the TaskTracker as unreliable. When the work is completed, the JobTracker updates its status.
Client applications can poll the JobTracker for information.
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?